COMPUTER SYSTEM LINKED BY USING INFORMATION IN DATA OBJECTS

Abstract: Various improvements to steganographic systems, and applications therefore, are disclosed. The improvements include facilitating scale and rotation registration for steganographic decoding by use of rotationally symmetric steganographically embedded patterns and subliminal digital graticules; improved techniques for decoding without access to unencoded originals; improving robustness of steganographic coding in motion pictures and/or in the presence of lossy compression/decompression; and representing data (1690) by patterned bit cells (802) whose energy in the spatial domain facilitates decoding registration (1692). Applications include enhanced-security financial transactions, counterfeit resistant identification cards, fraud deterrent systems for cellular telephony, covert modem channels in video transmissions, photo duplication kiosks with automatic copyright detection, and hotlinked image objects (e.g., with embedded URLs) for use on the Internet.

Description: COMPUTER SYSTEM LINKED BY USING INFORMATION IN DATA OBJECTS Background of the Invention Hiding data in imagery or audio is a technique well known to artisans in the field, and is termed "steganography." There are a number of diverse approaches to, and applications of, steganography. A brief survey follows: British patent publication 2,196,167 to Thorn EMI discloses a system in which an audio recording is electronically mixed with a marking signal indicative of the owner of the recording, where the combination is perceptually identical to the original. U.S. patents 4,963,998 and 5,079,648 disclose variants of this system. U.S. Patent 5,319,735 to Bolt, Berenak & Newman rests on the same principles as the earlier Thorn EMI publication, but additionally addresses psycho-acoustic masking issues. U.S. Patents 4,425,642, 4,425,661, 5,404,377 and 5,473,631 to Moses disclose various systems for imperceptibly embedding data into audio signals ~ the latter two patents particularly focusing on neural network implementations and perceptual coding details. U.S. Patent 4,943,973 to AT&T discloses a system employing spread spectrum techniques for adding a low level noise signal to other data to convey auxiliary data therewith. The patent is particularly illustrated in the context of transmitting network control signals along with digitized voice signals. U.S. Patent 5,161,210 to U.S. Philips discloses a system in which additional low- level quantization levels are defined on an audio signal to convey, e.g., a copy inhibit code, therewith. U.S. Patent 4,972,471 to Gross discloses a system intended to assist in the automated monitoring of audio (e.g. radio) signals for copyrighted materials by reference to identification signals subliminally embedded therein. U.S. Patent 5,243,423 to DeJean discloses a video steganography system which encodes digital data (e.g. program syndication verification, copyright marking, media research, closed captioning, or like data) onto randomly selected video lines. DeJean relies on television sync pulses to trigger a stored pseudo random sequence which is XORed with the digital data and combined with the video. European application EP 581,317 discloses a system for redundantly marking images with multi-bit identification codes. Each "1 " ("0") bit of the code is manifested as a slight increase (decrease) in pixel values around a plurality of spaced apart "signature points." Decoding proceeds by computing a difference between a suspect image and the original, unencoded image, and checking for pixel perturbations around the signature points. PCT application WO 95/14289 describes the present applicant's prior work in this field. Komatsu et al., describe an image marking technique in their paper "A Proposal on Digital Watermark in Document Image Communication and Its Application to Realizing a Signature," Electronics and Communications in Japan. Part 1 , Vol. 73, No. 5, 1990, pp. 22- 33. The work is somewhat difficult to follow but apparently results in a simple yes/no determination of whether the watermark is present in a suspect image (e.g. a 1 bit encoded message). Others embed the information "in-band" (i.e. in the visible video signal itself)- Examples include U.S. Patents 4,528,588, 4,595,950, and 5,319,453; European application 441 ,702; and Matsui et. al, "Video-Steganography: How to Secretly Embed a Signature in a Picture," IMA Intellectual Property Project Proceedings. January 1994, Vol. 1, Issue 1 , pp. 187-205. Copyright marking of video and multimedia is found in "Access Control and Copyright Protection for Images (ACCOPI), WorkPackage 8: Watermarking," June 30, 1995, 46 pages. TALISMAN, appears to extend certain of the ACCOPI work. A British company, Highwater FBI. Ltd., has introduced a software product which is said to imperceptibly embed identifying information into photographs and other graphical images. This technology is the subject of European patent applications 9400971.9 (filed January 19, 1994), 9504221.2 (filed March 2, 1995), and 9513790.7 (filed July 3, 1995), the first of which has been laid open as PCT publication WO 95/20291. Brief Description Of The Drawings Fig. 1 is a simple and classic depiction of a one dimensional digital signal which is discretized in both axes. Fig. 2 is a general overview, with detailed description of steps, of the process of embedding an "imperceptible" identification signal onto another signal. Fig. 3 is a step-wise description of how a suspected copy of an original is identified. Fig. 4 is a schematic view of an apparatus for pre-exposing film with identification information. Fig. 5 is a diagram of a "black box" embodiment. Fig. 6 is a schematic block diagram of the embodiment of Fig. 5. Fig. 7 shows a variant of the Fig. 6 embodiment adapted to encode successive sets of input data with different code words but with the same noise data. Fig. 8 shows a variant of the Fig. 6 embodiment adapted to encode each frame of a videotaped production with a unique code number. Figs. 9A-9C are representations of an industry standard noise second. Fig. 10 shows an integrated circuit used in detecting standard noise codes. Fig. 1 1 shows a process flow for detecting a standard noise code that can be used in the Fig. 10 embodiment. Fig. 12 is an embodiment employing a plurality of detectors. Fig. 13 shows an embodiment in which a pseudo-random noise frame is generated from an image. Fig. 14 illustrates how statistics of a signal can be used in aid of decoding. Fig. 15 shows how a signature signal can be preprocessed to increase its robustness in view of anticipated distortion, e.g. MPEG. Figs. 16 and 17 show embodiments in which information about a file is detailed both in a header, and in the file itself. Figs. 18-20 show details of embodiments using rotationally symmetric patterns. Fig. 21 shows encoding "bumps" rather than pixels. Figs. 22-26 detail aspects of a security card. Fig. 27 is a diagram illustrating a network linking method using information embedded in data objects that have inherent noise. Figs. 27A and 27B show a typical web page, and a step in its encapsulation into a self extracting web page object. Fig. 28 is a diagram of a photographic identification document or security card. Figs. 29 and 30 illustrate two embodiments by which subliminal digital graticules can be realized. Fig. 29A shows a variation on the Fig. 29 embodiment. Figs. 31 A and 31B show the phase of spatial frequencies along two inclined axes. Figs. 32A - 32C show the phase of spatial frequencies along first, second and third concentric rings. Figs 33A - 33E show steps in the registration process for a subliminal graticule using inclined axes. Figs. 34A - 34E show steps in the registration process for a subliminal graticule using concentric rings. Figs. 35A - 35C shows further steps in the registration process for a subliminal graticule using inclined axes. Figs. 36A - 36D show another registration process that does not require a 2D FFT. Fig. 37 is a flow chart summarizing a registration process for subliminal graticules. Fig. 38 is a block diagram showing principal components of an exemplary wireless telephony system. Fig. 39 is a block diagram of an exemplary steganographic encoder that can be used in the telephone of the Fig. 38 system. Fig. 40 is a block diagram of an exemplary steganographic decoder that can be used in the cell site of the Fig. 1 system. Figs. 41 A and 41B show exemplary bit cells used in one form of encoding. Fig. 42 shows a hierarchical arrangement of signature blocks, sub-blocks, and bit cells used in one embodiment. Fig. 43 shows a general overview of a computer system linked by using information embedded in the objects. Figs. 44-59 are representative computer screens generated in accordance with the present invention. Fig. 60 is a basic overview of how a traditional file header can be replaced by a pointer to a database location where header information resides. Fig. 61 expands on Fig. 60 by illustrating that a pointer to a remote location can coexist with existing data file formats which might include headers. Fig. 62 is a basic map showing how an instrument or application can associate a pointer attached to an object to some remote digital storage location. Fig. 63 is a condensed depiction of a process that an instrument or application might go through in resolving and acting upon remote information pointed to by an attached pointer. Fig. 64 graphically illustrates that attached information can actually be sequences of actions or multiple sets of information. Fig. 65 shows how the basic principles of master identity objects can be used to fuel a reporting network wherein a central location can "keep track" of where its copies migrate. Detailed Description In the following discussion of an illustrative embodiment, the words "signal" and "image" are used interchangeably to refer to both one, two, and even beyond two dimensions of digital signal. Examples will routinely switch back and forth between a one dimensional audio-type digital signal and a two dimensional image-type digital signal. In order to fully describe the details of an illustrative embodiment, it is necessary first to describe the basic properties of a digital signal. Fig. 1 shows a classic representation of a one dimensional digital signal. The x-axis defines the index numbers of sequence of digital "samples," and the y-axis is the instantaneous value of the signal at that sample, being constrained to exist only at a finite number of levels defined as the "binary depth" of a digital sample. The example depicted in Fig. 1 has the value of 2 to the fourth power, or "4 bits," giving 16 allowed states of the sample value. For audio information such as sound waves, it is commonly accepted that the digitization process discretizes a continuous phenomena both in the time domam and in the signal level domam. As such, the process of digitization itself introduces a fundamental error source, in that it cannot record detail smaller than the discretization interval m either domam. The industry has referred to this, among other ways, as "aliasmg" in the tune domam, and "quantization noise" in the signal level domam. Thus, there will always be a basic error floor of a digital signal. Pure quantization noise, measured in a root mean square sense, is theoreucally known to have the value of one over the square root of twelve, or about 0 29 DN, where DN stands for 'Digital Number' or the finest unit increment of the signal level. For example, a perfect 12-bιt digitizer will have 4096 allowed DN with an innate root mean square noise floor of -0.29 DN. All known physical measurement processes add additional noise to the transformation of a continuous signal mto uie digital form. The quantization noise typically adds in quadrature (square root of the mean squares) to the "analog noise" of the measurement process, as it is sometimes referred to With almost all commercial and technical processes, the use of the decibel scale is used as a measure of signal and noise in a given recording medium. The expression "signal-to-noise ratio" is generally used, as tt will be in this disclosure. As an example, this disclosure refers to signal to noise ratios in terms of signal power and noise power, thus 20 dB represents a 10 times increase in signal amplitude. In summary, this embodiment embeds an N-bit value onto an entire signal through the addition of a very low amplitude encodation signal which has the look of pure noise. N is usually at least 8 and is capped on the higher end by ultimate signal-to-noise considerations and "bit error" tn retrieving and decoding the N-bit value. As a practical matter, N is chosen based on apphcation specific considerations, such as the number of unique different "signatures" that are desired To illustrate, if N = 128, then the number of unique digital signatures is in excess of 10 ΛΛ 38 (2 ΛΛ 128) This number is believed to be more than adequate to both identify the material with sufficient statistical certainry and to index exact sale and distπbution information. The amplitude or power of this added signal is determined by the aesthetic and inf ormational considerations of each and every application using the present methodology For instance, non-professional video can stand to have a higher embedded signal Level without becoming noticeable to the average human eye, while high precision audio may only be able to accept a relatively small signal level lest the human ear perceive an objectionable increase in "hiss." These statements are generalities and each application has its own set of cπteπa in choosing the signal level of the embedded identification signal. The higher the level of embedded signal, the more corrupted a copy can be and still be identified. On the other hand, the higher the level of embedded signal, the more objectionable the perceived noise might be, potentially impacting the value of me distributed mateπal To illustrate the range of different apphcations to which applicant's technology can be applied, t he present specification details two different systems. The first (termed, for lack of a better name, a "batch encodmg" system), applies identification coding to an existmg data signal. The second (termed, for lack of a better name, a "real time encodmg" system), applies identification coding to a signal as it is produced Those skilled in the an will recognize that the principles of applicant's technology can be applied in a number of other contexts in addition to diese particularly descπbed. The discussions of these two systems can be read in either order. Some readers may find me latter more intuitive than die former; for others the contrary may be true. BATCH ENCODING The following discussion of a first class of embodiments is best prefaced by a section defining relevant terms. The oπ g mal signal refers to either the oπginai digital signal or the high quality digitized copy of a non-digital oπgmal. The N-bit identification word refers to a unique identification binary value, typically having N range anywhere from 8 to 128, which is the tdenuficauon code ultimately placed onto the oπgmal signal via the disclosed transformation process. In the illustrated embodiment, each N-bit identification word begins with the sequence of values '0101,' which is used to determine an optimization of the signal-to- noise ratio in the identification procedure of a suspect signal (see definition below). The m'th bit value of the N-bit identification word is either a zero or one corresponding to me value of the m'th place, readmg left to nght, of the N-bit word. E.g., the first (m= I) bit value of the N=8 identification word 01110100 is the value '0,' the second bit vaiue of this identification word is T, etc. The m'th individual embedded code signal refers to a signal which has dimensions and extent precisely equal to the oπginai signal (e.g. both are a 512 by 512 digital image), and which is (in the illustrated embodiment) an independent pseudo-random sequence of digital values. "Pseudo" pays homage to the difficulty m philosophically defining pure randomness, and also indicates that there are vanous acceptable ways of generating the "random" signal. There will be exactly N individual embedded code signals associated with any given oπginai signal. The acceptable perceived noise level refers to an application-specific determination of how much "extra noise," i.e. amplitude of the composite embedded code signal descnbed next, can be added to the oπgmal signal and still have an acceptable signal to sell or otherwise distπbute. This disclosure uses a 1 dB increase in noise as a typical value which might be acceptable, but this is quite arbitrary The composite embedded code signal refers to die signal which has dimensions and extent precisely equal to the oπginai signal, (e.g both are a 512 by 512 digital image), and which contams the addition and appropriate attenuation of the N individual embedded code signals. The individual embedded signals are generated on an arbitrary scale, whereas die amplitude of the composite signal must not exceed the pre-set acceptable perceived noise level, hence die need for "attenuation" of the N added individual code signals. The distributable si g nal refers to die nearly similar copy of die original signal, cσnsisτrag of the original signal plus die composite embedded code signal. This is the signal which is distributed to die outside community, having only slightly higher but acceptable noise propeπics" than ihe original. A suspect signal refers to a signal which has the general appearance of die original and distributed signal and whose potential identification match to the original is being questioned. The suspect signal is then analyzed to see if it matches the N-bit identification word. The detailed methodology of this first embodiment begins by stating that the N-bit identification word is encoded onto die original signal by having each of the m bit values multiply uieir corresponding individual embedded code signals, the resultant being accumulated in the composite signal, the fully summed composite signal then being attenuated down to die acceptable perceived noise amplitude, and the resultant composite signal added to the original to become the distributable signal. The original signal, the N-bit identification word, and all N individual embedded code signals are men stored away in a secured place. A suspect signal is then found. This signal may have undergone multiple copies, compressions and decompressions, resamplings onto different spaced digital signals, transfers from digital to analog back to digital media, or any combination of these items. IF the signal still appears similar to the original, i.e. its innate quality is not ±oroughly destroyed by all of these transformations and noise additions, men depending on the signal to noise properties of the embedded signal, the identification process should function to some objective degree of statistical confidence. The extent of corruption of the suspect signal and die original acceptable perceived noise level are two key parameters in determining an expected confidence level of identification. The identification process on me suspected signal begins by resampling and aligning the suspected signal onto die digital format and extent of die original signal. Thus, if an image has been reduced by a factor of two, it needs to be digitally enlarged by diat same factor. Likewise, if a piece of music has been "cut out," but may still have the same sampling rate as the original, it is necessary to register this cut-out piece to die original, typically done by performing a local digital cross-correlation of the two signals (a common digital operation), finding at what delay value the correlation peaks, then using mis found delay value to register the cut piece to a segment of the original. Once the suspect signal has been sample-spacing matched and registered to me original, the signal levels of ώe suspect signal should be matched in an rms sense to the signal level of the original. This can be done via a search on the parameters of offset, amplification, and gamma being optimized by using the πunimum of the mean squared error between me two signals as a function of the three parameters We can call the suspect signal normalized and registered at this point, or just normalized for convenience. The newly matched pair men has die oπgmal signal subtracted from die normalized suspect signal to produce a difference signal The difference signal is then cross-correlated with each of the .V individual embedded code signals and the peak cross-coπelation value recorded. The first four bit code ('0101') is used as a calibrator boώ on die mean values of the zero value and the one value, and on further registration of die two signals if a finer signal to noise ratio is desired (i.e., die optimal separaπon of the 0101 signal will indicate an optimal registration of die two signals and will also indicate me probable existence of me N-bit identification signal bemg present.) The resulting peak cross-correlation values will form a noisy seπes of floating pomt numbers which can be transformed mto 0's and l's by ώeir proximity to the mean values of 0 and 1 found by uie 0101 calibration sequence. If the suspect signal has mdeed been denved from the oπginai, the identification number resulting from the above process will match me N-bit identification word of me oπginai, bearing m mind eiώer predicted or unknown "bit error" statistics. Signal-to-noise considerations will detemune if diere will be some land of "bit error" in die idenπfication process, leadmg to a form of X% probability of identification where X might be desired to be 99.9% or whatever. If me suspect copy is mdeed not a copy of the oπginai, an essentially random sequence of 0's and l's will be produced, as well as an apparent lack of separation of the resultant values. This is to say, if the resultant values are plotted on a histogram, the existence of die N-bit identification signal wdl exhibit strong bi-level characteπstics, whereas the non-existence of uie code, or the existence of a different code of a different oπgmal, will exhibit a type of random gaussian-like distπbution. This histogram separation alone should be sufficient for an identification, but it is even stronger proof of identification when an exact binary sequence can be objectively reproduced. Specific Example Imagine mat we have taken a valuable picture of two heads of state at a cocktail party, pictures which are sure to earn some reasonable fee in the commercial market. We desire to sell this picture and ensure that it is not used in an unauthorized or uncompensated manner. This and the following steps are summarized in Fig. 2. Assume the picture is transformed mto a positive color pππt. We first scan dus mto a digitized form via a normal high quality biack and white scanner with a typical photometnc spectral response curve. (It is possible to get better ultimate signal to noise ratios by scannmg in each of the Lhree pπmary colors of die color image, but dus nuance is not central to descnbmg uie basic process.) Let us assume uiat uie scanned image now becomes a 4000 by 4000 pixel monochrome digital image with a grey scale accuracy defined by 12-bit grey values or 4096 allowed levels. We will call t his the "original digital image" realizing diat uiis is the same as our "original signal" in the above definitions. During me scanning process we have arbitrarily set absolute black to correspond to digital value '30'. We estimate diat there is a basic 2 Digital Number root mean square noise existing on die original digital image, plus a theoretical noise (known in me industry as "shot noise") of uie square root of the brightness value of any given pixel. In formula, we have: < RMS Noise n . m > = sqπ(4 + (V n m -30)) (1) Here, n and m are simple indexing values on rows and columns of the image ranging from 0 to 3999. Sqπ is uie square root. V is the DN of a given indexed pixei on die original digital image. The < > brackets around die RMS noise merely indicates mat diis is an expected average value, where it is clear diat each and every pixel will have a random eπor individually. Thus, for a pixel value having 1200 as a digital number or "brightness value", we find diat its expected rms noise value is sqπ(1204) = 34.70, which is quite close to 34.64, die square root of 1200. We furthermore realize uiat die square root of the innate brightness value of a pixel is not precisely what die eye perceives as a minimum objectionable noise, thus we come up with the formula: <RMS Addable Noise π m > = X*sqπ(4+(V„. m -30rY) ( 2) Where X and Y have been added as empirical parameters which we will adjust, and "addable" noise refers to our acceptable perceived noise level from die definitions above. We now intend to experiment wiui what exact value of X and Y we can choose, but we will do so at the same time that we are performing the next steps in uie process. The next step in our process is to choose N of our N-bit identification word.' We decide that a 16 bit main identification value widi its 65536 possible values will be sufficiendy large to identify uie image as ours, and uiat we will be directly selling no more man 128 copies of die image which we wish to track, giving 7 bits plus an eighm bit for an odαVeven adding of the first 7 bits (i.e. an error checking bit on die first seven). The total bits required now are at 4 bits for the 0101 calibration sequence, 16 for the main identification, 8 for die version, and we now throw in another 4 as a further error checking value on me first 28 bits, giving 32 bits as N. The final 4 bits can use one of many industry standard error checking methods to choose its four values. We now randomly determine die 16 bit main identification number, finding for example, UOI 0001 1001 1110; our first versions of me original sold will have all 0's as die version identifier, and uie error checking bits will fall out where uiey may We now have our unique 32 btt identification word which we will embed on die onginai digital image. To do dus, we generate 32 independent random 4000 by 4000 encodmg images for each bit of our 32 bit identification word. The manner of generating diese random images is revealing. There are numerous ways to generate these. By far the simplest is to turn up die gam on die same scanner that was used to scan in uie oπgmal photograph, only dus time placing a pure black image as uie input, uien scanning dus 32 tunes. The only drawback to dus technique is that it does require a large amount of memory and diat "fixed pattern" noise wdl be pan of each independent "noise image." But, uie fixed pattern noise can be removed via normal "dark frame" subtraction techniques. Assume mat we set me absolute black average value at digital number '100,' and that raώer uian fmdmg a 2 DN rms noise as we did in the normal gam setting, we now find an rms noise of 10 DN about each and every pixel's mean value. We next apply a mid-spatial- frequency bandpass filter (spatial convolution) to each and every mdependent random image, essentially removmg die very high and die very low spatial frequencies from mem. We remove uie very low frequencies because simple real -world error sources like geometπcal warping, splotches on scanners, mis-registrations, and die like will exhibit themselves most at lower frequencies also, and so we want to concentrate our identification signal at higher spatial frequencies in order to avoid these types of corruptions. Likewise, we remove the higher frequencies because multiple generation copies of a given image, as well as compression-decompression transformations, tend to wipe out higher frequencies anyway, so there is no point in placing too much identification signal mto diese frequencies if diey wdl be uie ones most prone to being attenuated. Therefore, our new filtered mdependent noise images wdl be dominated by mid-spatial frequencies. On a practical note, smce we are using I2-bιt values on our scanner and we have removed the DC value effectively and our new rms noise wdl be slightly less man 10 digital numbers, it is useful to boil dus down to a 6-bit value ranging from -32 through 0 to 31 as uie resultant random image. Next we add all of die random images togemer which have a ' 1 ' in ±eir corresponding bit value of die 32-bit identification word, accumulating die result in a 16-bit signed integer image. This is the unattenuated and un-scaled version of the composite embedded signal. Next we experiment visually wuh adding die composite embedded signal to the oπgmal digital image, dirough varymg uie X and Y parameters of equation 2. In formula, we visually iterate to both maximize X and to find die appropπate Y m die following: V dB , n πι = V„ v n,m + V comp n m *X*sqrt(4 + V 0 „ - n m Λ Y) (3) where dist refers to the candidate distπbutable image, 1 e. we are visually iterating to find what X and Y will give us an acceptable image; oπg refers to me pixel value of die oπginai image; and comp refers to the pixel value of die composite image The n's and m's still index rows and columns of me image and indicate diat dus operation is done on all 4000 by 4000 pixels The symbol V is die DN of a given pixel and a given image. As an arbitrary assumption, now, we assume mat our visual expeπmentauon has found mat die value of X= 0.025 and Y=0.6 are acceptable values when comparmg the oπgmal image wuh die candidate distπbutable image. This is to say, die distπbutable image with die "extra noise" is acceptably close to die onginal in an aesdietic sense. Note mat smce our mdividual random unages had a random rms noise value around 10 DN, and that adding approximately 16 of diese tmages together will mcrease die composite noise to around 40 DN, die X multiplication value of 0.025 will bπng die added rms noise back to around 1 DN, or half die amplitude of our innate noise on the onginal. This is roughly a 1 dB gam in noise at die dark pixel values and coπespondingly more at uie bπghter values modified by the Y value of 0.6. So wuh diese two values of X and Y, we now have constructed our first versions of a distπbutable copy of the oπgmal. Omer versions wdl merely create a new composite signal and possibly change the X slighdy if deemed necessary We now lock up die oπgmal digital image along wuh uie 32-bιt identification word for each version, and die 32 mdependent random 4-bit unages, waiting for our first case of a suspected piracy of our oπgmal. Storage wise, dus is about 14 Megabytes for uie onginal image and 32*0.5bytes*16 million = — 256 Megabytes for die random mdividual encoded images. This is quite acceptable for a smgie valuable image. Some storage economy can be gamed by simple lossless compression. Finding a Suspected Piracy of our Image We sell our image and several months later find our two heads of state in die exact poses we sold uiem in, seemingly cut and lifted out of our image and placed mto anouier stylized background scene. This new "suspect" image is bemg printed in 100,000 copies of a given magazine issue, let us say We now go about determining if a portion of our oπgmal image has mdeed been used m an unauuionzed manner. Fig. 3 summarizes the details. The first step is to take an issue of the magazine, cut out me page with die image on it, then carefully but not too carefully cut out die two figures from me background image using ordinary scissors. If possible, we will cut out only one connected piece radier dian die two figures separately We paste dus onto a black background and scan this mto a digital foπn. Next we electronically flag or mask out the black background, which is easy to do by visual inspection. We now procure die oπgmal digital image from our secured place along widi me 32-bit identification word and die 32 individual embedded images. We place die oπgmal digital image onto our computer screen using standard image manipulation software, and we roughly cut along ώe same borders as our masked area of ώe suspect image, masking ώis image at the same time m roughly ώe same manner. The word 'roughly' is used smce an exact cuttmg is not needed, it merely aids ώe identification statistics to get it reasonably close. Next we rescale ώe masked suspect image to roughly match ώe size of our masked oπgmal digital image, ώat is, we digitally scale up or down ώe suspect image and roughly overlay it on ώe onginal image. Once we have performed ώis rough registration, we ώen throw ώe two images into an automated scaling and registration program. The program performs a search on ώe three parameters of x position, y position, and spatial scale, wiώ ώe figure of meπt being ώe mean squared error between ώe two images given any given scale vaπable and x and y offset. This is a fairiy standard image processing methodology. Typically dus would be done using generally smooth interpolation techniques and done to sub-pixel accuracy. The search method can be one of many, where ώe simplex method is a typical one. Once ώe optimal scaling and x-y position vaπabies are found, next comes an ' oώer search on optimizing ώe black level, brightness gain, and gamma of ώe two images. Again, ώe figure of merit to be used is mean squared error, and again ώe simplex or oώer search methodologies can be used to optimize ώe three vaπabies. After these three variables are optimized, we apply their corrections to ώe suspect image and align it to exactly ώe pixel spacing and masking of ώe oπgmal digital image and its mask. We can now call ώis ώe standard mask. The next step is to subtract ώe original digital image from ώe newly normalized suspect image only within ώe standard mask region. This new image is called ώe difference image. Then we step ώrough all 32 individual random embedded images, doing a local cross-correlation between ώe masked difference image and ώe masked individual embedded image. 'Local' refers to ώe idea ώat one need only start correlatmg over an offset region of +/- 1 pixels of offset between ώe nominal registrauon points of ώe two images found duπng ώe search procedures above. The peak correlation should be very close to ώe nominal registration point of 0,0 offset, and we can add ώe 3 by 3 coπelation values together to give one grand coπeiation value for each of ώe 32 individual bits of our 32-bit identification word. After doing ώis for all 32 bit places and their conesponding random images, we have a quasi-floating point sequence of 32 values. The first four values represent our calibration signal of 0101. We now take the mean of ώe first and third floating pomt value and call ώis floating pomt value '0,' and we take ώe mean of ώe second and ώe fourth value and call ώis floating pomt value ' 1. ' We ώen step ώrough all remaining 28 bit values and assign either a '0' or a ' 1 ' based simply on which mean value ώey are closer to. Stated simply, if ώe suspect image is indeed a copy of our onginal, ώe embedded 32-bit resultmg code should match ώat of our records, and if it is not a copy, we should get general randomness The ώird and ώe fourth possibilities of 3) Is a copy but doesn't match identification number and 4) isn't a copy but does match are, in ώe case of 3), possible if ώe signal to noise ratio of ώe process has plummeted, i.e. ώe ' suspect image' is truly a very poor copy of ώe oπgmal, and in ώe case of 4) is basically one chance in four billion smce we were usmg a 32-bit identification number If we are truly worπed about 4), we can just have a second independent lab perform their own tests on a different issue of ώe same magazine Finally, checking ώe error-check bits against what ώe values give is one final and possibly overkill check on ώe whole process. In situations where signal to noise is a possible problem, ώese enor checking bits might be eliminated without too much harm. Benefits Now ώat a full descπption of ώe first embodiment has been descπbed via a detailed example, it is appropπaie to pomt out ώe rationale of some of ώe process steps and ώeir benefits. The ultimate benefits of ώe foregoing process are ώat obtaining an identification number is fully mdependent of ώe manners and meώods of preparing ώe difference image. That is to say, ώe manners of preparmg ώe difference image, such as cuttmg, registering, scaling, etcetera, cannot mcrease ώe odds of fmdmg an identification number when none exists; it only helps ώe signal-to-noise ratio of ώe identification process when a true identification number is present. Meώods of preparmg images for identification can be different from each oώer even, providing ώe possibility for multiple mdependent methodologies for making a match. The abdity to obtain a match even on sub-sets of ώe oπginai signal or image is a key pomt in today's lnformation-πch world. Cuttmg and pasting boώ unages and sound clips is becoming more common, allowing such an embodiment to be used m detectmg a copy even when oπgmal matenal has been ώus corrupted. Finally, ώe signal to noise ratio of matching should begm to become difficult only when ώe copy mateπal itself has been significantly altered either by noise or by sigmficant distortion; boώ of ώese also will affect ώat copy's commercial value, so ώat trying to thwart ώe system can only be done at ώe expense of a huge decrease in commercial value. An early conception of ώis technology was ώe case where only a single "snowy image" or random signal was added to an onginal image, i.e. ώe case where N = 1. "Decodmg" ώis signal would mvolve a subsequent mathematical analysis usmg (generally statistical) algoπώms to make a judgment on ώe presence or absence of ώis signal. The reason ώis approach was abandoned as ώe prefened embodiment was ώat ώere was an inherent gray area m ώe certainty of detectmg ώe presence or absence of ώe signal. By moving onward to a multitude of bit planes, i.e. N > 1 , combined wiώ simple pre¬ defined algonώms prescπbmg ώe manner of choosmg between a "0" and a "1 " , ώe certainty question moved from ώe realm of expert statistical analysis mto ώe realm of guessmg a random bmary event such as a com flip This is seen as a powerful feature relative to ώe intuitive acceptance of ώis technology in boώ ώe courtroom and ώe markerplace. The analogy which summarizes ώe inventor's ώoughts on ώis whoie question is as follows The search for a single identification signal amounts to calling a com flip only once, and relying on arcane experts to make ώe call; whereas ώe N > 1 embodiment relies on ώe broadly intuitive principle of correctly calling a com flip N times in a row This situation is greatly exacerbated, i.e. ώe problems of "interpretation" of ώe presence of a single signal, when images and sound clips get smaller and smaller in extent. Another important reason ώat ώe N > 1 case is prefened over ώe N = I embodiment is ώat m ώe N = 1 case, ώe manner in which a suspect image is prepared and manipulated has a direct bearing on ώe likel±ood of making a positive identification. Thus, ώe mann r with which an expert makes an identification determination becomes an integral pan of ώat determination. The existence of a multitude of mathematical and statistical approaches to making ώis determination leave open ώe possibility ώat some tests might make positive identifications while others might make negative determinations, inviting further arcane debate about ώe relative meπts of ώe vanous identification approaches. The N> 1 embodiment avoids ώis further gray area by presentmg a meώod where no amount of pre-processmg of a signal - oώer than pre-processmg which suneptitiously uses knowledge of ώe pπvate code signals - can mcrease ώe likelihood of "calling ώe com flip N tunes m a row. " The fullest expression of ώe present system will come when it becomes an industry standard and numerous mdependent groups set up with ώeir own means or 'in-house' brand of applying embedded identification numbers and in ώeir decipherment. Numerous mdependent group identification will further enhance ώe ultimate objectivity of ώe meώod, ώereby enhancing its appeal as an industry standard Use of True Polaπrv in Creating ώe Composite Embedded Code Signal The foregoing discussion made use of ώe 0 and 1 formalism of bmary technology to accomplish its ends. Specifically, ώe 0's and l's of ώe N-bit identification word direcdy multiplied their conesponding mdividual embedded code signal to form ώe composite embedded code signal (step 8, Fig 2). This approach certainly has its conceptual simplicity, but ώe multiplication of an embedded code signal by 0 along with ώe storage of ώat embedded code contams a kind of inefficiency It is preferred to maintain ώe formalism of ώe 0 and 1 nature of ώe N-bit identification word, but to have ώe 0's of ώe word mduce a subtraction of ώeir coπcspondmg embedded code signal. Thus, in step 8 of Fig. 2, raώer than only 'adding' ώe individual embedded code signals which conespond to a T m ώe N-bit identification word, we will also 'subtract' ώe mdividual embedded code signals which conespond to a '0 * m ώe N-bit identification word. At first glance ώis seems to add more apparent noise to ώe final composite signal But it also increases ώe energy-wise separation of ώe 0's from ώe l 's, and ώus ώe 'gam' which is applied in step 10, Fig 2 can be coπespondiπgly lower We can refer to ώis improvement as ώe use of true polaπty The mam advantage of ώis improvement can largely be summarized as 'mformational efficiency.' 'Perceptual Orthogonality' of the Individual Embedded Code Sisals The foregomg discussion contemplates ώe use of generally random noise-like signals as ώe mdividual embedded code signals. This is perhaps ώe simplest form of signal to generate. However, ώere is a foπn of informational optimization which can be applied to ώe set of ώe mdividual embedded signals, which ώe applicant descπbes under ώe rubπc 'perceptual orthogonality ' This term is loosely based on ώe mathematical concept of ώe orthogonality of vectors, wiώ ώe current additional requirement ώat ώis orthogonality should maximize ώe signal energy of ώe identification information whde maintaining it below some perceptibdity threshold. Put another way, ώe embedded code signals need not necessarily be random m nature. Use and Improvements of ώe First Embodiment m ώe Field of Emulsion-Based Photography The foregomg discussion oudmed techniques ώat are applicable to photographic mateπals The following section explores ώe detads of ώis area further and discloses certain improvements which lend ώemselves to a broad range of applications. The first area to be discussed involves ώe pre-apphcation or pre-exposing of a senal number onto traditional photographic products, such as negative film, print paper, transparencies, etc In general, ώis is a way to embed a pnon umque seπal numbers (and by implication, ownership and tracking mformation) mto photographic mateπal. The seπal numbers ώemselves would be a permanent pan of ώe normally exposed picture, as opposed to bemg relegated to ώe margins or stamped on ώe back of a printed photograph, which all require separate locations and separate meώods of copymg. The 'seπal number' as it is called here is generally synonymous wiώ ώe N-bit identification word, only now we are usmg a more common lndustπai terminology. In Fig. 2, step 11, ώe disclosure calls for ώe storage of ώe "oπgmal [image]" along wiώ code images. Then m Fig. 3, step 9, it directs ώat ώe oπgmal be subtracted from ώe suspect image, ώereby leavmg ώe possible identification codes plus whatever noise and corruption has accumulated. Therefore, ώe previous disclosure made ώe tacit assumption ώat ώere exists an onginal without ώe composite embedded signals. Now in ώe case of selling pπnt paper and oώer duplication film products, ώis will still be ώe case, 1 e., an "onginal" wiώout ώe embedded codes will indeed exist and ώe basic meώodology of ώe first embodiment can be employed. The oπginai film serves perfectly well as an 'unencoded oπginai ' However, in ώe case where pre-exposed negative film is used, ώe composite embedded signal on ώe onginal film and ώus ώere will never be an "oπgmal" separate from ώe pre-embedded signal. It is ώis latter case, ώerefore, which will be examined a bit more closely, along wiώ observations on how to best use ώe principles discussed above (ώe former cases adhering to ώe previously outlined meώods) The clearest pomt of departure for ώe case of pre-numbered negative film, i.e. negative film which has had each and every frame pre-exposed wiώ a very famt and umque composite embedded signal, comes at step 9 of Fig. 3 as previously noted. There are certainly oώer differences as well, but ώey are mostly logistical in nature, such as how and when to embed ώe signals on ώe film, how to store ώe code numbers and seπal number, etc. Obviously ώe pre-exposing of film would mvolve a major change to ώe general mass production process of creating and packaging film. Fig. 4 has a schematic ouduung one potential post-hoc mechanism for pre-exposing film. 'Post- hoc' refers to apply mg a process after ώe full common manufacturing process of film has already taken place. Eventually, economies of scale may dictate placing ώis pre-exposing process directly mto ώe chain of manufacturing film. Depicted in Fig. 4 is what is commonly known as a film wπtiπg system. The computer, 106, displays ώe composite signal produced in step 8, Fig. 2, on its phosphor screen. A given frame of film is ώen exposed by imagmg dus phosphor screen, where ώe exposure level is generally very famt, i.e. generally imperceptible Clearly, ώe marketplace will set its own demands on how famt ώis should be, ώat is, ώe level of added 'grauuness' as practitioners would put it. Each frame of film is sequentially exposed, where m general ώe composite image displayed on ώe CRT 102 is changed for each and every frame, ώereby givmg each frame of film a different seπal number. The transfer lens 104 highlights ώe focal conjugate planes of a film frame and ώe CRT face. Getting back to ώe applymg ώe principles of ώe foregomg embodiment m ώe case of pre-exposed negative film... At step 9, Fig. 3, if we were to subtract ώe "oπgmal" wiώ its embedded code, we would obviously be "erasing" ώe code as well smce ώe code is an integral part of ώe onginal. Fortunately, remedies do exist and identifications can still be made. However, it will be a challenge to artisans who refine dus embodiment to have ώe signal to noise ratio of ώe identification process in ώe pre-exposed negative case approach ώe signal to noise ratio of ώe case where ώe un-encoded onginal exists. A succinct definition of ώe problem is m order at dus pomt. Given a suspect picture (signal), find ώe embedded identification code IF a code exists at al. The problem reduces to one of finding ώe amplitude of each and every mdividual embedded code signal within ώe suspect picture, not only wiώin ώe context of noise and corruption as was previously explamed, but now also wiώm ώe context of ώe coupling between a captured image and ώe codes. 'Coupling' here reters to the idea ώat ώe captured image "randomly biases" ώe cross-conelation So, bearing in mind ώis additional item of signal coupling, ώe identification process now estimates ώe signal amplitude of each and every mdividual embedded code signal (as opposed to taking ώe cross-conelauon result of step 12, Fig. 3) If our identification signal exists in ώe suspect picture, the amplitudes ώus found will spin into a polaπty wiώ positive amplitudes being assigned a ' 1 ' and negative amplitudes bemg assigned a '0 * Our unique idennficanon code manifests itself If, on ώe oώer hand, no such identification code exists or it is someone else's code, ώen a random gaussian-like distnbution of amplitudes is found wiώ a random hash of values It remains to provide a few more details on how ώe amplitudes of ώe mdividual embedded codes are found. Again, fortunately, ώis exact problem has been treated in oώer technological applications Besides, dirow ώis problem and a little food mto a crowded room of mathematicians and statisticians and surely a half dozen optimized meώodologies will pop out after some reasonable penod of tune. It is a rather cleanly defined problem. ' One specific example solution comes from ώe field of astronomical imaging. Here, it is a mature pπor an to subtract out a "thermal noise frame" from a given CCD image of an object Often, however, it is not precisely known what scaling factor to use in subtractmg ώe ώeπnal frame, and a search for ώe correct scaling factor is pertoπned. This is precisely ώe task of ώis step of ώe present embodiment General practice merely performs a common search aigoπdim on ώe scaling factor, where a scaling factor is chosen and a new image is created accordmg to NEW IMAGE = ACQUIRED IMAGE - SCALE * THERMAL IMAGE (4) The new image is applied to ώe fast fouπer transform routine and a scale factor is eventually found which minimizes ώe integrated high frequency content of ώe new image This general type of search operation wiώ its minimization of a particular quantity is exceedingly common. The scale factor ώus found is ώe sought-for "amplitude." Refinements which are contemplated but not yet implemented are where ώe couplmg of ώe higher deπvatives of ώe acquired image and ώe embedded codes are estimated and removed from ώe calculated scale factor. In oώer words, certain bias effects from ώe coupling menuoned earlier are present and should be eventually accounted for and removed boώ ώrough theoretical and empiπcai experimentation. Use and Improvements m ώe Detection of Signal or Image Alteration Apaπ from ώe basic need of identifying a signal or image as a whole, ώere is also a raώer ubiquitous need to detect possible alterations to a signal or image The followmg section descnbes how ώe foregomg embodiment, wiώ ceπain modifications and improvements, can be used as a powerful tool in ώis area. The potential sceπaπos and applications of detectmg alterations are innumerable. To first summaπze, assume ώat we have a given signal or image which has been positively identified usmg ώe basic meώods oudined above. In oώer words, we know its N-bit identifica t ion word, us individual embedded code signals, and its composite embedded code. We can ώen fairly simply crea t e a spatial map of ώe composite code's amplitude wiώm our given signal or image. Furthermore, we can divide dus amplimde map by ώe known composite code's spatial amplitude, giving a normalized map, i.e. a map which should fluctuate about some global mean value. By simple examination of ώis map, we can visually detect any areas which have been significantly altered wherem ώe value of ώe normalized amplitude dips below some statistically set ώreshold based purely on typical noise and corrπption (error). The details of implementing ώe creation of ώe amplitude map have a vaπety of choices. One is to perform ώe same procedure which is used to detemune ώe signal amplimde as descπbed above, only now we step and repeat ώe multiplication of any given area of ώe signal/image wiώ a gaussian weight function centered about ώe area we are investigating. Universal Venus Custom Codes The disclosure ώus far has oudined how each and every source signal has its own unique set of mdividual embedded code signals. This entails ώe storage of a significant amount of additional code mformation above and beyond ώe onginal, and many applications may meπt some form of economizing. One such approach to economizing is to have a given set of mdividual embedded code signals be common to a batch of source mateπals. For example, one ώousand images can all utdize ώe same basic set of individual embedded code signals. The storage requirements of ώese codes ώen become a small fraction of ώe overall storage requirements of ώe source mateπal. Furthermore, some applications can utdize a universal set of mdividual embedded code signals, i.e., codes which remain ώe same for all instances of distπbuted mateπal. This type of requirement would be seen by systems which wish to hide ώe N-bit identification word itself, yet have standardized equipment be able to read ώat word. This can be used in systems which make go/no go decisions at pomt-of-read locations. The potential drawback to dus set-up is ώat ώe universal codes are more prone to be sleuώed or stolen; therefore ώey will not be as secure as ώe apparatus and meώodology of ώe previously disclosed arrangement. Perhaps ώis is just ώe difference between 'high secunry' and 'air-tight secuπty,' a distinction carrying ' little weight wiώ ώe bulk of potential applications. Use in Printing. Paper. Documents. Plastic Coated Identification Cards, and Oώer Mateπal Where Global Embedded Codes Can Be Imprinted The term 'signal' is often used narrowly to refer to digital data mformation, audio signals, images, etc. A broader interpretation of 'signal,' and ώe one more generally intended, includes any form of modulation of any mateπal whatsoever. Thus, ώe micro-topology of a piece of common paper becomes a 'signal' (e.g. it height as a function of x-y coordinates). The reflective properties of a flat piece of plastic (as a function of space also) becomes a signal. The point is ώat photographic emulsions, audio signals, and digitized information are not ώe only types of signals capable of utilizing ώe principles described herein. „__. As a case in point, a machine very much resembling a braille printing marhinf can be designed so as to imprint unique 'noise-like' indentations as outlined above. These indentations can be applied wiώ a pressure which is much smaller ώan is typically applied in creating braille, to ώe point where ώe patterns are not noticed by a normal user of ώe paper. But by following ώe steps of ώe present disclosure and applying ώem via ώe mechanism of micro-indentations, a unique identification code can be placed onto any given sheet of paper, be it intended for everyday stationary purposes, or be it for important documents, legal tender, or oώer secured material. The reading of ώe identification material in such an embodiment generally proceeds by merely reading ώe document optically at a variety of angies. This would become an inexpensive meώod for deducing ώe micro-topology of ώe paper surface. Certainly oώer forms of reading ώe topology of ώe paper are possible as well. In ώe case of plastic encased material such as identification cards, e.g. driver's licenses, a similar braille-like impressions machine can be utilized to imprint unique identification codes. Subtle layers of photoreactive materials can also be embedded inside ώe plastic and 'exposed.' It is clear ώat wherever a material exists which is capable of being modulated by 'noise-like' signals, ώat material is an appropriate carrier for unique identification codes and utilization of ώe principles disclosed herein. All ώat remains is ώe matter of economically applying ώe identification information and maintaining ώe signal level below an acceptability threshold which each and every application wdl define for itself. REAL TIME ENCODER While ώe first class of embodiments most commonly employs a standard microprocessor or computer to perform ώe encodation of an image or signal, it is possible to utilize a custom encodation device which may be faster ώan a typical Von Neuman-type processor. Such a system can be utilized wiώ all manner of serial data streams. Music and videotape recordings are examples of serial data streams - data streams which are often pirated. It would assist enforcement efforts if auώorized recordings were encoded wiώ identification data so ώat pirated knock-offs could be traced to ώe original from which ώey were made. Piracy is but one concern driving ώe need for applicant's technology. Another is auώentication. Often it is important to confirm ώat a given set of data is really what it is purported to be (often several years after its generation). To address ώese and oώer needs, ώe system 200 of Fig. 5 can be employed. System 200 can be thought of as an identification coding black box 202. The system 200 receives an input signal (sometimes termed ώe "master" or "unencoded" signal) and a code word, and produces (generally in real time) an identification-coded output signal. (Usually, ώe system provides key data for use in later decoding.) The contents of ώe "black box" 202 can take various forms. An exemplary black box system is shown in Fig. 6 and includes a look-up table 204, a digital noise source 206, first and second sealers 208, 210, an adder/subtracter 212, a memory 214, and a register 216. The input signal (which in ώe illustrated embodiment is an 8 - 20 bit data signal provided at a rate of one million samples per second, but which in oώer embodiments could be an analog signal if appropriate A/D and D/A conversion is provided) is applied from an input 218 to ώe address input 220 of ώe look-up table 204. For each mput sample (i.e. look-up table address), ώe table provides a corresponding 8-bit digital output word. This output word is used as a scaling factor ώat is applied to one input of ώe first sealer 208. The first sealer 208 has a second input, to which is applied an 8-bit digital noise signal from source 206. (In ώe illustrated embodiment, ώe noise source 206 comprises an analog noise source 222 and an analog-to-digital convener 224 although, again, oώer implementations can be used.) The noise source in ώe illustrated embodiment has a zero mean ouφut value, wiώ a full width half maximum (FWHM) of 50 - 100 digital numbers (e.g. from -75 to +75). The first sealer 208 multiplies ώe two 8-bit words at its inputs (scale factor and noise) to produce ~ for each sample of ώe system input signal ~ a 16-bit ouφut word. Since ώe noise signal has a zero mean value, ώe ouφut of ώe first sealer likewise has a zero mean value. The ouφut of ώe first sealer 208 is applied to ώe mput of ώe second sealer 210. The second sealer serves a global scaling function, establishing ώe absolute magnitude of ώe identification signal ώat will ultimately be embedded into ώe input data signal. The scaling factor is set ώrough a scale control device 226 (which may take a number of forms, from a simple rheostat to a graphically implemented control in a graphical user interface), permitting this factor to be changed in accordance wiώ ώe requirements of different applications. The second sealer 210 provides on its ouφut line 228 a scaled noise signal. Each sample of ώis scaled noise signal is successively stored in ώe memory 214. (In ώe illustrated embodiment, ώe ouφut from ώe first sealer 208 may range between -1500 and + 1500 (decimal), while ώe ouφut from ώe second sealer 210 is in ώe low smgle digits, (such as between -2 and +2).) Register 216 stores a multi-bit identification code word. In ώe illustrated embodiment ώis code word consists of 8 bits, although larger code words (up to hundreds of bits) are commonly used. These bits are referenced, one at a time, to control how ώe input signal is modulated wiώ ώe scaled noise signal. In particular, a pointer 230 is cycled sequentially ώrough ώe bit positions of ώe code word in register 216 to provide a control bit of "0" or "1 " to a control mput 232 of ώe adder/subtracter 212. If , for a particular mput signal sample, ώe control bit is a " 1 ", ώe scaled noise signal sample on Ime 232 is added to ώe input signal sample. If ώe control bit is a "0", ώe scaled noise signal sample is subtracted from ώe mput signal sample. The ouφut 234 from ώe adder/subtracter 212 provides ώe black box's ouφut signal. The addition or subtraction of ώe scaled noise signal m accordance wiώ ώe bus ot ώe code word effects a modulation of ώe mput signal ώat is generally imperceptible. However, wiώ knowledge of ώe contents of ώe memory 214, a user can later decode ώe encodmg, determining ώe code number used in ώe oπgmal encodmg process. (Actually, use of memory 214 is optional, as explamed below ) It will be recognized ώat ώe encoded signal can be distnbuted in well known ways, including converted to printed image form, stored on magnetic media (floppy diskette, anaiog or DAT tape, etc.), CD-ROM, etc. etc. Decoding A vaπety of techniques can be used to determine ώe identificauon code wiώ which a suspect signal has been encoded. Two are discussed below. The first is less preferable ώan ώe tatter for most applications, but is discussed herein so ώat ώe reader may have a fuller context withm which to understand ώe disclosed technology. More particularly, ώe first decodmg meώod is a difference meώod, relying on subtraction of conesponding samples of ώe oπgmal signal from ώe suspect signal to obtain difference samples, which are ώen exammed (typically mdividually) for determmisuc codmg mdicia (i.e. ώe stored noise data). This approach may ώus be termed a "sample-based, deterministic" decodmg technique The second decoding meώod does not make use of ώe onginal signal. Nor does it examine particular samples lookmg for predetermmed noise characteπstics. Raώer, ώe statistics of ώe suspect signal (or a portion ώereof) are considered in ώe aggregate and analyzed to discern ώe presence of identificauon codmg ώat permeates ώe entire signal. The reference to permeation means ώe entire identification code can be discerned from a small fragment of ώe suspect signal. This latter approach may ώus be termed a "holographic, stausncai" decoding technique. Boώ of ώese meώods begin by registering ώe suspect signal to match ώe oπgmal. This entails scaling (e.g. m amplitude, duration, color balance, etc.), and sampling (or resampling) to restore ώe onginal sample rate. As in ώe earlier descπbed embodiment, ώere are a vaπety of well understood techniques by which ώe operations associated wiώ ώis registration function can be performed. As noted, ώe first decodmg approach proceeds by subtracting ώe oπgmal signal from ώe registered, suspect signal, leavmg a difference signal The polanty of successive difference signal samples can ώeπ be compared wiώ ώe polarities of ώe conesponding stored noise signal samples to determine ώe identification code. That is, if ώe polarity of ώe first difference signal sample matches ώat of the first noise signal sample, ώen ώe first bit of ώe identification code is a "1. " (In such case, ώe polaπty of ώe 9th, 17ώ, 25ώ, etc. samples should also all be positive.) If ώe polarity of ώe first difference signal sample is opposite ώat of ώe conesponding noise signal sample, then ώe first bit of ώe identification code is a "0." By conducting ώe foregomg analysis wiώ eight successive samples of ώe difference signal, ώe sequence of bits ώat comprise ώe original code word can be determined. If, as in ώe illustrated embodiment, pointer 230 stepped ώrough ώe code word one bit at a time, beginning wiώ ώe first bit, during encoding, ώen ώe first 8 samples of ώe difference signal can be analyzed to uniquely detemune ώe value of ώe 8-bit code word. In a noise-free world (speaking here of noise independent of ώat wiώ which ώe identification coding is effected), ώe foregoing analysis would always yield ώe conect identification code. But a process ώat is only applicable in a noise-free world is of limited utility indeed. (Further, accurate identification of signals in noise-free contexts can be handled in a variety of oώer, simpler ways: e.g. checksums; statistically improbable conespondeπce between suspect and original signals; etc.) While noise- induced aberrations in decoding can be dealt wiώ - to some degree ~ by analyzing large portions of ώe signal, such abeπations still place a practical ceiling on ώe confidence of ώe process. Further, ώe villain ώat must be confronted is not always as benign as random noise. Raώer, it increasingly takes ώe form of human-caused corruption, distortion, manipulation, etc. In such cases, ώe desired degree of identification confidence can only be achieved by oώer approaches. The illustrated embodiment (ώe "holographic, statistical" decoding technique) relies on recombining ώe suspect signal wiώ certain noise data (typically ώe data stored in memory 214), and analyzing ώe entropy of ώe resulting signal. "Entropy" need not be understood in its most strict mathematical definition, it being merely ώe most concise word to describe randomness (noise, smooώness, snowiness, etc.). Most serial data signals are not random. That is, one sample usually coπelates - to some degree -- wiώ ώe adjacent ' samples. Noise, in contrast, typically is random. If a random signal (e.g. noise) is added to (or subtracted from) a non-random signal, ώe entropy of ώe resulting signal generally increases. That is, ώe resulting signal has more random variations ώan ώe original signal. This is ώe case wiώ ώe encoded ouφut signal produced by ώe present encoding process; it has more entropy ώan ώe original, unencoded signal. If, in contrast, ώe addition of a random signal to (or subtraction from) a non-random signal reduces entropy, ώen someώing unusual is happening. It is ώis anomaly ώat ώe present decodmg process uses to detect embedded identification codmg To fully understand ώis entropy-based decodmg meώod, it is first helpful to highlight a eharactenstic of ώe onginal encodmg process: ώe similar treatment of every eighώ sample In ώe encodmg process discussed above, ώe pomter 230 increments ώrough ώe code word, one bit for each successive sample of ώe mput signal. If ώe code word is eight bits in lengώ, ώen ώe pomter returns to ώe same bit position m ώe code word every eighώ signal sample. If ώis bit is a "1", noise is added to ώe mput signal; if ώis bit is a "0", noise is subtracted from ώe mput signal. Due to ώe cyclic progression of ώe pomter 230, every eighώ sample of an encoded signal ώus shares a eharactenstic: ώey are all eiώer augmented by ώe corresponding noise data (which may be negative), or ώey are all diminished, dependmg on whether ώe bit of ώe code word then being addressed by pomter 230 is a " 1 " or a "0" To exploit this eharactenstic, ώe entropy-based decodmg process treats every eighώ sample of ώe suspect signal in like fashion. In particular, ώe process begins by addmg to ώe 1st, 9th, 17th, 25ώ, etc. samples of ώe suspect signal ώe corresponding scaled noise signal values stored in ώe memory 214 (i.e ώose stored in ώe 1st, 9ώ, 17th, 25th, etc., memory locations, respectively) The entropy of ώe resulting signal (i.e. ώe suspect signal wiώ every 8ώ sample modified) is ώen computed (Computation of a signal's entropy or randomness is well understood by artisans in ώis field. One generally accepted tecimique is to take ώe deπvative of ώe signal at each sample pomt, square ώese values, and ώen sum over ώe enure signal. However, a vaπety of oώer well known techniques can alternatively be used.) The foregomg step is ώen repeated, ώis tune subtracting ώe stored noise values from ώe 1st, 9th, 17ώ, 25 etc. suspect signal samples. One of ώese two operations will undo ώe encodmg process and reduce ώe resulting signal's entropy; ώe oώer wdl aggravate it. If addmg ώe noise data m memory 214 to ώe suspect signal reduces its entropy, ώen ώis data must earlier have been subtracted from ώe onginal signal This indicates ώat pomter 230 was pointing to a "0" bit when ώese samples were encoded. (A "0" at ώe control mput of adder/subtracter 212 caused it to subtract ώe scaled noise from ώe mput signal.) Conversely, if subtracting ώe noise data from every eighώ sample of ώe suspect signal reduces its entropy, ώen ώe encoding process must have earlier added dus noise. This mdicates ώat pomter 230 was pointing to a "1" bit when samples 1, 9, 17, 25, etc., were encoded. By noting whether emropy decreases by (a) addmg or (b) subtracting ώe stored noise data to/from ώe suspect signal, it can be determmed ώat ώe first bit of ώe code word is (a) a "0", or (b) a "1 " The foregomg operations are ώen conducted for ώe group of spaced samples of ώe suspect signal beginning wiώ ώe second sample (i.e. 2, 10, 18, 26 ...). The entropy of ώe resultmg signals indicate whether ώe second bit of ώe code word is a "0" or a "1 " Likewise wiώ ώe following 6 groups of spaced samples in ώe suspect signal, until all 8 bits of ώe code word have been discerned. It will be appreciated ώat ώe foregoing approach is not sensitive to corruption mechanisms ώat alter ώe values of mdividual samples; instead, ώe process considers ώe entropy of ώe signal as a whole, yielding a high degree of confidence in ώe results. Further, even small exceφts of ώe signal can be analyzed in ώis manner, permitting piracy of even small details of an oπgmal work to be detected. The results are ώus statistically robust, boώ in ώe face of natural and human corruption of ώe suspect signal. It will further be appreciated ώat ώe use of an N-bit code word in ώis real time embodiment provides benefits analogous to ώosc discussed above in connection wiώ ώe batch encodmg system. (Indeed, ώe present embodiment may be conceptualized as making use of N different noise signals, j ust as in ώe batch encodmg system. The first noise signal is a signal havmg ώe same extent as ώe mput signal, and compnsmg ώe scaled noise signai at ώe 1st, 9th, 17ώ, 25ώ, etc., samples (assuming N=8), wiώ zeroes at ώe mtervenmg samples. The second noise signal is a similar one compnsmg ώe scaled noise signal at ώe 2d, lOώ, 18ώ, 26ώ, etc., samples, wiώ zeroes at ώe mtervenmg samples. Etc. These signals are all combmed to provide a composite noise signal.) One of ώe important advantages mherent m such a system is ώe high degree of statistical confidence (confidence which doubles wiώ each successive bit of ώe identification code) ώat a match is really a match. The system does not rely on subjective evaluation of a suspect signal for a smgle, deterministic embedded code signal. Illustrative Vanations From ώe foregomg descπption, it will be recognized ώat numerous modifications can be made to ώe illustrated systems wiώout changing ώe fundamental principles. A few of ώese vanations are described below. The above-descπbed decodmg process tries boώ adding and subtracting stored noise data to/from ώe suspect signal in order to find which operation reduces entropy. In oώer embodiments, only one of ώese operations needs to be conducted. For example, m one alternative decoding process ώe stored noise data corresponding to every eighώ sample of ώe suspect signal is only added to said samples. If ώe entropy of ώe resultmg signal is ώereby mcreased, ώen ώe conespondmg bit of ώe code word is a " 1 " (i.e. ώis noise was added earlier, duπng ώe encoding process, so adding it again only compounds ώe signal's randomness). If ώe entropy of ώe resulting signal is ώereby decreased, ώen ώe conesponding bit of ώe code word is a "0". A further test of entropy if ώe stored noise samples are subtracted is not required. The statistical reliability of ώe identification process (coding and decoding) can be designed t o exceed virtually any confidence direshold (e.g 99 9% , 99 99% , 99.999%, etc. confidence) by appropna t e selection of ώe global scaling factors, etc Additional confidence m any given application (unnecessary in most applications; can be achieved by recheckmg ώe decodmg process. One way to recheck ώe decoding process is to remove ώe stored noise data from ώe suspect signal in accordance wiώ ώe bits of ώe discerned code word, yielding a "restored" signal (e.g. if ώe first bit of ώe code word is found to be "1 ," ώen ώe noise samples stored in ώe 1st, 9th, 17th, etc. locations of ώe memory 214 are subtracted from ώe conesponding samples of ώe suspect signal) The entropy of ώe restored signal is measured and used as a baselme m further measurements. Next, ώe process is repeated, ώis time removmg ώe stored noise data from ώe suspect signal in accordance wiώ a modified code word. The modified code word is ώe same as ώe discerned code word, except 1 bit is toggled (e.g. ώe first) The entropy of ώe resultmg signal is determined, and compared wiώ ώe baselme. If ώe togglmg of ώe bit in ώe discerned code word resulted m mcreased entropy, ώen ώe accuracy of ώat bit of ώe discerned code word is confirmed. The process repeats, each time wiώ a different bit of ώe discerned code word toggled, untd all bits of ώe code word have been so checked. Each change should result in an mcrease m entropy compared to ώe baselme value. The data stored tn memory 214 is subject to a vaπety of alternatives. In ώe foregomg discussion, memory 214 contains ώe scaled noise data. In oώer embodiments, ώe unsealed noise data can be stored instead. In stdl oώer embodiments, it can be desirable to store at least part of ώe mput signal itself in memory 214. For example, ώe memory can allocate 8 signed bits to ώe noise sample, and 16 bits to store ώe most significant bits of an 18- or 20-bit audio signal sample. This has several benefits. One is ώat it simplifies registration of a "suspect" signal. Another is ώat. in ώe case of encodmg an mput signal which was already encoded, ώe data m memory 214 can be used to discern which of ώe encodmg processes was performed first. That is, from ώe mput signal data in memory 214 (albeit incomplete), it is generally possible to detemune wiώ which of two code words it has been encoded. Yet another alternative for memory 214 is ώat is can be omitted altogether One way ώis can be achieved is to use a deterministic noise source m ώe encodmg process, such as an algoπώmic noise generator seeded wiώ a known key number. The same deterministic noise source, seeded wiώ ώe same key number, can be used m ώe decodmg process. In such an aπangement, only ώe key number needs be stored for later use m decodmg, instead of ώe large data set usually stored in memory 214. Alternatively, if ώe noise signal added dunng encoding does not have a zero mean value, and ώe length N of ώe code word is known to ώe decoder, ώen a universal decodmg process can be implemented This process uses ώe same entropy test as ώe foregomg procedures, but cycles through possible code words, adding/subtracting a small dummy noise value (e.g. less ώan ώe expected mean noise value) to every Nth sample of ώe suspect signai, in accordance wiώ ώe bits of ώe code word being tested, until a reduction in entropy is noted. Such an approach is not favored for most applications, however, because it offers less secuπty ώan ώe oώer embodiments (e.g. it is subject to cracking by brute force). Many applications are well served by ώe embodiment illustrated in Fig. 7, in which different code words are used to produce several differently encoded versions of an mput signal, each making use of ώe same noise data. More particularly, ώe embodiment 240 of Fig. 7 includes a noise store 242 mto which noise from source 206 is wπtten duπng ώe identification-coding of ώe mput signal wiώ a first code word. (The noise source of Fig. 7 is shown outside of ώe real tune encoder 202 for convenience of illustration.) Thereafter, additional identification-coded versions of ώe mput signal can be produced by readmg ώe stored noise data from ώe store and usmg it m conjunction wiώ second ώrough Nώ code words to encode ώe signal. (While bmary -sequential code words are dlustrated in Fig. 7, in oώer embodiments arbitrary sequences of code words can be employed.) Wiώ such an anaπgement, a great number of differendy- encoded signals can be produced, wiώout requiring a proportionally-sized long term noise memory. Instead, a fixed amount of noise data is stored, wheώer encoding an original once or a thousand tunes. (If desired, several differendy-eoded ouφut signals can be produced at ώe same tune, raώer ώan senatun. One such implementation includes a plurality of adder/subtracter circuits 212, each dπven wiώ ώe same mput signal and wiώ ώe same scaled noise signal, but wiώ different code words. Each, ώen, produces a differendy encoded ouφut signal.) In applications having a great number of differently-encoded versions of ώe same onginal, it will be recognized ώat ώe decodmg process need not always discern every bit of ώe code word. Sometimes, for example, ώe application may require identifying only a group of codes to which ώe suspect signal belongs. (E.g., high order bits of ώe code word might mdicate an organization to which several differendy coded versions of ώe same source matenai were provided, wiώ low-order bits identifying specific copies. To identify ώe organizauon wiώ which a suspect signal is associated, it may not be necessary to examine ώe low order bits, smce ώe organization can be identified by ώe high order bits alone.) If ώe identification requirements can be met by discerning a subset of ώe code word bits in ώe suspect signal, ώe decoding process can be sbonened. Some applications may be best served by restarting ώe encoding process - sometimes wiώ a different code word - several times within an integral work. Consider, as an example, videotaped productions (e.g. television programming). Each frame of a videotaped production can be identification- coded wiώ a umque code number, processed in real-time wiώ an arrangement 248 like ώat shown in Fig. 8. Each tune a vertical retrace is detected by sync detector 250, ώe noise source 206 resets (e g. to repeat ώe sequence just produced) and an identification code increments to ώe next value. Each frame of ώe videotape is ώereby uniquely identification-coded. Typically, ώe encoded signal is stored on a videotape for long term storage (alώough oώer storage media, including laser disks, can be used) Returning to ώe encodmg apparatus, ώe look-up table 204 in ώe illustrated embodiment exploits ώe fact ώat high amplitude samples of ώe tnμut data signal can tolerate (wiώout objectionable degradation of ώe ouφut signal) a higher level of encoded identification codmg ώan can low amplitude mput samples Thus, for example, mput data samples havmg decimal values of 0, 1 or 2 may be conespond (in ώe look¬ up table 204) to scale factors of unity (or even zero), whereas mput data samples havmg values in excess of 200 may conespond to scale factors of 15. Generally speaking, ώe scale factors and ώe mput sample values correspond by a square root relation. That is, a four-fold mcrease m a value of ώe sampled mput signal corresponds to approximately a two- fold mcrease in a value of ώe scaling factor associated therewith. (The parenthetical reference to zero as a scaling factor alludes to cases, e.g., in which ώe source signal is temporally or spatially devoid of information content In an image, for example, a region characteπzed by several contiguous sample values of zero may correspond to a jet black region of ώe frame. A scaling value of zero may be appropπate here smce ώere is essentially no image data to be pirated.) Contmumg wiώ ώe encoding process, ώose skilled in ώe art will recognized ώe potential for "rail enors" m ώe dlustrated embodiment. For example, if ώe mput signal consists of 8-bit samples, and ώe samples span ώe entire range from 0 to 255 (decimal), ώen ώe addition or subtraction of scaled noise to/from ώe mput signal may produce ouφut signals ώat cannot be represented by 8 bits (e.g -2, or 257) A number of well-understood techmques exist to rectify ώis simation, some of ώem proactive and some of ώem reactive. (Among ώese known techmques are: specify mg ώat ώe mput signal shall not have samples m ώe range of 0-4 or 251-255, ώereby safely permitting modulation by ώe noise signal, or including provision for detectmg and adaptively modifying mput signal samples ώat would otherwise cause rail errors.) While ώe dlustrated embodiment descnbes steppmg ώrough ώe code word sequentially, one bit at a time, to control modulation of successive bits of ώe mput signal, it will be appreciated ώat ώe bits of ώe code word can be used oώer ώan sequentially for ώis purpose Indeed, bus of ώe code word can be selected m accordance wiώ any predetermined algoπώm. The dynamic scaling of ώe noise signal based on ώe instantaneous value of ώe mput signal is an optimization ώat can be omitted in many embodiments. That is, ώe look-up table 204 and ώe first sealer 208 can be omitted entirely, and ώe signal from ώe digital noise source 206 applied directly (or ώrough ώe second, global sealer 210) to ώe adder/subtracter 212 It will be further recognized ώat ώe use of a zero-mean noise source simplifies ώe illustrated embodiment, but is not essential. A noise signal wiώ another mean value can readily be used, and D C compensation (if needed) can be effected elsewhere m ώe system. The use of a noise source 206 is also optional A vaπety of oώer signal sources can be used, dependmg on application- dependent constraints (e g. ώe ώreshoid at which ώe encoded identification signal becomes perceptible). In many instances, ώe level of ώe embedded identification signal is low enough ώat ώe identification signal needn't have a random aspect; it is imperceptible regardless of its nature. A pseudo random source 206, however, is usually desired because it provides ώe greatest identificauon code signal S/N rauo (a somewhat awkward term in dus instance) for a level of lmpercepnbdity of ώe embedded identification signal. It will be recognized ώat idenufication codmg need not occur after a signal has been reduced to stored form as data (i.e. "fixed in tangible foπn," in ώe words of ώe U S Cσpyπght Act). Consider, for example, ώe case of popular musicians whose performances are often recorded illicidy By identificauon codmg ώe audio before it dπves concert hall speakers, unauthorized recordings of ώe concert can be traced to a panicular place and time. Likewise, live audio sources such as 911 emergency calls can be encoded pπor to recording so as to facilitate ώeir later auώenucauon. While ώe black box embodiment has been descnbed as a stand alone unit, it will be recognized ώat it can be integrated mto a number of different tools/instruments as a component. One is a scanner, which can embed idenuficauon codes in ώe scanned ouφut data. (The codes can simply serve to memonalize ώat ώe data was generated by a panicular scanner). Anoώer is m creauvity software, such as popular drawing/graphics/animation/paint programs offered by Adobe, Macromedia, Corel, and ώe Idee Finally, wlule ώe real-tune encoder 202 has been dlustrated wiώ reference to a panicular hardware unplementauon, it will be recognized ώat a vaπety of oώer implementations can alternatively be employed. Some utilize oώer hardware configurauons. Others make use of software routmes for some or all of ώe dlustrated fuπcuonal blocks. (The software routmes can be executed on any number of different general purpose programmable computers, such as 80x86 PC-compatible computers, RISC-based workstauons, etc.) TYPES OF NOISE. OUASI-NOISE. AND OPTTMIZED-NOISE Heretofore this disclosure postulated Gaussian noise, "white noise," and noise generated directly from application lnstrumentauon as a few of ώe many examples of ώe kind of earner signal appropπate to carry a smgle bit of mformation ώroughout an image or signal. It is possible to be even more proactive in "designing" characteπstics of noise m order to achieve certain goals. The "design" of usmg Gaussian or instrumental noise was aimed somewhat toward "absolute" secuπty This section of ώe disclosure takes a look at oώer considerations for ώe design of ώe noise signals which may be considered ώe ultimate earners of ώe identification mformation For some applications u might be advantageous to design ώe noise earner signal (e.g. ώe Nώ embedded code signal in ώe first embodiment; ώe scaled noise data in ώe second embodiment), so as to provide more absolute signal strengώ to ώe identification signal relative to ώe perceptibility of ώat signal One example is ώe followmg. It is recognized ώat a true Gaussian noise signal has ώe value '0' occur most frequendy, followed by 1 and -1 at equal probabilities to each oώer but lower ώan '0', 2 and -2 next, and so on. Clearly, ώe value zero carπes no mformation as it is used in such an embodiment Thus, one simple adjustment, or design, would be ώat any time a zero occurs in ώe generation of ώe embedded code signal, a new process takes over, whereby ώe value is convened "randomly" to either a 1 or a -1 In logical terms, a decision would be marie: if '0', ώen random(l.-l) The histogram of such a process would appear as a Gaussian/Poissonian type distπbution, except ώat ώe 0 bm would be empty and ώe 1 and -1 bm would be mcreased by half ώe usual histogram value of ώe 0 bm. In this case, identification signal energy would always be applied at all parts of ώe signal. A few of ώe trade-offs include: ώere is a (probably negligible) lowering of secuπty of ώe codes m ώat a "determmisuc component" is a part of generating ώe noise signal. The reason ώis might be completely negligible is ώat we stdl wmd up wiώ a com flip type situation on randomly choosing ώe 1 or ώe - 1 Anoώer trade-off is ώat this type of designed noise will have a higher ώreshold of perceptibility, and will only be applicable to applications where ώe least sigmficant bit of a data stream or image is already negligible relative to ώe commercial value of ώe mateπal, 1 e if ώe least sigmficant bit were stπpped from ώe signal (for ail signal samples), no one would know ώe difference and ώe value of ώe matenal would not suffer. This blocking of ώe zero value m ώe example above is but one of many ways to "optimize" ώe noise properties of ώe signal earner, as anyone in ώe art can realize. We refer to ώis also as "quasi-noise" m ώe sense ώat natural noise can be transformed m a pre-determined way mto signals which for all intents and purposes wdl read as noise. Also, cryptographic meώods and algoπthms can easdy, and often by definition, create signals which are perceived as completely random. Thus ώe word "noise" can have different connotations, primarily between ώat as defined subjectively by an observer or listener, and ώat defined mathematically. The difference of ώe latter is ώat mathematical noise has different properties of secunty and ώe simplicity wiώ which it can eiώer be "sleuthed" or ώe simplicity wiώ which instruments can "automatically recognize" ώe existence of ώis noise. "Universal" Embedded Codes The bulk of ώis disclosure teaches ώat for absolute secuπty, ώe noise-like embedded code signals which carry ώe bits of mformation of ώe identification signal should be umque to each and every encoded signal, or, shghdy less restnctive, ώat embedded code signals should be generated spaπngly, such as usmg ώe same embedded codes for a batch of 1000 pieces of film, for example. Be ώis as it may, ώere is a whole oώer approach to this issue wherem ώe use of what we will call "universal" embedded code signals can open up large new applications for ώis technology. The economics of ώese uses would be such ώat ώe de facto lowered secuπty of ώese umversal codes (e.g. ώey would be analyzable by time honored cryptographic decodmg meώods, and ώus potentially ώwarted or reversed) would be economically negligible relative to ώe economic gains ώat ώe intended uses would provide. Piracy and illegitimate uses would become merely a predictable "cost" and a source of uncollected revenue only; a simple line item m an economic analysis of ώe whole. A good analogy of ώis is m ώe cable industry and ώe scrambling of video signals. Everybody seems to know ώat crafty, skilled technical individuals, who may be generally law abidmg citizens, can climb a ladder and flip a few wires in ώeir cable junction box in order to get all ώe pay channels for free. The cable industry knows ώis and takes active measures to stop it and prosecute ώose caught, but ώe "lost revenue" deπved from ώis practice remains prevalent but aimost negligible as a percentage of profits gained from ώe scrambling system as a whole. The scrambling system as a whole is an economic success despite its lack of "absolute secuπty." The same holds true for applications of diis technology wherem, for ώe pπce of lowering secuπty by some amount, large economic opportunity presents itself. This section first descnbes what is meant by umversal codes, ώen moves on to some of ώe interesting uses to which ώese codes can be applied. Umversal embedded codes generally refer to ώe idea ώat knowledge of ώe exact codes can be distnbuted. The embedded codes won't be put mto a dark safe never to be touched untd litigation aπses (as alluded to in oώer parts of ώis disclosure), but mstead will be distnbuted to vanous locations where on-ώe-spot analysis can take place. Generally ώis distπbution wdl stdl take place widun a secuπty controlled environment, meaning ώat steps wdl be taken to limit ώe knowledge of ώe codes to ώose wiώ a need to know. Instrumentation which attempts to automatically detect copyπghted mateπal is a non-human example of "something" wiώ a need to know ώe codes. There are many ways to implement ώe idea of universal codes, each wiώ ώeir own meπts regarding any given applicanon. For ώe purposes of teaching this an, we separate ώese approaches mto three broad categones: universal codes based on libraries, universal codes based on deterministic formula. and umversal codes based on pre-defined industry standard patterns. A rough rule of thumb is ώat ώe first is more secure ' ώan ώe latter two, but ώat ώe latter two are possibly more economical to implement ώan ώe first. Umversal Codes: 1) Libraπes of Universal Codes The use of libraπes of universal codes sunply means ώat applicant's techmques are employed as descnbed, except for ώe fact ώat only a limited set of ώe individual embedded code signals are generated and ώat any given encoded mateπal will make use of some sub-set of ώis limited "umversal set. " An example is in order here. A photographic pπnt paper manufacturer may wish to pre-expose every piece of 8 by 10 mch pπnt paper which ώey sell wiώ a umque identification code. They also wish to sell identification code recogmtion software to ώeir large customers, service bureaus, stock agencies, and individual photographers, so ώat all ώese people can not only veπfy ώat ώeir own matenal is correc t ly —marked, but so ώat ώey can also detemune if durd party mateπal which ώey are about to acquire has been identified by ώis technology as being copynghted. This latter mformation will help ώem veπfy copyπght holders and avoid litigation, among many oώer benefits. In order to "economically" institute ώis plan, ώey realize that generating umque mdividual embedded codes for each and every piece of pπnt paper would genenue Terabytes of mdependent mformation, which would need storing and to which recogmtion software would need access. Instead, ώey decide to embed ώeir pπnt paper wiώ 16 bit identificauon codes deπved from a set of only 50 mdependent "umversal" embedded code signals. The detads of how dus is done are m ώe next paragraph, but ώe pomt is ώat now ώeir recogmtion software only needs to contain a limited set of embedded codes in ώeir library of codes, typically on ώe order of 1 Megabyte to 10 Megabytes of information for 50x16 mdividual embedded codes splayed out onto an 8x10 photographic pπnt (allowmg for digital compression). The reason for pickmg 50 instead of just 16 is one of a little more added secuπty, where if it were ώe same 16 embedded codes for all photographic sheets, not only would ώe seπal number capability be limited to 2 to ώe 16th power, but lesser and lesser sophisticated pirates could crack ώe codes and remove ώem usmg software tools. There are many different ways to implement ώis scheme, where ώe followmg is but one exemplary meώod. It is determined by ώe wisdom of company management ώat a 300 pixels per mch cπteπa for ώe embedded code signals is sufficient resolution for most applications. This means ώat a composite embedded code image wdl contain 3000 pixels by 2400 pixels to be exposed at a very low level onto each 8x10 sheet. This gives 7.2 million pixels. Usmg our staggered codmg system such as descπbed m ώe black box implementation of Figs. 5 and 6, each mdividual embedded code signal wdl contain only 7.2 million divided by 16, or approximately 450K true mformation carrymg pixels, i.e. every 16th pixel along a given raster line. These values will typically be in ώe range of 2 to -2 in digital numbers, or adequately descπbed by a signed 3 bit number. The raw mformation content of an embedded code is ώen approximately 3/8ώ's bytes tunes 450K or about 170 Kilobytes. Digital compression can reduce dus further. All of ώese decisions are subject to standard engineering optimization principles as defined by any given applicauon at hand, as is well known in ώe art. Thus we find ώat 50 of ώese mdependent embedded codes will amount to a few Megabytes. This is quite reasonable level to distnbute as a "library" of umversal codes withm ώe recogniuon software. Advanced standard encryption devices could be employed to mask ώe exact nature of ώese codes if one were concerned ώat would-be pirates would buy ώe recogniuon software merely to reverse engineer ώe umversal embedded codes. The recogmtion software could sunply unencrypt ώe codes pπor to applying ώe recogmtion techmques taught ra ώis disclosure. The recogmtion software itself would certainly have a vaπety of features, but ώe core task it would perform is determining if ώere is some umversal copyπght code wiώm a given image. The key questions become WHICH 16 of ώe total 50 umversal codes it might contain, if any, and if ώere are 16 found, what are ώeir bit values. The key vanables in determining ώe answers to ώese questions are: registration, rotation, magnification (scale), and extent. In ώe most general case wiώ no helpful hints whatsoever, all variables must be independendy varied across all mutual combinations, and each of ώe 50 universal codes must ώen be checked by adding and subtracting to see if an entropy decrease occurs. Strictiy speaking, dus is an enormous job, but many helpful hints will be found which make ώe job much simpler, such as havmg an onginal image to compare to ώe suspected copy, or knowing ώe general oπentation and extent of ώe image relative to an 8x10 pπnt paper, which ώen ώrough simple registration techmques can detemune all of ώe vaπabies to some acceptable degree. Then it merely requires cyclmg ώrough ώe 50 universal codes to find any decrease m entropy. If one does, ώen 15 others should as well. A protocol needs to be set up whereby a given order of ώe 50 translates into a sequence of most significant bit ώrough least sigmficant bit of ώe ID code word. Thus if we find ώat umversal code number "4" is present, and we find its bit value to be "0", and ώat universal codes "1 " dirough "3" are definitely not present, ώen our most significant bit of our N-bit ID code number is a "0". Likewise, we find ώat ώe next lowest umversal code present is number "7" and it turns out to be a "1", ώen our next most significant bit is a "1". Done properly, ώis system can cleanly trace back to ώe copyright owner so long as ώey registered ώeir photographic paper stock seπal number wiώ some registry or wiώ ώe manufacturer of ώe paper itself. That is, we look up m ώe registry ώat a paper usmg universal embedded codes 4,7,11,12,15,19,21,26,27,28,34,35,37,38,40, and 48, and having ώe embedded code 0110 0101 0111 0100 belongs to Leonardo de Boticelli, an unknown wddlife photographer and glacier cmematographer whose address is in Northern Canada. We know diis because he dutifully registered his film and paper stock, a few minutes of work when he bought ώe stock, which he plopped mto ώe "no postage necessary" envelope ώat ώe manufacturing company kindly provided to make ώe process ridiculously simple. Somebody owes Leonardo a royalty check it would appear, and certainly ώe registry has automated ώis royalty payment process as pan of its services. One final pomt is ώat truly sophisticated pirates and others wiώ illicit intentions can indeed employ a variety of cryptographic and not so cryptographic meώods to crack ώese umversal codes, sell ώem, and make software and hardware tools which can assist in ώe removing or distorting of codes. We shall not teach ώese meώods as pan of ώis disclosure, however. In any event, ώis is one of ώe pπces which must be paid for ώe ease of umversal codes and ώe applications ώey open up. Uπiversal Codes: 2) Umversal Codes Based on Deterministic Formulas The libraπes of umversal codes require ώe storage and transmittal of Megabytes of mdependent, generally random data as ώe keys wiώ which to unlock ώe existence and identity of signals and imagery ώat have been marked wiώ umversal codes. Alternatively, vanous deterministic formulas can be used which "generate" what appear to be random data/image frames, ώereby obviatmg ώe need to store all of ώese codes m memory and interrogate each and of ώe "50" umversal codes. Deterministic formulas can also assist in speeding up ώe process of determining ώe ID code once one is known to exist in a given signal or image. On ώe oώer hand, determmisuc formulas lend ώemselves to sleuthing by less sophisticated pirates. And once sieuώed, ώey lend themselves to easier communication, such as postmg on ώe Internet to a hundred newsgroups. There may well be many applications which do not care about sleuthing and publishing, and deterministic formulas for generating ώe mdividual umversal embedded codes might be just ώe ticket. Umversal Codes: 3) "Simple" Umversal Codes This category is a bit of a hybπd of ώe first two, and is most directed at truly large scale lmplementauons of ώe principles of this technology The applications employmg this class are of ώe type where staunch secuπty is much less important ώan low cost, large scale implementation and ώe vastly larger economic benefits ώat this enables. One exemplary application is placement of identification recogmtion umts direcdy widun modesdy pπced home audio and video instrumentation (such as a TV). Such recogmtion umts would typically momtor audio and/or video lookmg for ώese copynght identification codes, and ώence tπggeπng simple decisions based on ώe findings, such as disabling or enabling recording capabdities, or incrementing program specific billing meters which are transmitted back to a central audio/video service provider and placed onto monώly mvoices. Likewise, it can be foreseen ώat "black boxes" m bars and oώer public places can momtor (listen wiώ a microphone) for copyπghted mateπals and generate detaded reports, for use by ASCAP, BMI, and ώe like. A core principle of simple umversal codes is ώat some basic industry standard "noiselike" and seamlessly repeuuve patterns are injected mto signals, images, and image sequences so ώat inexpensive recogmtion umts can eiώer A) determine ώe mere existence of a copynght "flag", and B) additionally to A, detemune precise identificauon informauon which can facilitate more complex decision making and actions. In order to implement ώis particular embodiment, ώe basic principles of generating ώe mdividual embedded noise signals need to be simplified in order to accommodate inexpensive recogmtion signal processing circuitry, whde maintaining ώe properties of effective randomness and holographic permeation. Wiώ large scale industry adoption of ώese simple codes, ώe codes ώemselves would border on public domam mformation (much as cable scramblmg boxes are almost de facto public domam), leavmg ώe door open for determmed pirates to develop black market counteπneasures, but ώis situation would be quite analogous to ώe scramblmg of cable video and ώe objective economic analysis of such illegal activity One pnor an known to ώe applicant m ώis general area of pro-active copynght detection is ώe Senal Copy Management System adopted by many firms m ώe audio mdustry To ώe best of applicant's knowledge, ώis system employs a non-audio "flag" signal which is not pan of ώe audio data stream, but which is nevertheless grafted onto ώe audio stream and can indicate wheώer ώe associated audio data should or should not be duplicated One problem wiώ this system is ώat it is restncted to media and instrumentation which can support ώis extra "flag" signal. Anoώer deficiency is ώat ώe flagging system cames no identity mformation which would be useful m making more complex decisions. Yet anoώer difficulty is ώat high quality audio sampling of an analog signal can come arbitrarily close to making a perfect digital copy of some digital master and ώere seems to be no provision for inhibiting this possibility Applicant's technology can be brought to bear on ώese and oώer problems, in audio applications, video, and all of ώe oώer applications previously discussed. An exemplary apphcation of simple umversal codes is ώe followmg A single mdustry standard "1.000000 second of noise" would be defined as ώe most basic indicator of ώe presence or absence of ώe copynght marking of any given audio signal Fig 9 has an example of what ώe waveform of an mdustry standard noise second might look like, boώ in ώe tune domain 400 and ώe frequency domam 402. It is by definition a continuous function and would adapt to any combmauon of sampling rates and bit quantizauons. It has a normalized amplitude and can be scaled arbitraπly to any digital signal amplitude. The signal level and ώe first M'ώ deπvatives of ώe signal are conunuous at ώe two boundanes 404 (Fig. 9C), such ώat when it is repeated, ώe "break" in ώe signal would not be visible (as a waveform) or audible when played ώrough a high end audio system. The choice of I second is arbitrary m ώis example, where ώe precise length of ώe interval will be deπved from considerauons such as audibility, quasi-white noise status, seamless repeatabdity, simplicity of recogniuon processing, and speed wiώ which a copynght marking determination can be made. The injection of ώis repeated noise signal onto a signal or image (again, at levels below human perception) would indicate ώe presence of copynght matenal. This is essentially a one bit identification code, and ώe embeddmg of further idenufication mformation wdl be discussed later on in ώis section. The use of ώis identification technique can extend far beyond ώe low cost home implementations discussed here, where studios could use ώe techmque, and momtoπng stations could be set up which literally momtor hundreds of channels of mformation simultaneously, searching for marked data streams, and furthermore searching for ώe associated identity codes which could be tied in wiώ bdlmg networks and royalty tracking systems. This basic, standardized noise signature is seamlessly repeated over and over again and added to audio signals which are to be marked wiώ ώe base copynght identification. Part of ώe reason for ώe word "simple" is seen here: clearly pirates will know about ώis mdustry standard signal, but ώeir illicit uses derived from ώis knowledge, such as erasure or corruption, will be economically minuscule relative to ώe economic value of ώe overall technique to ώe mass market. For most high end audio ώis signal will be some 80 to 100 dB down from full scale, or even much further; each situation can choose its own levels ώough certainly ώere will be recommendations. The amplitude of ώe signal can be modulated according to ώe audio signal levels to which ώe noise signature is being applied, i.e. ώe amplitude can increase significantly when a drum beats, but not so dramatically as to become audible or objectionable. These measures merely assist ώe recognition circuitry to be described. Recognition of ώe presence of diis noise signature by low cost instrumentation can be effected in a variety of ways. One rests on basic modifications to ώe simple principles of audio signal power metering. Software recognition programs can also be written, and more sophisticated mathematical detection algorithms can be applied to audio in order to make higher confidence detection identifications. In such embodiments, detection of ώe copyright noise signature involves comparing ώe time averaged power level of an audio signal wiώ ώe time averaged power level of ώat same audio signal which has had ώe noise signature subtracted from it. If ώe audio signal wiώ ώe noise signature subtracted has a lower power level ώat ώe unchanged audio signal, ώen ώe copyright signature is present and some status flag to ώat effect needs to be set. The main engineering subtleties involved in making ώis comparison include: dealing wiώ audio speed playback discrepancies (e.g. an instrument might be 0.5% "slow" relative to exactly one second intervals); and, dealing wiώ ώe unknown phase of ώe one second noise signature within any given audio (basically, its "phase" can be anywhere from 0 to 1 seconds). Anoώer subdety, not so central as ώe above two but which noneώeless should be addressed, is ώat ώe recognition circuits should not subtract a higher amplitude of ώe noise signature ώan was originally embedded onto ώe audio signal. Fortunately ώis can be accomplished by merely subtracting only a small amplitude of ώe noise signal, and if ώe power level goes down, ώis is an indication of "heading toward a trough" in ώe power levels. Yet anoώer related subdety is ώat ώe power level changes wdl be very small relative to ώe overall power levels, and calculations generally will need to be done wiώ appropriate bit precision, e.g. 32 bit value operations and accumulations on 16-20 bit audio in ώe calculations of time averaged power levels. Clearly, designing and packaging ώis power level comparison processing circuitry for low cost applications is an engineering optimization task. One trade-off will be ώe accuracy of making an identification relative to ώe "short-cuts" which can be made to ώe circuitry in order to lower its cost and complexity. One embodiment for placing ώis recognition circuitry inside of instrumentation is ώrough a single programmable integrated circuit which is custom made for ώe task. Fig. 10 shows one such integrated circuit 506. Here ώe audio signal comes in, 500, eiώer as a digital signal or as an analog signal to be digitized inside ώe IC 500, and ώe ouφut is a flag 502 which is set to one level if ώe copyright noise signature is found, and to anoώer level if it is not found. Also depicted is ώe fact ώat ώe standardized noise signature waveform is stored in Read Only Memory, 504, inside ώe IC 506. There will be a slight time delay between ώe application of an audio signal to ώe IC 506 and ώe ouφut of a valid flag 502, due to ώe need to momtor some finite poπion of ώe audio before a recognition can place. In ώis case, ώere may need to be a "flag valid" ouφut 508 where ώe IC informs ώe external world if it has had enough tune to make a proper determination of ώe presence or absence of ώe copynght noise signature. There are a wide vanety of specific designs and phdosophies of designs applied to accomplishmg ώe basic function of ώe IC 506 of Fig. 10. Audio engineers and digital signal processing engineers are able to generate several fundamentally different designs. One such design is depicted m Fig. 11 by a process 599, which itself is subject to further engineering optimization as wdl be discussed. Fig. 11 depicts a flow chart for any of: an analog signal processing network, a digital signal processing network, or programming steps in a software program. We find an mput signal 600 which along one paώ is applied to a time averaged power meter 602, and ώe resulting power ouφut itself treated as a signal P„,. To ώe upper nght we find ώe standard noise signature 504 which will be read out at 125% of normal speed, 604, ώus changmg its pitch, givmg ώe "pitch changed noise signal" 606. Then ώe mput signal has ώis pitch changed noise signal subtracted in step 608, and ώis new signal is applied to ώe same form of time averaged power meter as m 602, here labelled 610. The ouφut of this operation is also a time based signal here labelled as P,.^., 610. Step 612 ώen subtracts ώe power signal 602 from ώe power signal 610, givmg an ouφut difference signal P^, 613. If ώe universal standard noise signature does mdeed exist on ώe mput audio signal 600, ώen case 2, 616, will be created wherem a beat signal 618 of approximately 4 second penod wdl show up on ώe ouφut signal 613, and it remains to detect ώis beat signal wiώ a step such as in Fig. 12, 622. Case 1, 614, is a steady noisy signal which exhibits no peπodic beating. 125% at step 604 is chosen arbitrarily here, where engineering considerations would determine an optimal value, leading to different beat signal frequencies 618. Whereas waiting 4 seconds in dus example would be qmte a while, especially is you would want to detect at least two or diree beats. Fig. 12 outlines how ώe basic design of Fig. 11 could be repeated and operated upon vanous delayed versions of ώe mput signal, delayed by something Idee l/20ώ of a second, wiώ 20 parallel circuits working m concert each on a segment of ώe audio delayed by 0.05 seconds from ώeir neighbors. In ώis way, a beat signal will show up approximately every l/5ώ of a second and will look like a travelling wave down ώe columns of beat detection circuits. The existence or absence of dus travellmg beat wave tπggers ώe detection flag 502. Meanwhile, ώere would be an audio signal momtor 624 which would ensure ώat, for example, at least two seconds of audio has been heard before setting ώe flag valid signal 508. Though ώe audio example was descπbed above, it should be clear to anyone in ώe art ώat ώe same type of definition of some repeutive universal noise signal or image could be applied to ώe many oώer signals, unages, pictures, and physical media already discussed. The above case deals only wiώ a single bit plane of mformation, i.e , ώe noise signature signal is eiώer ώere (1) or it isn't (0). For many applications, it would be nice to detect seπal number mformation as well, which could ώen be used for more complex decisions, or for loggmg mformation on billing statements or whatnot. The same principles as ώe above would apply, but now ώere would be N independent noise signatures as depicted in Fig. 9 mstead one smgle such signature. Typically, one such signature would be ώe master upon which ώe mere existence of a copynght marking is detected, and ώis would have generally higher power ώan ώe others, and ώen ώe oώer lower power "identification ' noise signatures would be embedded mto audio. Recogmtion circuits, once havmg found ώe existence of ώe primary noise signature, would ώen step ώrough ώe oώer N noise signatures applying ώe same steps as descπbed above. Where a beat signal is detected, ώus mdieates ώe bit value of T, and where no beat signal is detected, ώis mdieates a bit value of '0' It might be typical ώat N will equal 32, ώat way 2 n number of identificauon codes are available to any given mdustry employing this technology. Use of ώis Technology When ώe Length of ώe Identification Code is 1 The principles descnbed herein can obviously be applied in ώe case where only a smgle presence or absence of an idenufication signal - a fingerprint if you wdl - is used to provide confidence ώat some signal or image is copynghted. The example above of ώe mdustry standard noise signature is one case in pomt. We no longer have ώe added confidence of ώe com flip analogy, we no longer have tracking code capabdities or basic seπal number capabdities, but many applications may not require ώese attributes and ώe added simplicity of a smgle fingerprint might outweigh ώese oώer attributes in any event. The "Wallpaper" Analogy The term "holographic" has been used in ώis disclosure to descπbe how an identification code number is distnbuted in a largely integral foπn ώroughout an encoded signal or image This also refers to ώe idea ώat any given fragment of ώe signal or image contams ώe entire umque identification code number. As wiώ physical implementations of holography, ώere are limitations on how small a fragment can become before one begins to lose this property, where ώe resolution limits of ώe holographic media are ώe mam factor in ώis regard for holography itself. In ώe case of an uncorrupted distπbuuon signal which has used ώe encodmg device of Fig. 5, and which furthermore has used our "designed noise" of above wherem ώe zero's were randomly changed to a 1 or -1, ώen ώe extent of ώe fragment required is merely N conuguous samples m a signal or image raster Ime, where N is as defined previously bemg ώe length of our idenufication code number. This is an mformational extreme; practical situations where noise and corruption are operative will require generally one, two or higher orders of magnitude more samples ώan ώis simple number N Those slulled m ώe art will recognize ώat ώere are many vaπabies involved m pinning down precise statistics on ώe size of ώe smallest fragment wiώ which an identification can be made. For tutoπal purposes, ώe applicant also uses ώe analogy ώat ώe unique identification code number is "wallpapered" across and image (or signal). That is, it is repeated over and over again all ώroughout an image. This repetition of ώe ID code number can be regular, as in ώe use of ώe encoder of Fig. 5, or random itself, where ώe bits m ώe ID code 216 of Fig. 6 are not stepped ώrough m a normal repetitive fashion but raώer are randomly selected on each sample, and ώe random selection stored along wiώ ώe value of ώe ouφut 228 itself, in any event, ώe information earner of ώe ID code, ώe mdividual embedded code signal, does change across ώe image or signal. Thus as ώe wallpaper analogy summarizes: ώe ID code repeats itself over and over, but ώe patterns ώat each repetition unpππts change randomly accordingly to a generally unsleuώable key. Lossy Data Compression As earlier mentioned, applicant's prefened forms of identification coding wiώstand lossy data compression, and subsequent decompression. Such compression is finding increasing use, particularly m contexts such as ώe mass distπbutton of digitized entertainment programming (movies, etc.). Wlule data encoded accordmg to ώe disclosed techmques can wiώstand all types of lossy compression known to applicant, ώose expected to be most commercially important are ώe CCITT G3 , CCITT G4, JPEG, MPEG and JBIG compression/decompression standards. The CCITT standards are widely used in black-and-white document compression (e.g. facsimile and document-storage). JPEG is most widely used wiώ still images. MPEG is most widely used wiώ movmg images. JBIG is a likely successor to ώe CCITT standards for use wiώ black-and-white imagery. Such techmques are well known to ώose in ώe lossy data compression field; a good overview can be found in Penπebaker et al, JPEG, Sail Image Data Compression Standard, Van Nostrand Rerahold, N.Y., 1993. Towards Steganography Proper and ώe Use of ώis Technology in Passing More Complex Messages or Information This disclosure concentrates on what above was called wallpapering a smgle identification code across an entire signal. This appears to be a desirable feature for many applications. However, ώere are oώer applicauons where it might be desirable to pass messages or to embed very long strings of pertinent identificauon information m signals and unages. One of many such possible applicauons would be where a given signal or image is meant to be manipulated by several different groups, and ώat certain regions of an image are reserved for each group's identificauon and insertion of peπment manipulation mformation. In ώese cases, ώe code word 216 in Fig. 6 can actually change in some pre-defined manner as a function of signal or image position. For example, in an image, ώe code could change for each and every raster Ime of ώe digital image It might be a 16 bit code word, 216, but each scan Ime would have a new code word, and ώus a 480 scan Ime image could pass a 980 (480 x 2 bytes) byte message A receiver of the message would need to have access to eiώer ώe noise signal stored m memory 214, or would have to know ώe universal code structure of ώe noise codes if ώat meώod of codmg was being used To ώe bes t of applicant's knowledge, ώis is a novel approach to ώe mature field of steganography. In all ώree of ώe foregomg applicauons of universal codes, it will often be desirable to append a short (perhaps 8- or 16-bit) pnvate code, which users would keep in ώeir own secured places, m addition to ώe umversal code. This affords ώe user a further modicum of secuπty against potential erasure of ώe universal codes by sophisticated pirates. Applicant's Prior Application The Detailed Descnption to dus pomt has simply repeated ώe disclosure of applicant's pπor mtemational application, laid open as PCT publication WO 95/14289 It was reproduced above simply to provide context for ώe disclosure which follows. One Master Code Signal As A Distinction From N Independent Embedded Code Signals In certain sections of dus disclosure, perhaps exemplified in ώe section on ώe realtime encoder, an economizing step was taken whereby ώe N mdependent and source-signal-coextensive embedded code signals were so designed ώat ώe non-zero elements of any given embedded code signal were umque to just diat embedded code signal and no oώers. Said more carefully, ceπam pixels/sample pomts of a given signal were "assigned" to some pre-determined m'ώ bit location in our N-bit identificauon word Furthermore, and as anoώer basic optimization of implementation, ώe aggregate of ώese assigned pixels/samples across all N embedded code signals is precisely ώe extent of ώe source signal, meaning each and every pixel/sample location m a source signal is assigned one and only one m'ώ bit place m our N-btt idenuficauon word. (This is not to say, however, ώat each and every pixel MUST be modified). As a matter of simpiifkauon we can ώen talk about a smgle master code signal (or "Snowy Image") raώer ώan N mdependent signals, realizing ώat pre-defined locations in this master signal conespond to umque bit locauons in our N-bit identification word. We therefore construct, via diis circuitous route, ώis raώer simple concept on ώe smgle master noise signal. Beyond mere economization and simplification, ώere are also performance reasons for this shift, primarily denved from ώe idea ώat mdividual bit places m our N-bit idenuficauon word are no longer "competing" for ώe mformation carrymg capacity of a smgle pixel/sample. Wiώ ώis smgle master more clearly understood, we can gam new insights mto oώer sections of this disclosure and explore further details withm ώe given application areas. SUBSTITUTE " SHEET (RULE 26) More of Deterministic Umversal Codes Using ώe Master Code Concent One case m pomt is to further explore ώe use of Determmisuc Umversal Codes, labelled as item "2" in ώe sections devoted to umversal codes. A given user of this technology may opt for ώe followmg variant use of ώe pnnciples of ώis technology The user in question might be a mass distnbutor of home videos, -but clearly ώe pnnciples would extend to all oώer potential users of ώis technology Fig. 13 pictoπally represents ώe steps mvoived. In ώe example ώe user is one "Alien Productions." They first create an unage canvas which is coextensive to ώe size of ώe video frames of then- movie "Bud's Adventures. " On ώis canvas ώey pπnt ώe name of ώe movie, ώey place ώeir logo and company name. Fuπheπnore, they have specific informauon at ώe bottom, such as ώe distπbution lot for ώe mass copying ώat ώey are cunently cranking out, and as indicated, ώey actually have a umque frame number mdicated. Thus we find ώe example of a standard unage 700 which forms ώe initial basis for ώe creation of a master Snowy Image (master code signal) which wdl be added mto ώe onginal movie frame, creating an ouφut distπbutable frame. This unage 700 can be eiώer black & white or color. The process of turning this image 700 mto a pseudo random master code signal is alluded to by ώe encryption/scrambling routine 702, wherem ώe onginal unage 700 is passed ώrough any of dozens of well known scramblmg meώods. The depicuon of ώe number "28" alludes to ώe idea ώat ώere can actually be a library of scramblmg meώods. and ώe particular meώod used for dus particular movie, or even for this particular frame, can change The result is our classic master code signal or Snowy Image. In general, its bnghtness values are large and it would look very much like ώe snowy image on a television set tuned to a blank channel, but clearly it has been deπved from an infoπnauve unage 700, transformed through a scramblmg 702. (Note: ώe splotchmess of ώe example picture is actually a raώer poor depiction; it was a function of ώe erode tools avadable to ώe inventor). This Master Snowy Image 704 is ώen ώe signal which is modulated by our N-bit identification word as oudined in oώer secuons of ώe disclosure, ώe resultmg modulated signal is ώen scaled down m bnghtness to ώe acceptable perceived noise level, and ώen added to ώe onginal frame to produce ώe distπbutable frame. There are a vaπety of advantages and features ώat ώe meώod depicted in Fig. 13 affords. There are also vaπauons of theme withm this overall vaπation. Clearly, one advantage is ώat users can now use more intuitive and personalized meώods for stamping and signing ώeir work. Provided ώat ώe encryption/scrambling routines, 702, are mdeed of a high secuπty and not published or leaked, ώen even if a would-be pirate has knowledge of ώe logo unage 700, ώey should not be able to use ώis knowledge to be able to sleuth ώe Master Snowy Image 704, and ώus ώey should not be able to crack ώe system, as it were. On ώe oώer hand, simple encryption routines 702 may open ώe door for cracking ώe system. Anoώer clear advantage of ώe meώod of Fig 13 is ώe ability to place further mformation into ώe overall protecuve process. Stnctiy speaking, ώe mformation contained in ώe logo image 700 is not direcdy earned in ώe final distπbutable frame. Said another way, and provided ώat ώe encrypuon/scrambling routine 702 has a straightforward and known deciyption/descrambling meώod which tolerates bit trunca t ion enors, it is generally impossible to fully re-create ώe image 700 based upon havmg ώe distπbutable frame, ώe N-bit idenuficauon code word, ώe bnghtness scaling factor used, and ώe number of ώe decryption routine to be used. The reason diat an exact recreation of ώe image 700 is impossible is due to ώe scaling operation itself and ώe concomitant bit truncation. For ώe present discussion, ώis whole issue is somewhat academic, however. A vaπation on ώe ώeme of Fig. 13 is to actually place ώe N-bit idenuficauon code word direcdy mto ώe logo image 700. In some sense ώis would be self-referential. Thus when we pull out our stored logo image 700 it already contams visually what our identification word is, ώen we apply encryption routme #28 to ώis image, scale it down, ώen use ώis version to decode a suspect unage usmg ώe techmques of ώis disclosure The N bit word ώus found should match ώe one contained in our logo image 700. One desirable feature of ώe encryption/scrambling routme 702 might be fbut is certainly not required to be) ώat even given a small change m ώe mput image 700, such as a smgle digit change of ώe frame number, ώere would be a huge visual change m ώe ouφut scrambled master snowy image 704 Likewise, ώe actual scramblmg routme may change as a function of frame numbers, or certain "seed" numbers typically used widun pseudo-randomizing functions could change as a function of frame number All manner of vanations are ώus possible, all helpmg to maintain high levels of secuπty Eventually, engineering optmuzauon considerations will begin to investigate ώe relationship between some of ώese randomizing meώods, and how ώey all relate to maintaining acceptable signal strengώ levels through ώe process of transforming an uncompressed video stream mto a compressed video stream such as wiώ ώe MPEG compression methodologies Anoώer desired feature of ώe encryption process 702 is ώat it should be mformationally efficient, i.e., ώat given any random mput, it should be able to ouφut an essentially spatially uniform noisy image wiώ little to no residual spatial patterns beyond pure randomness. Any residual conelated patterns will contπbute to mefficiency of encodmg ώe N-bit identification word, as well as opening up further tools to would-be pirates to break ώe system. Anoώer feature of ώe meώod of Fig. 13 is ώat ώere is more intuitional appeal to usmg recognizable symbols as part of a decoding system, which should ώen translate favorably in ώe essentially lay environment of a courtroom. It strengthens ώe simplicity of ώe com flip vernacular mentioned elsewhere. Jury members or judges wdl better relate to an owner's logo as being a piece of ώe key of recognizing a suspect copy as being a knock-off. It should also be mentioned ώat, stπctly speaking, ώe logo image 700 does not need to be randomized. The steps could equally apply straight to ώe logo image 700 directly It is not entirely clear to the inventor what practical goal ώis might have. A tnvial extension of ώis concept to ώe case where N = 1 is where, simply and easily, ώe logo image 700 is merely added to an oπgmal image at a very low bnghtness level. The inventor does not presume ώis tnvial case to be at all a novelty. In many ways ώis is similar to ώe age old issue of subliminal advemsing, where ώe low light level patterns added to an image are recognizable to ώe human eye/brain system and - supposedly - operaimg on ώe human bram at an unconscious level. By pointing out ώese tnvial extensions of ώe cunent technology, hopefully there can aπse further claπty which distmguishes applicant's novel pnnciples m relauon to such well known pπor an techniques. 5-bit Abπdged Alphanumenc Code Sets and Oώers It is desirable in some applications for ώe N-bit idenuficauon word to actually signify names, companies, strange words, messages, and ώe like. Most of this disclosure focuses on using ώe N-bit identificauon word merely for high statistical secuπty, indexed tracking codes, and oώer index based message carrying. The informauon carrying capacity of "invisible signatures" inside imagery and audio is somewhat limited, however, and ώus it would be wise to use our N bits efficieπdy if we actually want to "spell out" alphanumenc items in ώe N-bit idenuficauon word. One way to do dus is to define, or to use an already existing, reduced bit (e.g. less ώan 8-bit ASCII) standardized codes for passing alphanumenc messages. This can help to satisfy ώis need on ώe pan of some applicauons. For example, a simple alphanumenc code could be budt on a 5-bit mdex table, where for example ώe letters V,X,Q, and Z are not mcluded, but ώe digits 0 ώrough 9 arc mcluded. In ώis way, a 100 bit identificauon word could carry wiώ it 20 alphanumenc symbols. Anoώer alternative is to use variable bit length codes such as ώe ones used m text compression routines (e.g. Huffman) whereby more frequendy used symbols have shorter bit length codes and less frequendy used symbols have longer bit lengths. More on Detecting and Recognizing ώe N-bit Identification Word in Suspect Signals Classically speaking, ώe detection of ώe N-bit identificauon word fits nicely mto ώe old art of detectmg known signals in noise. Noise in ώis last statement can be inteφreted very broadly, even to ώe pomt where an usage or audio track itself can be considered noise, relative to ώe need to detect ώe underlying signature signals. One of many references to ώis older art is ώe book Kassam, Saleem A., "Signal Detection in Non-Gaussian Noise," Springer- Veriag, 1988 (generally available at well stocked libraπes, e.g. avadable at ώe U.S. Library of Congress by catalog number TK5102.5 .K357 1988, To ώe best of ώis inventor's current understanding, none of ώe material in ώis book is directly applicable to ώe issue of discovering ώe polaπty of applicant's embedded signals, but ώe broader pnnciples are mdeed applicable. In particular, section 1.2 "Basic Concepts of Hypoώesis Testing" of Kassam's book lays out ώe basic concept of a bmary hypoώesis, assigning ώe value "1" to one hypoώesis and ώe value "0" to ώe —oώer hypoώesis. The last paragraph of ώat section is also on pomt regarding ώe earher-descπbed embodiment, i.e., ώat ώe "0" hypoώesis corresponds to "noise only" case, whereas ώe "1" conesponds to ώe presence of a signal in ώe observations. Applicant's use of true polanry is not like ώis, however, where now ώe "0 * conesponds to ώe presence of an inverted signal raώer ώan to "noise-only " Also m ώe present embodiment, ώe case of "noise-only" is effectively ignored, and ώat an identification process will eiώer come up wiώ our N-bit idenuficauon word or it will come up wiώ "garbage." The continued and inevitable engineering improvement m ώe detection of embedded code signals wdl undoubtedly bonow heavdy from ώis genenc field of known signal detection. A common and well-known technique m ώis field is ώe so-called "matched filter," which is incidentally discussed early in section 2 of ώe Kassam book. Many basic texts on signal processing include discussions on ώis meώod of signal detecuon. This is also known m some fields as correlation detection. Furthermore, when ώe phase or location of a known signal is known a pnon, such as is often ώe case m applicauons of ώis technology, ώen ώe matched filter can often be reduced to a simple vector dot product between a suspect image and ώe embedded signal associated wiώ an m'ώ bit plane m our N-bit idenuficauon word. This ώen represents yet anoώer simple "detection algonώm" for taking a suspect image and producmg a sequence of Is and 0s wiώ ώe mtenuon of determining if ώat senes corresponds to a pre-embedded N-bit idenuficauon word. In words, and wiώ reference to Fig. 3, we run ώrough ώe process steps up through and mcludmg ώe subtracting of ώe onginal unage from ώe suspect, but ώe next step is merely to step ώrough all N random mdependent signals and perform a simple vector dot product between ώese signals and ώe difference signal, and if diat dot product is negative, assign a '0' and if dial dot product is positive, assign a ' 1.' Careful analysis of dus "one of many" algoπthms will show its siπulaπty to ώe traditional matched filter. There are also some immediate improvements to ώe "matched filter" and "conelation-type" ώat can provide enhanced abdity to properly detect very low level embedded code signals. Some of ώese improvements are denved from pnnciples set forth m ώe Kassam book, oώers are generated by ώe mventor and ώe mventor has no knowledge of ώeir bemg developed m oώer papers or works, but neiώer has ώe mventor done fully extensive searching for advanced signal detection techniques. One such technique is perhaps best exemplified by figure 3.5 in Kassam on page 79, wherem ώere are certain plots of ώe vanous locally optimum weighting coefficients which can apply to a general dot-product algoπώmic approach to detecuon. In oώer words, raώer ώan performing a simple dot product, each elemental multiplication operation m an overall dot product can be weighted based upon known a priori statistical mfoπnation about ώe difference signal itself, 1 e., the signal wiώm which ώe low level known signals are being sought. The interested reader who is not already familiar wiώ ώese topics is encouraged to read chapter 3 of Kassam to gain a fuller understanding. One principle which did not seem to be explicitly present in ώe Kassam book and which was developed rudimeπtaπly by ώe mventor mvoives ώe exploitation of ώe magnitudes of ώe statistical propemes of ώe known signal bemg sought relative to ώe magnitude of ώe statistical propemes of ώe suspect signal as a whole. In panicular, ώe problematic case seems to be where ώe embedded signals we are lookmg for are of much lower level ώan ώe noise and corruption present on a difference signal. Fig 14 attempts to set ώe stage for ώe reasoning behind ώis approach. The top figure 720 contains a genenc look at ώe differences m ώe histograms between a typical "problematic" difference signal, i.e., a difference signal which has a much higher overall energy ώan ώe embedded signals ώat may or may not be within it. The term "mean-removed" simply means ώat ώe means of boώ ώe difference signal and ώe embedded code signal have been removed, a common operation pπor to performing a normalized dot product. The lower figure 722 ώen has a generally similar histogram plot of ώe deπvatives of ώe two signals, or in ώe case of an unage, ώe scalar gradients. From pure mspection it can be seen ώat a simple thresholding operation in ώe deπvative transform domam, wiώ a subsequent conversion back mto ώe signal domam, will go a long way toward removmg certain innate biases on ώe dot product "recogmtion algoπthm" of a few paragraphs back. Thresholding here refers to ώe idea ώat if ώe absolute value of a difference signal deπvauve value exceeds some ώreshold, ώen it is replaced simply by ώat threshold value. The threshold value can be so chosen to contain most of ώe histogram of ώe embedded signal. Anoώer operation which can be of minor assistance in "alleviating" some of ώe bias effects m ώe dot product algoπthm is ώe removal of ώe low order frequencies m ώe difference signal, I e , running ώe difference signal ώrough a high pass filter, where ώe cutoff frequency for ώe high pass filter is relatively near ώe ongin (or DC) frequency. Special Consideranons for Recognizing Embedded Codes on Signals Which Have Beeri Compressed and Decompressed, or Alternatively, for Recognizing Embedded Codes Wiώm Anv Signal Which Has Undergone Some Known Process Which Creates Non-Umfoπn Enor Sources Long tide for a basic concept. Some signal processmg operations, such as compressing and decompressmg an image, as wiώ ώe JPEG/MPEG formats of image/video compression, create enors m some given transform domain which have certain conelations and structure. Usmg JPEG as an example, if a given unage is compressed ώen decompressed at some high compression raiio, and ώat resultmg image is ώen founer transformed and compared to ώe fouπer transform of ώe oπgmal uncompressed image, a definite pattern is clearly visible. This patterning is indicative of coπelated enor, I e enor which can be to some extent quantified and predicted. The prediction of ώe grosser properties of ώis conelated enor can ώen be used to advantage in ώe heretofore -discussed meώods of recognizing t he embedded code signals widun some suspect image which may have undergone eiώer JPEG compression or any oώer operation which leaves ώese telltale conelated enor signatures The basic idea is ώa t in areas where ώere are known higher levels of enor, ώe value of ώe recogmtion meώods is diminished rela t ive t o ώe areas wiώ known lower levels of conelated enors It is often possible to quantify ώe expec t ed levels of enor and use ώis quantification to appropπately weight ώe retransformed signal values Using JPEG compression again as an example, a suspect signal can be fouπer transformed, and ώe fouπer space representation may clearly show ώe telltale box gπd pattern. The fouπer space signal can ώen be "spatially filtered" near ώe gπd pomts, and ώis filtered representation can ώen be transformed back m t o its regular time or space domam to ώen be run ώrough ώe recogmtion meώods presented in ώis disclosure. Likewise, any signal processing meώod which creates non-uniform enor sources can be transformed mto ώe domain in which ώese error sources are non-uniform, ώe values at ώe high pomts of ώe enor sources can be attenuated, and ώe ώusly "filtered" signal can be transformed back mto ώe time/space domam for standard recogmtion. Often ώis whole process wdl include ώe lengώy and arduous step of "characterizing" ώe typical conelated enor behavior in order to "design" ώe appropnate filtering profiles "SIGNATURE CODES" and "INVISIBLE SIGNATURES" Bnefly and for ώe sake of claπty, ώe phrases and terms "signatures," "invisible signatures," and "signature codes" have been and wdl continue to be used to refer to ώe general techmques of ώis technology and often refer specifically to ώe composite embedded code signal as defined early on in ώis disclosure MORE DETAILS ON EMBEDDING SIGNATURE CODES INTO MOTION PICTURES Just as ώere is a distinction made between ώe JPEG standards for compressmg stdl images and ώe MPEG standards for compressed motion unages, so too should ώere be distinctions made between placing invisible signatures mto still images and placing signatures mto motion unages. As wiώ ώe JPEG/MPEG distinction, it is not a matter of different foundations, it is ώe fact ώat wiώ motion unages a new dimension of engineering optimization opens up by ώe inclusion of time as a parameter. Any textbook dealing wiώ MPEG wdl surely contain a section on how MPEG is (generally) not merely applymg JPEG on a frame by frame basis. It wdl be ώe same wiώ ώe application of ώe pnnciples of ώis technology generally speaking, ώe placement of invisible signatures mto motion image sequences will not be sunply independently placing invisible signatures mto one frame after ώe next A vaπety of time-based considerations come mto play, some dealing wiώ ώe psychophysics of motion image perception, oώers dnven by simple cost engineering considerations. One embodiment actually uses ώe MPEG compression standard as a piece of a solution. Oώer motion image compression schemes could equally well be used, be ώey already invented or yet to be invented. This example also utilizes ώe scrambled logo image approach to generatmg ώe master snowy image as depicted in Fig. 13 and discussed m ώe disclosure. A "compressed master snowy image" is mdependently rendered as depicted in Fig. 15. "Rendered" refers to ώe generally well known technique in video, movie and animation production whereby an unage or sequence of images is created by constructive techmques such as computer instructions or ώe drawing of ammauon cells by hand. Thus, "to render" a signature movie in ώis example is essentially to let eiώer a computer create it as a digital file or to design some custom digital electronic circuitry to create it. The overall goal of ώe procedure outlined in Fig. 15 is to apply ώe mvisible signatures to ώe oπgmal movie 762 in such a way ώat ώe signatures do not degrade ώe commercial value of ώe movie, memorialized by ώe side-by-side viewing, 768, AND in such a way ώat ώe signature optimally survives through ώe MPEG compression and decompression process. As noted earlier, ώe use of ώe MPEG process in panicular is an example of ώe genenc process of compression. Also it should be noted ώat ώe example presented here has definite room for engineering vanations. In panicular, ώose practiced in ώe an of motion picture compression will appreciate ώe fact if we start out wiώ two video streams A and B, and we compress A and B separately and combine ώeir results, ώen ώe resultant video stream C will not generally be ώe same as if we pre-added ώe video streams A and B and compressed ώis resultant. Thus we have m general, e.g.: MPEG(A) + MPEG(B) =\= MPEG(A+B) where =\= is not equal to. This is somewhat an abstract notion to mtroduce at ώis pomt m ώe disclosure and wdl become more clear as Fig. 15 is discussed. The general idea, however, is ώat ώere will be a vaπety of algebras ώat can be used to optimize ώe pass-ώrough of "invisible" signatures dirough compression procedures. Clearly, ώe same principles as depicted m Fig. 15 also work on stdl images and ώe JPEG or any oώer stdl image compression standard. Turning now to ώe detads of Fig. 15, we begin wiώ ώe simple steppmg ώrough of all Z frames of a movie or video. For a two hour movie played at 30 frames per second, Z turns out to be (30*2*60*60) or 216,000. The inner loop of 700, 702 and 704 merely mimics Fig. 13's steps. The logo frame optionally can change during ώe stepping ώrough frames. The two arrows emanating from ώe box 704 represent boώ ώe continuation of ώe loop 750 and ώe depositing of ouφut frames mto ώe rendered master Snowy Image 752. To take a bπef but potentially appropnate digression at ώis pomt, ώe use of ώe concept of a Markov process brings certain ciaπry to ώe discussion of optimizing ώe engineering implementation of ώe meώods of Fig. 15 Bπefly, a Markov process is one m which a sequence of events takes place and in general ώere is no memory between one step in ώe sequence and ώe next. In ώe context of Fig 15 and a sequence of images, a Markovian sequence of images would be one in which ώere is no apparent or appreciable correlation between a given frame and ώe next. Imagine taking ώe set of all movies ever produced, steppmg one frame at a time and seiectmg a random frame from a random movie to be inser t ed mto an ouφut movie, and ώen steppmg ώrough, say, one minute or 1800 of ώese frames. The resultmg "movie" would be a fine example of a Markovian movie. One pomt of this discussion is ώat dependmg on how ώe logo frames are rendered and dependmg on how ώe encryption/scrambling step 702 is performed, ώe Master Snowy Movie 752 wdl exhibit some generally quantifiable degree of Markovian charactenstics The pomt of ώis pomt is ώat ώe compression procedure itself will be affected by dus degree of Markovian nature and ώus needs to be accounted for in designing ώe process of Fig. 15. Likewise, and only in general, even if a fully Markovian movie is created m ώe High Bnghtness Master Snowy Movie, 752, ώen ώe processmg of compressing and decompressmg dial movie 752, represented as ώe MPEG box 754, will break down some of ώe Markovian nature of 752 and create at least a marginally non-Markovian compressed master Snowy Movie 756. This pomt will be uulized when ώe disclosure bπefly discusses ώe idea of usmg multiple frames of a video stream in order to find a smgle N-bit identification word, ώat is, ώe same N-bit idenuficauon word may be embedded mto several frames of a movie, and it is quite reasonable to use ώe mformation deπved from ώose multiple frames to find ώat smgle N-bit identification word. The non-Markovian nature of 756 ώus adds certain tools to readmg and recognizing ώe invisible signatures. Enough of ώis tangent. Wiώ ώe intent of pre-conditioning ώe ultimately utdized Master Snowy Movie 756, we now send ώe rendered High Bπghmess Master Snowy Movie 752 ώrough boώ ώe MPEG compression AND decompression procedure 754. Wiώ ώe caveat previously discussed where it is acknowledged ώat ώe MPEG compression process is generally not distπbutive, ώe idea of ώe step 754 is to crudely segregate ώe initially rendered Snowy Movie 752 mto two components, ώe component which survives ώe compression process 754 which is 756, and ώe component which does not survive, also crudely estimated usmg ώe difference operauon 758 to produce ώe "Cheap Master Snowy Movie" 760. The reason use is made of ώe deliberately loose term "Cheap" is ώat we can later add ώis signature signal as well to a distπbutable movie, knowing ώat it probably won't survive common compression processes but ώat nevertheless it can provide "cheap" extra signature signal energy for applications or situations which will never expenence compression. [Thus it is at least noted m Fig. 15]. Back to Fig. 15 proper, we now have a rough cut at signatures which we know have a higher ldcelraood of surviving intact ώrough ώe compression process, and we use diis "Compressed Master Snowy Movie" 756 to ώen go through ώis procedure of being scaled down 764, added to ώe oπgmal movie 766, producmg a candidate distπbutable movie 770, ώen compared to ώe oπgmal movie (768) to ensure ώat it meets whatever commercially viable cπteπa which have been set up (I e. ώe acceptable perceived noise level) The aπow from ώe side-by-side step 768 back to ώe scale down step 764 conesponds quite directly to ώe "expenment visually... " step of Fig 2, and ώe gam control 226 of Fig. 6 Those practiced in ώe art of image and audio mformation ώeory can recognize ώat ώe whole of Fig. 15 can be summarized as attemptmg to pre-condition ώe invisible signature signals in such a way drat ώey are better able to wiώstand even quite appreciable compression. To reiterate a previously menuoned item as well, ώis idea equally applies to ANY such pre-identifiable process to which an image, and unage sequence, or audio track might be subjected. This clearly includes ώe JPEG process on still images. Additional Elements of ώe Realtime Encoder Circuitry It should be noted ώat ώe meώod steps represented in Fig. 15, generally followmg from box 750 up ώrough ώe creation of ώe compressed master snowy movie 756, could wiώ certain modificauon be implemented in hardware. In panicular, ώe overall analog noise source 206 in Fig. 6 could be replaced by such a hardware circuit. Likewise ώe steps and associated procedures depicted in Fig. 13 could be implemented in hardware and replace ώe analog noise source 206. Recogmtion based on more ώan one frame: non-Markovian signatures As noted in ώe digression on Markov and non-Markov sequences of unages, it is pomted out once again ώat m such circumstances where ώe embedded invisible signature signals are non-Markovian in nature, i.e., ώat ώere is some conelation between ώe master snowy unage of one frame to ώat of ώe next, AND furthermore ώat a singie N-bit identification word is used across a range of frames and ώat ώe sequence of N-bit identification words associated wiώ ώe sequence of frames is not Markovian m nature, ώen it is possible to uulize ώe data from several frames of a movie or video in order to recognize a smgle N-bit identification word. All of ώis is a fancy way of saying ώat ώe process of recognizing ώe invisible signatures should use as much mformation as is available, in this case translating to multiple frames of a motion unage sequence. HEADER VERIFICATION The concept of ώe "header" on a digital unage or audio file is a well established practice m ώe an. The top of Fig. 16 has a simplified look at ώe concept of ώe header, wherem a data file begins wiώ generally a comprehensive set of mformation about ώe file as a whole, often including mformation about who ώe author or copynght holder of ώe data is, if ώere is a copynght holder at all. This header 800 is ώen typically followed by ώe data itself 802, such as an audio stream, a digital image, a video stream, or compressed versions of any of ώese items. This is ail exceedmgiy known and common in ώe industry One way in which ώe pnnciples of ώis technology can be employed in ώe service of mforma t ion integnty is geneπcally depicted m ώe lower diagram ot Fig. 16 In general, ώe N-bit identifica t ion word can be used to essentially "wallpaper" a given simple message ώroughout an image (as depicted) or audio data stream, ώereby reinforcing some message already contained m a traditional header This is refened to as "header veπfication" in ώe title of ώis section. The thinking here is ώat less sophisticated would-be pirates and abusers can alter ώe mformation content of header mformation, and ώe more secure techmques of ώis technology can ώus be used as checks on ώe veracity of header mformation. Provided ώat ώe code message, such as "joe's image" in ώe header, matches ώe repeated message throughout an unage, ώen a user obtaining ώe unage can have some higher degree of confidence ώat no alteration of ώe header has taken place. Likewise, ώe header can actually cany ώe N-bit identification word so ώat ώe fact ώat a given data set has been coded via ώe meώods of ώis technology can be highlighted and ώe veπfication code built nght mto ώe header. Naturally, this data file format has not been created yet smce ώe pnnciples of dus technology are currendy not bemg employed. THE "BODIER" THE ABILITY TO LARGELY REPLACE A HEADER Alώough all of ώe possible applications of ώe followmg aspect of applicant's technology are not fully developed, it is nevertheless presented as a design alternative ώat may be important some day The title of this section contains ώe silly phrase used to descπbe ώis possibdity- ώe "bodier." Whereas ώe previous section outlmed how ώe N-bit identification word could "veπfy" mformation contained wiώin ώe header of a digital file, ώere is also ώe prospect ώat ώese meώods could completely replace ώe very concept of ώe header and place ώe mformation which is traditionally stored tn ώe header directiy mto ώe digital signal and empirical data itself This could be as simple as standardizing on, purely for example, a 96-bit (12 bytes) leader stπng on an otherwise entirely empincal data stream. This leader string would plain and simple contain ώe numeπc lengώ, m elemental data umts, of ώe enure data file not including ώe leader suing, and ώe number of bits of depώ of a smgle data element (e.g. its number of grey levels or ώe number of discrete signal levels of an audio signal). From ώere, umversal codes as descπbed m ώis specification would be used to read ώe N-bit identification word wπtten direcdy withm ώe empincal data. The lengώ of ώe empincal data would need to be long enough to contain ώe full N bus. The N-bit word would effectively transmit what would otherwise be contained m a traditional header Fig. 17 depicts such a data format and calls it ώe "umversal empincal data format " The leader stπng 820 is compπsed of ώe 64 bit stπng lengώ 822 and ώe 32 bit data word size 824 The data stream 826 ώen immediately follows, and ώe mformation traditionally contained in ώe header but now contained directly in ώe data stream is represented as ώe attached dotted Ime 828. Anoώer term used for ώis attached mformation is a "shadow channel" as also depicted m Fig. 17. Yet anoώer element ώat may need to be mcluded m ώe leader stπng is some sort of complex check sum bits which can venfy ώat ώe whole of ώe data file is intact and unaltered. This is not included MORE ON DISTRIBUTED UNIVERSAL CODE SYSTEMS: DYNAMIC CODES One intπguing vaπation on ώe ώeme of umversal codes is ώe possibdity of ώe N-bit identification word actually containing mstructions which vary ώe operations of ώe umversal code 'system itself. One of many examples is immediately in order: a data transmission is begun wherem a given block of audio data is fully transmitted, an N-bit idenuficauon word is read knowing ώat ώe first block of data used umversal codes #145 out of a set of 500, say, and ώat pan of ώe N-bit identification word ώus found is ώe mstructions ώat ώe next block of data should be "analyzed" usmg ώe umversal code set #411 raώer ώan #145. In general, ώis technology can ώus be used as a meώod for changmg on ώe fly ώe actual decodmg mstructions themselves. Also in general, ώis abdiry to utilize "dynamic codes" should gready increase ώe sophistication level of ώe data veπfication procedures and mcrease ώe economic viability of systems which are prone to less sophisticated ώwaπing by hackers and would-be pirates. The mventor does not believe ώat ώe concept of dynamically changmg decoding/decrypting instructions is novel per se, but ώe carrymg of ώose mstructions on ώe "shadow channel" of empincal dau does appear to be novel to ώe best of ώe mventor' s understanding. [Shadow channel was previously defined as yet anoώer vernacular phrase encapsulating ώe more steganographic proper elements of ώis technology] . A vaπant on ώe ώeme of dynamic codes is ώe use of umversal codes on systems which have a priori assigned knowledge of which codes to use when. One way to summarize ώis possibility is ώe idea of "ώe daily password." The password m tins example represents knowledge of which set of umversal codes is currendy operauve, and ώese change dependmg on some set of application-specific circumstances. Presumably many applications would be continually updating ώe umversal codes to ones which had never before been used, which is often ώe case wiώ ώe traditional concept of ώe dady password. Part of a currendy transmitted N-bit identificauon word could be ώe passmg on of ώe next day's password, for example. Though time might be ώe most common tπgger events for ώe changmg of passwords, ώere could be event based tπggers as well. SYMMETRIC PATTERNS AND NOISE PATTERNS: TOWARD A ROBUST UNIVERSAL CODING SYSTEM The placement of identification patterns into images is certainly not new . Logos stamped in t o comers of images, subtle patterns such as true signatures or ώe wallpapering of ώe copyright circle-C symbol, and ώe watermark proper are all examples of placing patterns into images in order to signify ownership or to try to prevent illicit uses of ώe creative material. What does appear to be novel is ώe approach of placing mdependent "carrier" patterns, which ώemseives are capable of being modulated wiώ certain information, directly into images and audio for ώe purposes of transmission and discernment of said information, while effectively being imperceptible and/or unintelligible to a perceiving human. Steganographic solutions currently known to ώe inventor all place ώis information "direcdy" mto empirical data (possibly first encrypted, ώen directly), whereas ώe meώods of ώis disclosure posit ώe creation of ώese (most-often) coextensive carrier signals, ώe modulation of ώose carrier signals wiώ ώe information proper, THEN ώe direct application to ώe empirical data. In extendmg ώese concepts one step further into ώe application arena of universal code systems, where a sending site transmits empirical data wiώ a certain umversal coding scheme employed and a receiving site analyzes said empirical data using ώe universal coding scheme, it would be advantageous to take a closer look at ώe engineering considerations of such a system designed for ώe transmission of images or motion images, as opposed to audio. Said more clearly, ώe same type of analysis of a specific implementation such as is contained in Fig. 9 and its accompanying discussion on ώe universal codes in audio applications should as well be done on imagery (or two dimensional signals). This section is such an analysis and outline of a specific implementation of universal codes and it attempts to anticipate various hurdles ώat such a meώod should clear. The unifying ώeme of one implementation of a universal coding system for imagery and motion imagery is "symmetry." The idea driving ώis couldn't be more simple: a prophylactic against ώe use of image rotation as a means for less sophisticated pirates to bypass any given universal coding system. The guiding principle is that ώe universal coding system should easϋy be read no matter what rotational orientation ώe subject imagery is in. These issues are quite common in ώe fields of optical character recognition and object recognition, and ώese fields should be consulted for further tools and tricks in furthering ώe engineering implementation of ώis technology. As usual, an immediate example is in order. Digital Video And Internet Company XYZ has developed a delivery system of its product which relies on a non-symmetric universal coding which double checks incoming video to see if ώe individual frames of video itself, ώe visual data, contain XYZ's own relatively high security internal signature codes using ώe meώods of ώis technology. This works well and fine for many delivery situations, including ώeir Internet toilgate which does not pass any material unless boώ ώe header information is verified AND ώe in-frame universal codes are found. However, anoώer piece of ώeir commercial network performs mundane routme monitoring on Internet channels to look for unauthorized transmission of ώeir proprietary creative property. They control ώe encryption procedures used, ώus it is no problem for ώem to uneπcrypt creative property, including headers, and perform straightforward checks A pirate group ώat wants to traffic mateπal on XYZ's network has determmed how to modify ώe secuπty features in XYZ's header mformation system, and ώey have furthermore discovered ώat by sunply rotating imagery by 10 or 20 degrees, and transmitting tt over XYZ's network, ώe network doesn't recognize ώe codes and tnerefore — does not flag dlicit uses of ώeir mateπal. and ώe receiver of ώe pirate's rotated mateπal simply unrotates it. Summarizing ώis last example via logical categoπes, ώe πon-symmetπc universal codes are quite acceptable for ώe "enablement of auώorized action based on ώe rinding of die codes," whereas it can be somewhat easdy by-passed in ώe case of "random monitoring (policing) for ώe presence of codes " [Bear m mind ώat ώe πon-symmetπc umversal codes may very well catch 90% of illicit uses, i.e. 90% of ώe illicit users wouldn't boώer even going to ώe simple by-pass of rotation.] To address ώis latter category, ώe use of quasi-rotauonally symmetπc universal codes is called for. "Quasi" deπves from ώe age old squaring ώe circle issue, in dus instance translating mto not quite bemg able to represent a full incrementally rotauonai symmetnc 2-D object on a square gπd of pixels. Furthermore, basic considerations must be made for scale/magnification changes of ώe umversal codes. It is understood ώat ώe momtoπng process must be performed when ώe monitored visual mateπai is m ώe "perceptual" domam, i.e. when it has been unencrypted or unscrambled and in ώe form wiώ which it is (or would be) presented to a human viewer. Would-be pirates could attempt to use oώer simple visual scramblmg and unscrambling techmques, and tools could be developed to momtor for ώese telltale scrambled signals. Said anoώer way, would-be pirates would ώen look to transform visual matenal out of ώe perceptual domam, pass by a monitoring pomt, and ώen transform ώe mateπal back mto ώe perceptual domam, tools oώer ώan ώe momtoπng for umversal codes would need to be used in such scenaπos. The momtoπng discussed here ώerefore applies to applications where momtoπng can be performed m ώe perceptual domam, such as when it is actually sent to viewing equipment. The "ring" is ώe only full rotationally symmetric two dimensional object. The "disk" can be seen as a simple finite senes of conccπtnc and perfectly abutted πngs havmg widώ along ώeir radial axis. Thus, ώe "nng" needs to be ώe staπing pomt from which a more robust umversal code standard for images is found. The nng also will fit nicely mto ώe issue of scale/magnification changes, where ώe radius of a nng is a smgle parameter to keep track of and account for. Anoώer property of ώe nng is ώat even ώe case where differential scale changes are made to different spatial axes m an image, and ώe nng turns mto an oval, many of ώe smooώ and quasi-symmetπc properties ώat any automated momtoπng system wdl be lookmg for are generally maintained. Likewise, appreciable geometπc distortion of any unage wdl clearly distort ππgs but ώey can stdl maintain gross symmetnc properties. Hopefully, more pedestrian meώods such as simply "viewing" imagery will be able to detect attempted illicit piracy in ώese regards, especially when such lengώs are taken to by-pass ώe umversal codmg system. Rings to Knots Havmg discovered ώe ring as ώe only ideal symmetric pattern upon whose foundation a full rotationally robust universal coding system can be built, we must mm ώis basic pattern mto someώing functional, someώing which can carry information, can be read by computers and oώer instrumentation , can survive simple transformations and corruptions, and can give rise to reasonably high levels of securi t y (probably not unbreakable, as ώe section on universal codes explained) in order to keep ώe economics of subversion as a simple incremental cost item. One embodiment of ώe "ring-based" universal codes is what ώe mventor refers to as "knot patterns" or simply "knots," in deference to woven Celtic knot patterns which were later refined and exalted in ώe works of Leonardo Da Vmci (e.g. Mona Lisa, or his knot engravmgs). Some rumors have it ώat ώese drawings of knots were mdeed steganographic in nature, i.e. conveying messages and signatures; all ώe more appropriate. Figs. 18 and 19 explore some of ώe fundamental properties of ώese knots. Two simple examples of knot patterns are depicted by ώe supra-radial knots, 850 and ώe radial knots 852. The names of ώese types are based on ώe central symmetry pomt of ώe splayed rings and wheώer ώe constituent rings intersect ώis pomt, are fully outside it, or in ώe case of sub-radial knots ώe central point would be inside a constituent circle. The examples of 850 and 852 clearly show a symmetrical arrangement of 8 rings or circles. "Rings" is ώe more appropriate term, as discussed above, in ώat ώis term explicidy acknowledges ώe width of ώe rings along ώe radial axis of ώe ring. It is each of ώe individual rings in ώe knot patterns 850 and 852 which will be ώe carrier signal for a single associated bit plane in our N-bit identification word. Thus, ώe knot patterns 850 and 852 each are an 8-bit carrier of information. Specifically, assuming now ώat ώe knot patterns 850 and 852 are luminous rings on a black background, ώen ώe "addition" of a luminous ring to an independent source image could represent a "1 " and ώe "subtraction" of a luminous ring from an independent source image could represent a "0." The application of ώis simple encoding scheme could ώen be replicated over and over as in Fig. 19 and its mosaic of knot patterns, wiώ ώe ultimate step of adding a scaled down version of ώis encoded (modulated) knot mosaic direcdy and coextensively to ώe original image, wiώ ώe resultant being ώe distributable image which has been encoded via this universal symmetric coding meώod. It remains to communicate to a decoding system which ring is ώe least significant bit in our N-bit identification word and which is ώe most significant. One such meώod is to make a slightly ascending scale of radii values (of ώe mdividual rings) from ώe LSB to ώe MSB. Anoώer is to merely make ώe MSB, say, 10% larger radius ώan ail ώe oώers and to pre-assign counterclockwise as ώe order wiώ which ώe remaining bits fall out. Yet anoώer is to put some simple hash mark inside one and only one circie. In oώer words, ώere are a variety of ways wiώ which ώe bit order of ώe rings can be encoded in ώese knot patterns. A procedure for, first, checkmg for ώe mere existence of ώese knot patterns and, second, for reading of ώe N-bit identification word, is as follows A suspect image is first fouπer transformed via ώe extremely common 2D FFT computer procedure. Assuming ώat we don't know ώe exact scale of ώe knot patterns, i.e., we don't know ώe radius of an elemental nng of ώe knot pattern m ώe units of pixeis, and ώat we don't know ώe exact rotational state of a knot pattern, we merely inspect (via basic automated pattern recogmtion meώods) ώe resultmg magnitude of ώe Founcr transform of ώe oπgmal image for telltale πpple patterns (concentπc low amplitude sinusoidal nngs on top of ώe spatial frequency profile of a source image). The peπodicity of ώese nngs, along wiώ ώe spacing of ώe nngs, will inform us ώat ώe umversal knot patterns are or are not likely present, and ώeir scale in pixels Classical small signal detection meώods can be applied to ώis problem just as ώey can to ώe oώer detection methodologies of ώis disclosure. Common spatial filtering can ώen be applied to ώe fouπer transformed suspect image, where ώe spatial filter to be used would pass all spatial frequencies which are on ώe crests of ώe concentπc circles and block all oώer spatial frequencies. The resultmg filtered image would be fouπer transformed out of ώe spatial frequency domam back mto ώe unage space domam, and almost by visual mspection ώe mversion or non-inversion of ώe lununous nngs could be detected, along wiώ identification of ώe MSB or LSB nng, and ώe (in this case 8) N-bit identificauon code word could be found. Clearly, a pattern recogmtion procedure could perform dus decodmg step as well. The preceding discussion and ώe meώod it descnbes has certain practical disadvantages and shortcomings which wdl now be discussed and unproved upon The basic meώod was presented in a simple-minded fashion in order to communicate ώe basic pnnciples involved. Let's enumerate a few of ώe pracucai difficulties of ώe above descπbed umversal codmg system usmg ώe knot patterns. For one (1), ώe nng patterns are somewhat inefficient tn ώeir "covering" of ώe full image space and in usmg all of ώe information carrymg capacity of an unage extent. Second (2), ώe nng patterns ώemselves will almost need to be more visible to ώe eye if ώey are applied, say, in a straightforward additive way to an 8-bit black and white image. Next (3), ώe "8" nngs of Fig 18, 850 and 852, is a raώer low number, and moreover, ώere is a 22 and one half degree rotauon which could be applied to ώe figures which ώe recogmtion meώods would need to contend wiώ (360 divided by 8 divided by 2). Next (4), stnct overlapping of nngs would produce highly condensed areas where ώe added and subtracted bnghtness could become quite appreciable. Next (5), ώe 2D FFT routme used in ώe decodmg is notoπously computationally cumbersome as well as some of ώe pattern recogmuon meώods alluded to Finally (6), though ώis heretofore descπbed foπn of umversal codmg does not pretend to have ultra-high secunty in ώe classical sense of top secuπty communications systems, it would nevertheless be advantageous to add certain secuπty features which would be inexpensive to implement m hardware and software systems which at ώe same time would mcrease ώe cost of would-be pirates attempting to ώwart ώe system, and mcrease ώe necessary sophistication level of ώose pirates, to ώe pomt ώat a would-be pirate would have to go so far out of ώeir way to ώwaπ the system ώat willfulness would be easily proven and hopefully subject to stiff cπminal liability and penalty (such as ώe creation and distπbu t ion of tools which stπp creative property of ώese knot pattern codes). All of ώese items can be addressed and should continue to be refined upon in any engmeenng —implementation of ώe pnnciples of ώe technology This disclosure addresses ώese items wiώ reference t o ώe followmg embodiments Beginning wiώ item number 3, ώat ώere are only 8 nngs represented in Fig. 18 is simply remedied by increasing ώe number of nngs. The number of nngs ώat any given application will utilize is clearly a function of ώe application The trade-offs include but are not limited to on ώe side which argues to limit ώe number of nngs utdized, ώere will ultimately be more signal energy per nng (per visibdity) if ώere are less nngs, ώe nngs will be less crowded so ώat ώere discernment via automated recogmuon meώods wdl be facilitated; and m general smce ώey are less crowded, ώe full knot pattern can be contained usmg a smaller overall pixel extent, e g a 30 pixel diameter region of unage raώer ώan a 100 pixel diameter region. The arguments to mcrease ώe number of nngs include- ώe desire to transmit more information, such as ascu information, seπal numbers, access codes, allowed use codes and mdex numbers, history mformation, etc , anoώer key advantage of havmg more nngs is ώat ώe rotation of ώe knot pattern back mto itself is reduced, ώereby allowmg ώe recogmuon meώods to deal wiώ a smaller range of rotation angles (e.g , 64 nngs will have a maximum rotational displacement of just under 3 degrees, i e maximally dissimilar to its onginai pattern, where a rotation of about 5 and one half degrees brmgs ώe knot pattern back mto its initial alignment, ώe need to distmguish ώe MSB/LSB and ώe bit plane order is better seen m ώis example as well). It is anticipated ώat most practical applications will choose between 16 and 128 nngs, conespondmg to N = 16 to N = 128 for ώe choice of ώe number of bits in ώe N-bit identification code word The range of dus choice would somewhat conelate to ώe overall radius, m pixels, allotted to an elemental knot pattern such as 850 or 852 Addressing ώe practical difficulty item number 4, ώat of ώe condensation of nngs patterns at some pomts m ώe image and lack of nng patterns m oώers (which is very similar, but stdl distmct from, item 1, ώe inefficient covering), ώe followmg improvement can be applied Fig 18 shows an example of a key fearure of a "knot" (as opposed to a pattern of nngs) m ώat where patterns would supposedly intersect, a virtual ώird dimension is posited whereby one strand of ώe knot takes precedence over anoώer strand m some predefined way, see item 854. In ώe terms of imagery, ώe bnghtness or dimness of a given intersection pomt in ώe knot patterns would be "assigned" to one and only one strand, even m areas where more ώan two strands overlap The idea here is ώen extended, 864, to how rules about ώis assignment should be earned out m some rotationally symmetric manner For example, a rule would be ώat, travellmg clockwise, an incoming strand to a loop would be "behind" an outgomg strand Clearly ώere are a multitude of vanations which could be applied to ώese rales, many which would cntically depend on ώe geometry of ώe knot patterns chosen Oώer issues mvolved will probably be ώat ώe finite widώ, and moreover ώe bnghtness profile of ώe widώ along ώe normal axis to ώe direction of a strand, will all play a role in ώe rules of bπghmess assignment to any given pixel underlying ώe knot patterns. A major improvement to ώe nominal knot pattern system previously descnbed directly addresses practical difficulties (1), ώe inefficient covering, (2) ώe unwanted visibility of ώe nngs, and (6) ώe need for higher levels of secuπty This improvement also indirectly address item (4) ώe overlapping issue, which has been discussed m ώe last paragraph This major improvement is ώe followmg- just pπor to ώe step where ώe mosaic of ώe encoded knot patterns is added to an oπgmal image to produce a distπbutable image, ώe mosaic of encoded knot patterns, 866, is spatially filtered (usmg common 2D FFT techmques) by a standardized and (generally smooώly) random phase-only spatial filter. It is very important to note ώat ώis phase-only filter is itself fully rotationally symmetnc withm ώe spatial frequency domam, i e. its filtering effects are fully rotationally symmetnc. The effect of ώis phase-only filter on an mdividual luminous nng is to transform it mto a smooώiy varymg pattern of coπcenrπc nngs, not totally dissimilar to ώe pattern on water several instances after a pebble is dropped in, only ώat ώe wave patterns are somewhat random in ώe case of ώis phase-only filter raώer ώan ώe umfoπn penoώcity of a pebble wave pattern. Fig. 20 attempts to give a rough (i.e. non-greyscale) depiction of ώese phase-only filtered nng patterns The top figure of Fig. 20 is a cross section of a typical bπghmess contour/profile 874 of one of ώese phase-only filtered nng patterns. Referenced m ώe figure is ώe nominal location of ώe pre-filtered outer nng center, 870 The center of an mdividual nng, 872, is referenced as ώe pomt around which ώe bπghmess profile is routed m order to fully descnbe ώe two dimensional bπghmess distπbution of one of ώese filtered patterns. Yet anoώer rough attempt to communicate ώe characteπsucs of ώe filtered nng is depicted as 876, a crude grayscale unage of ώe filtered nng This phase-only filtered nng, 876 will can be refened to as a random πpple pattern. Not depicted in Fig. 20 is ώe composite effects of phase-only filtering on ώe knot patterns of Fig 18, or on ώe mosaic of knot patterns 866 in Fig. 19. Each of ώe individual nngs in ώe knot patterns 850 or 852 wdl give πse to a 2D bπghmess pattern of ώe type 876, and togeώer ώey form a raώer complicated bnghmess pattern Realizing ώat ώe encodmg of ώe nngs is done by making it lununous (1) or "anti-luminous" (0), ώe resultmg phase-only filtered knot patterns begin to take on subtle charactenstics which no longer make direct sense to ώe human eye, but which are still readdy discernabie to a computer especially after ώe phase-only filtering is inverse filtered reproducing ώe onginal nngs patterns Returning now to Fig. 19, we can imagine ώat an 8-bit identificauon word has been encoded on ώe knot patterns and ώe knot patterns phase-only filtered. The resultmg bnghtness distπbution would be a πch tapestry of overlapping wave patterns which would have a certain beauty, but would not be readily intelligible to ώe eye/brain. [An exception to ώis might draw from ώe lore of ώe Souώ Pacific Island communities, where it is said ώat sea travellers have learned ώe subtle art of readmg small and multiply complex ocean wave patterns, generated by diffracted and reflected ocean waves off of mtervenmg islands , as a pnmary navigational tool.] For want of a better term, ώe resultmg mosaic of filtered knot pa t terns (deπved from 866) can be called ώe encoded knot tapestry or just ώe knot tapestry Some basic properties of ώis knot tapestry are ώat it retains ώe basic rotational symmetry of its generator mosaic, i t is generally unintelligible to ώe eye/brain, ώus raising it a notch on ώe sophistication level of reverse engineering, it is more efficient at usmg ώe available mformation content of a gπd of pixels (more on ώis ra ώe next section), and if ώe basic knot concepts 854 and 864 are utilized, it will not give πse to local "hot spots" where ώe signal level becomes unduly condensed and hence objectionably visible to a viewer. The basic decodmg process previously descπbed would now need ώe additional step of inverse filtering ώe phase-only filter used m ώe encodmg process. This inverse filtering is qmte well known in ώe image processmg mdustry Provided ώat ώe scale of ώe knot patterns are known a priori, ώe inverse filtering is straightforward. If on ώe oώer hand ώe scale of ώe knot patterns is not known, ώen an additional step of discovering ώis scale is in order One such meώod of discovering ώe scale of ώe knot patterns is to iteratively apply ώe inverse phase-only filter to vaπously scaled version of an image bemg decoded, searching for which scale-version begins to exhibit noticeable knot patterning A common search algoπthm such as ώe simplex meώod could be used m order to accurately discover ώe scale of ώe patterns. The field of object recogmuon should also be consulted, under ώe general topic of unknown-scale object detection. An addiuonal pomt about ώe efficiency wiώ which ώe knot tapestry covers ώe unage pixel gπd is in order. Most applications of ώe knot tapestry meώod of umversal unage codmg will posit ώe application of ώe fully encoded tapestry (i.e. ώe upestry which has ώe N-bit identification word embedded) at a relative low bπghmess level mto ώe source unage. In reai terms, ώe bπghmess scale of ώe encoded tapestry wdl vary from, for example, -5 grey scale values to 5 grey scale values in a typical 256 grey scale unage, where ώe preponderance of values will be wiώin -2 and 2. This brings up ώe purely practical matter ώat ώe knot tapestry wdl be subject to appreciable bit truncation enor Put as an example, imagine a constructed knot tapestry nicely utdizmg a full 256 grey level image, ώen scaling ώis down by a factor of 20 in bπghmess mcludmg ώe bit truncation step, ώen rescaiiπg this truncated version back up m bπghmess by ώe same factor of 20, ώen inverse phase-only filtering ώe resultant. The resultmg knot pattern mosaic wdl be a noticeably degraded version of ώe onginal knot pattern mosaic The pomt of bringing all of ώis up is ώe followmg: it will be a simply defined, but mdeed challengmg, engineering task to select ώe vanous free parameters of design m ώe implementation of ώe knot tapestry meώod, ώe end goal bemg to pass a maximum amount of mformation about ώe N-bit idenuficauon word widun some pre-defined visibility tolerance of ώe knot tapestry The free parameters mclude but would not be fully limited to: ώe radius of ώe elemental nng in pixels, N or ώe number of nngs, ώe distance in pixels from ώe center of a knot pattern to ώe center of an elemental nng, ώe packing cπteπa and distances of one knot pattern wiώ ώe oώers, ώe rales for strand weaving, and ώe forms and types of phase-only filters to be used on ώe knot mosaics It would be desirable to feed such parameters mto a computer optimization routme which could assist in ώeir selection. Even ώis would begm surely as more of an art ώάn a science due to ώe many non-linear free parameters mvolved A side note on ώe use of phase-only filtering is ώat it can assist in ώe detection of ώe nng patterns. It does so m ώat ώe inverse filtering of ώe decodmg process tends to "blur" ώe underlying source image upon which ώe knot tapestry is added, while at ώe same tune "bringing mto focus" ώe nng patterns Wiώout ώe blurring of ώe source unage, ώe emerging nng patterns would have a harder tune "competmg" wiώ ώe sharp features of typical unages. The decodmg procedure should also utdize ώe gradient diresholdmg meώod descπbed m anoώer section. Bπefly, this is ώe meώod where if it is known ώat a source signal is much larger m bπghmess than our signature signals, ώeπ an unage bemg decoded can have higher gradient areas Uiresholded m ώe service of increasing ώe signal level of ώe signature signals relauve to ώe source signal As for ώe oώer practical ώftϊculry mentioned earlier, item (5) which deals wiώ ώe relauve computauonal overhead of ώe 2D FFT routine and of typical pattern recogmuon routmes, ώe first remedy here posited but not filled is to find a simpler way of quickly recognizing and decodmg ώe poiaπty of ώe nng bπghtnesses ώan ώat of usmg ώe 2D FFT Barring this, it can be seen ώat if ώe pixel extent of an mdividual knot pattern (850 or 852) is, for example, 50 pixels in diameter, than a simple 64 by 64 pixel 2D FFT on some section of an image may be more ώan sufficient to discern ώe N-bit identification word as previously descπbed The idea would be to use ώe smallest image region necessary, as opposed to bemg required to utdize an entire unage, to discern ώe N-bit identification word Anoώer note is ώat ώose practitioners m ώe science of unage processmg wdl recognize ώat mstead of beginning ώe discussion on ώe knot upestry wiώ ώe utdization of nngs, we could have mstead jumped nght to ώe use of 2D bπghmess distπbution patterns 876, QUA bases functions The use of ώe "nng" terminology as ώe baselme technology is partly didactic, as is appropπate for patent disclosures m any event. What is more important, perhaps, is ώat ώe use of true "nngs" in ώe decodmg process, post-inverse filtering, is probably ώe simplest form to mput mto typical pattern recogmtion routmes. Neural Network Decoders Those skilled m ώe signal processmg an will recognize ώat computers employing neural network architectures are well suited to ώe pattern recogmtion and detection-of-small-signal-in-noise issues posed by ώe present technology. While a complete discourse on ώese topics is beyond ώe scope of ώis specification, ώe interested reader is refened to, e.g., Cherkassky, V , "From Statistics to Neural Networks- Theory & Pattern Recogmtion Applications." Springer- Verlag, 1994, Masters, T , "Signal & Image Processmg wiώ Neural Networks- C Sourcebook." Wiley, 1994, Guyon, I, "Advances in Pattern Recognition Systems Using Neural Networks. " World Scientific Publishers, 1994, Nigπn, A , "Neural Networks for Pattern Recognition. " MIT Press, 1993; Cichoki, A. , "Neural Networks for Optimization &. Signal Processing. " Wiley, 1993; and Chen, C, "Neural Networks for Pattern Recognition & Their Applications. ' World Scientific Publishers, 1991. 2D UNIVERSAL CODES II SIMPLE SCAN LINE IMPLEMENTATION OF THE ONE DIMENSIONAL CASE The above section on nngs, knots and tapestnes certainly has its beauty, but some of ώe steps involved may have enough complexity ώat practical implementations may be too costly for certain applications. A poor cousm ώe concept of nngs and well-designed symmetry is to simply utdize ώe basic concepts presented in connection wiώ Fig. 9 and ώe audio signal, and apply ώem to two dimensional signals such as images, but to do so in a manner where, for example, each scan Ime m an image has a random starting pomt on, for example, a 1000 pixel long umversal noise signal. It would ώen be mcumbent upon recogmtion software and hardware to interrogate imagery across ώe full range of rotational states and scale factors to "find" ώe existence of ώese umversal codes. THE UNIVERSAL COMMERCIAL COPYRIGHT (UCO IMAGE. AUDIO, AND VIDEO FILE FORMATS It is as well known as it is regretted ώat ώere exist a plethora of file format standards (and not-so-standards) for digital unages, digital audio, and digital video. These standards have generally been formed wiώin specific mdustnes and applications, and as ώe usage and exchange of creative digital matenal proliferated, ώe vanous file formats slugged it out m cross-disciplinary arenas, where today we find a de facto histogram of devotees and users of ώe vanous favoπte formats. The JPEG, MPEG standards for formatting and compression are only slight exceptions it would seem, where some concerted cross- mdustry coUaborauon came mto play. The cry for a simple universal standard file format for audio/visual dau is as old as ώe bolls. The cry for ώe protection of such matenal is older still. Wiώ all due respect to ώe innate difficulties attendant upon ώe creauon of a umversal format, and wiώ all due respect to ώe pretentiousness of outlining such a plan withm a patent disclosure, ώe mventor does believe ώat ώese meώods can serve perhaps as well as anyώing for bemg ώe foundation upon which an accepted world-wide "umversal commercial copynght" format is built. Pracutioners know ώat such animals are not built by proclamauon, but ώrough ώe efficient meetmg of broad needs, tenacity, and luck. More germane to ώe purposes of ώis disclosure is ώe fact ώat ώe application of ώis technology would benefit if it could become a central piece wiώm an mdustry standard file format. The use of umversal codes in particular could be specified wiώm such a standard. The fullest expression of ώe commercial usage of ώis technology comes from ώe knowledge ώat ώe invisible signing is taking place and ώe confidence ώat instills in copynght holders. The followmg is a list of reasons ώat ώe pnnciples of ώis technology could serve as ώe catalyst for such a standard: (1) Few if any tecnnical developments have so isolated and so pomtedly addressed ώe issue of broad-brush protection of empincal data and audio/visual mateπal; (2) All previous file formats have treated ώe mformation about ώe data, and ώe data itself, as two separate and physically distinct entities, whereas ώe meώods of ώis technology can combine ώe two mto one physical entity; (3) The mass scale application of ώe pnnciples of ώis technology will require substantial standardization work in ώe first place, including integration wiώ ώe years-to-come improvements in compression technologies, so ώe standards infrastructure will exist by default; (4) ώe growth of multimedia has created a genenc class of data called "content," which includes text, images, sound, and graphics, arguing for higher and higher levels of "content standards"; and (5) marry mg copynght protection technology and secuπty features direcdy mto a file format standard is long overdue. Elements of a umversal standard would certainly include ώe mirroring aspects of ώe header veπfication meώods, where header informauon is venfied by signature codes direcdy widun data. Also, a umversal standard would oudme how hybπd uses of fully pπvate codes and public codes would commingle. Thus, if ώe public codes were "stπpped" by sophisticated pirates, ώe pπvate codes would remain intact. A umversal standard would specify how invisible signatures would evolve as digital images and audio evoive. Thus, when a given image is created based on several source unages, ώe standard would specify how and when ώe old signatures would be removed and replaced by new signatures, and if ώe header would keep track of ώese evolutions and if ώe signatures ώemselves would keep some kind of record. PIXELS VS. BUMPS Most of ώe disclosure focuses on pixels bemg ώe basic earners of ώe N-bit identification word. The section discussing ώe use of a smgle "master code signal" went so far as to essentially "assign" each and every pixel to a umque bit plane m ώe N-bit identification word. For many applicauons, wiώ one exemplar bemg ώat of ink based printing at 300 dots per mch resolution, what was once a pixel m a pπstme digital image file becomes effectively a blob (e.g. of diώered ink on a piece of paper). Often ώe isolated mformation carrymg capacity of ώe anginal pixel becomes compromised by neighboring pixels spilling over mto ώe geometπcaliy defined space of ώe onginal pixel. Those practiced m ώe art will recognize this as simple spatial filtering and vanous forms of blurring. In such circumstances it may be more advantageous to assign a certain highly local group of pixels to a umque bit plane in ώe N-bit identification word, raώer ώan merely a smgle pixel. The end goal is simply to pre -concentrate more of ώe signature signal energy into ώe lower frequencies, realizing ώa t most practical implementations quickly stπp or mitigate higher frequencies. A simple-minded approach would be to assign a 2 by 2 block of pixels all to be modulated wiώ ώe same ultimate signature grey value, raώer ώan modulating a smgle assigned pixel. A more fancy approach is depicted m Figs. 21A and 21B, where an array of pixel groups is depicted. This is a specific example of a large class of configurations. The idea is ώat now a certain small region of pixels is associated wiώ a given umque bit plane m ώe N-bit identification word, and ώat ώis grouping ac t ually shares pixels between bit planes (ώough it doesn't necessary have to share pixels, as in ώe case of a 2x2 block of pixels above) Depicted m Figs. 21A and 21B is a 3x3 anay of pixels wiώ an example normalized weighting (normalized -- > ώe weights add up to 1) The meώods of ώis technology now operate on ώis elementary "bump," as a unit, raώer ώan on a smgle pixel. It can be seen ώat m ώis example ώere is a fourfold decrease m ώe number of master code values ώat need to be stored, due to ώe spreading out of ώe signature signal. Applications of ώis "bump approach" to placing m invisible signatures include any application which wdl expenence a pnoπ known high amounts of blumng, where proper idenufication is stdl desired even after dus heavy blumng. MORE ON THE STEGANOGRAPHIC USES OF THIS TECHNOLOGY As menuoned m ώe initial sections of ώe disclosure, steganography as an art and as a science is a genenc pπor art to ώis technology Putting ώe shoe on ώe oώer foot now, and as already doubtless apparent to ώe reader who has ventured ώus far, ώe meώods of this technology can be used as a novel meώod for performing steganography (Indeed, all of ώe discussion ώus far may be regarded as exploring vanous forms and implementations of steganography ) In ώe present section, we shall consider steganography as ώe need to pass a message from pomt A to pomt B, where that message is essentially hidden wiώin generally mdependent empincal data. As anyone in ώe mdustry of telecommunications can attest to, ώe range of purposes for passmg messages is quite broad. Presumably ώere would be some extra need, beyond pure hobby, to place messages mto empincal data and empincal signals, raώer ώan sendmg ώose messages via any number of conventional and straightforward channels Past literature and product propaganda within steganography posits ώat such an extra need, among oώers, might be ώe desire to hide ώe fact ώat a message is even bemg sent. Anoώer possible need is ώat a convenuonal communications channel is not available direcdy or is cost prohibitive, assuming, ώat is, ώat a sender of messages can "transmit" ώeir encoded empincal data somehow This disclosure includes by reference all previous discussions on ώe myπad uses to which steganography might apply, while addmg ώe followmg uses which ώe mventor has not previously seen descπbed. The first such use is very simple It is ώe need to carry messages about ώe empincal data withm which ώe message is earned. The little joke is ώat now ώe media is truly ώe message, ώough it would be next to impossible ώat some previous steganographer hasn't already exploited ώis joke. Some of ώe discussion on placing mformation about ώe empincal data directly inside ώat empincal data was already covered m ώe section on replacmg ώe header and ώe concept of ώe "bodier " This section expands upon ώat section somewhat. The advantages of placing a message about empincal dau directly m ώat dau is ώat now only one class of dau object is present raώer ώan ώe previous two classes. In any two class system, ώere is ώe πsk of ώe two classes becoming disassociated, or one class corrupted wiώout ώe oώer knowing about it. A concrete example here is what ώe mventor refers to as "device mdependent mstructions " There exist zdlions of machine dau formats and data file formats. This pieώora of formats has been notoπous m its power to impede progress toward umversal dau exchange and havmg one machine do ώe same dung ώat anoώer machine can do. The mstructions ώat an oπginator might put mto a second class of data (say ώe header) may not at all be compatible wiώ a machine which is intended to recognize ώese instructions. If format conversions have taken place, it is also possible ώat enucal instrucuons have been stπpped along ώe way, or garbled. The improvements disclosed here can be used as a way to "seal in" certain instructions direcdy mto empincal data m such a way ώat all ώat is needed by a readmg machine to recognize mstructions and messages is to perform a standardized "recogmuon aigoπώm" on ώe empincal dau (providing of course ώat ώe machine can at ώe very least "read" ώe empincal dau properly). All maclunes could implement this algoπtiim any old way ώey choose, usmg any compders or internal dau formats ώat ώey want. Implemenuuon of this device mdependent instruction meώod would generally not be concerned over ώe issue of piracy or dlicit removal of ώe sealed in messages. Presumably, ώe embedded messages and mstructions would be a central valuable component m ώe basic value and functioning of ώe mateπal Another example of a land of steganographic use of ώe technology is ώe embedding of umversal use codes for ώe benefit of a user community The "message" bemg passed could be sunply a registered seπal number identifying ownership to users who wish to legitimately use and pay for ώe empincal information. The seπal number could mdex mto a vast registry of creative property, containing ώe name or names of ώe owners, pπcing information, billing mformation, and ώe Idee. The "message" could also be ώe clearance of free and public use for some given mateπal. Sundar ownership identification and use indexing can be achieved m two class dau structure meώods such as a header, but ώe use of ώe smgle class system of ώis technology may offer certain advantages over ώe two class system in ώat ώe smgle class system does not care about file format conversion, header compatibilities, internal data format issues, header/body archiving issues, and media transformations. Fullv Exact Steganography Pnor an steganographic meώods cunently known to ώe inventor generally involve nilly determmisuc or "exact" prescπptions for passmg a message. Anoώer way to say ώis is ώat it is a basic assumption ώat for a given message to be passed conectly in its entirety, ώe receiver of ώe mforma t ion needs to receive ώe exact digital dau file sent by ώe sender, toleratmg no bit errors or "loss" of dau. By definition, "lossy" compression and decompression on empincal signals defeat such steganographic meώods. (Pnor an, such as ώe previously noted Komatsu work, are ώe exceptions here.) The pnnciples of ώis technology can also be utdized as an exact form of steganography proper. It is suggested ώat such exact forms of steganography, wheώer ώose of pnor art or ώose of ώis technology, be combmed wiώ ώe relatively recent an of ώe "digital signature" and/or ώe DSS (digital signature standard) in such a way ώat a receiver of a given empincal dau file can first venfy ώat not one smgle bit of informauon has been altered m ώe received file, and ώus veπfy ώat ώe contained exact steganographic message has not been altered. The simplest way to use ώe pnnciples of this technology in an exact steganographic system is to utdize ώe previously discussed "designed" master noise scheme wherem ώe master snowy code is not allowed to contain zeros. Boώ a sender and a receiver of informauon would need access to BOTH ώe master snowy code signal AND ώe onginal unencoded oπginai signal. The receiver of ώe encoded signal merely subtracts ώe onginal signal givmg ώe difference signal and ώe techmques of simple polanty checking between ώe difference signal and ώe master snowy code signal, data sample to data sample, producing a ώe passed message a smgle bit at a time. Presumably dau samples wiώ values near ώe "rails" of ώe grey value range would be skipped (such as ώe values 0, 1,254 and 255 m 8-bit depώ empincal data). Statistical Steganography The need for ώe receiver of a steganographic embedded dau file to have access to ώe onginal signal can be removed by turning to what ώe mventor refers to as "sutisucal steganography." In ώis approach, ώe meώods of ώis technology arc applied as simple a prton rules governing ώe readmg of an empincal dau set searching for an embedded message. This meώod also could make good use of it combination wiώ pnor art meώods of veπfying ώe integπty of a data file, such as wiώ ώe DSS. (See, e.g., Walton, "Image Auώenucation for a Slippery New Age," Dr. Dobb's Journal, Apnl, 1995, p 18 for meώods of veπfying ώe sample-by-sample, bit-by-bit, integπty of a digital image.) Statistical steganography posits ώat a sender and receiver boώ have access to ώe same master snowy code signal. This signal can be entirely random and securely transmitted to boώ parties, or generated by a shared and securely transmitted lower order key which generates a larger quasi-random master snowy code signal. It is a pnoπ defined ώat 16 bit chunks of a message will be passed widun contiguous 1024 sample blocks of empirical data, and ώat the receiver will use dot product decodmg meώods as outlined in this disclosure The sender of ώe mformation pre-checks diat ώe dot produc t approach indeed produces ώe accurate 16 bit values (that is, ώe sender pre-checks ώat ώe cross-talk between ώe earner image and ώe message signal is not such ώat ώe dot product operation will produce an unwanted mversion of any of ώe 16 bits). Some fixed number of 1024 sample blocks are transmitted and ώe same number times 16 bits of message is ώerefore transmitted. DSS techmques can be used to veπfy ώe iπtegπry of a message when ώe transmitted dau is known to only exist in digital form, whereas internal checksum and enor correcting codes can be transmitted m situations where ώe dau may be subject to change and transformation in its transmission. In ώis latter case, tt is best to have longer blocks of samples for any given message content size (such as 10K samples for a 16 bit message chunk, purely as an example). Contmumg for a moment on ώe topic of enor coπectiπg steganography, it will be recognized ώat many decodmg techmques disclosed herein operate on ώe principle of distinguishing pixels (or bumps) which have been augmented by ώe encoded dau from ώose ώat have been diminished by ώe encoded dau. The distinguishing of ώese positive and negative cases becomes increasingly difficult as ώe delu values (e.g. ώe difference between an encoded pixel and ώe conespondmg onginal pixel) approach zero An analogous situation aπses in certain modem transmission techmques, wherem an ambiguous middle ground separates ώe two desired signal states (e g +/- 1) Enors deπvmg from incoπect lnterpreution of ώis middle ground arc sometimes termed "soft enors " Pnnciples from modem technology, and oώer technologies where such problems aπse, can likewise be applied to mitigation of such enors in ώe present context. One approach is to weight ώe "confidence" of each delta determination. If ώe pixei (bump) clearly reflects one sute or ώe oώer (e g +/- 1), its "confidence" is said to be high, and it is given a proportionately greater weightmg Conversely, if ώe pixel (bump) is relatively ambiguous in its mterpretauon, its confidence is commensurately lower, and it is given a proportionately lesser weightmg By weightmg ώe dau from each pixel (bump) in accordance wiώ its confidence vaiue, ώe effects of soft enors can be greatly reduced. The foregomg procedure, wiule ώeoretically simple, relies on weightings which are best determmed empiπcally Accordingly, such an approach is not πecessaπly straightforward An alternative approach is to assign confidence values not to interpreutions of mdividual pixels, but raώer to determination of bit values - eiώer from an image excerpt, or across ώe entire image. In such an arrangement, each decoded message bit is given a confidence value dependmg on ώe ambiguity (or not) of ώe image statistics by which its value was determmed Such confidence weightmg can also be used as a helpful adjunct wiώ oώer enor detecting/coπecting schemes. For example, in known enor correcting polynomials, ώe above-de t ailed weightmg parameters can be used to further hone polynomial-based discernment of an enor's location. THE "NOISE" IN VECTOR GRAPHICS AND VERY-LOW-ORDER INDEXED GRAPHICS The meώods of ώis disclosure generally posit die existence of "empincal signals, " which is anoώer way of saying signals which have noise contained wiώm ώem almost by definuion. There are t wo classes of 2 dimensional graphics which are not generally considered to have noise inherent in ώem: vector graphics and certain mdexed bit-mapped graphics. Vector graphics and vector graphic files are generally files which contain exact mstructions for how a computer or printer draws lmes, curves and shapes. A change of even one bit value m such a file might change a circle to a square, as a very crude example. In oώer words, ώere is generally no "inherent noise" to exploit withm ώese files Indexed bit-mapped graphics refers to unages which are composed of generally a small number of colors or grey values, such as 16 m ώe early CGA displays on PC computers. Such "very-low-order" bit-mapped images usually display graphics and cartoons, raώer ώan bemg used in ώe attempted display of a digital unage taken wiώ a camera of ώe natural world. These types of very-iow-order bit-mapped graphics also are generally not considered to contain "noise" m ώe classic sense of ώat term. The exception is where mdexed graphic files do mdeed attempt to depict natural imagery, such as wiώ ώe GIF (graphic interchange format of CompuServe), where ώe concept of "noise" is stdl quite valid and ώe pnnciples of this technology still quite valid. These latter forms often use diώeπng (similar to poinulhst paintings and color newspaper pπnt) to achieve near lifelike imagery This secuon concerns dus class of 2 dimensional graphics which traditionally do not contain "noise. " This section takes a bπef look at how ώe pnnciples of ώis technology can still be applied in some fashion to such creative matenal. The easiest way to apply ώe pnnciples of this technology to ώese "noiseless" graphics is to convert ώem mto a form which is amenable to ώe application of ώe pnnciples of this technology Many terms have been used m ώe mdustry for ώis conversion, including "πpping" a vector graphic (raster image processmg) such ώat a vector graphic file is convened to a grayscale pixel-based raster image Programs such as Photoshop by Adobe have such internal tools to convert vector graphic files mto RGB or greyscale digital unages. Once ώese files are in such a form, ώe pnnciples of ώis technology can be applied m a straightforward manner. Likewise, very- low-indexed bitmaps can be converted to an RGB digital image or an equivalent. In ώe RGB domam, ώe signatures can be applied to ώe three color channels m appropnate ratios, or ώe RGB unage can be simply converted mto a greyscale/chroma format such as "Lab" in Adobe's Photoshop software, and ώe signatures can be applied to ώe "Lightness channel" therein. Smce most of ώe distnbution media, such as videotapes, CD-ROMs, MPEG video, digital images, and pπnt are all tn forms which are amenable to ώe application ot ώe principles of ώis technology, ώis conversion from vector graphic form and very-low-order graphic foπn is often done in any event. Anoώer way to apply ώe pnnciples of ώis technology to vector graphics and very- low-order bitmapped graphics is to recognize ώat, mdeed, ώere are certain properties to ώese mherent graphic -.formats which - to ώe eye - appear as noise. The pπmary example is ώe borders and contours between where a given Ime or figure is drawn or not drawn, or exactly where a bit-map changes from green to blue. In most cases, a human viewer of such graphics will be keenly aware of any attempts to "modulate signature signals" via ώe detailed and methodical changmg of ώe precise contours of a graphic ob j ect Nevertheless, such encodmg of ώe signatures is mdeed possible The distraction between ώis approach and ώat disclosed in ώe bulk of ώis disclosure is ώat now ώe signatures must ultimately deπve from what already exists in a given graphic, raώer ώan bemg purely and separately created and added mto a signal This disclosure pomts out ώe possibdities here πoneώeiess. The basic idea is to modulate a contour, a touch nght or a touch left, a touch up or a touch down, in such a way as to communicate an N-bit idenuficauon word. The locations of ώe changes contours would be contained in a an analogous master noise image, ώough now ώe noise would be a record of random spatial shifts one direction or anoώer, perpendicular to a given contour. Bit values of ώe N-bit identification word would be encoded, and read, usmg ώe same poiaπry checking meώod between ώe applied change and ώe change recorded in ώe master noise image. PLASTIC CREDIT AND DEBIT CARD SYSTEMS BASED ON THE PRINCIPLES OF THE TECHNOLOGY Growth in ώe use of plastic credit cards, and more recently debit cards and ATM cash cards, needs little introduction. Nor does ώere need to be much discussion here about ώe long history of fraud and dlicit uses of ώese financial instruments. The development of ώe credit card hologram, and its subsequent forgery development, nicely serves as a histoπc example of ώe give and take of plastic card secuπty measures and fraudulent countermeasures. This section will concern itself wiώ how ώe pnnciples of this technology can be realized m an alternative, highly fraud-proof yet cost effective plastic card-based financial network. A basic list of desired features for an ubiquitous plastic economy might be as follows 1) A given plastic financial card is completely impossible to forge; 2) An attempted forged card (a "look-alike") cannot even function wiώm a transaction setting; 3) Intercepted electronic transactions by a would-be thief would not in any way be useful or re-useable; 4) In ώe event of physical ώeft of an actual valid card, ώere are stdl formidable obstacles to a ώief usmg ώat card; and 5) The overall economic cost of implemenution of ώe financial card network is equal to or less ώan ώat of ώe cunent mtemational credit card networks, i.e , ώe fully loaded cost per transaction is equal to or less ώan ώe cunent norm, allowing for higher profit margins to ώe implementors of ώe networks Apart from item 5, which would require a detailed analysis of ώe engineering and social issues mvolved wiώ an all out implementation strategy, ώe following use of ώe pnnciples of ώis technology may well achieve ώe above list, even item 5 Figs. 22 ώrough 26, along wiώ ώe ensuing wπtten matenal, collectively outline what is refened to ra Fig. 26 as "The Negligible-Fraud Cash Card System. " The reason ώat ώe fraud-prevention aspec t s of ώe system are highlighted ra ώe title is ώat fraud, and ώe concomitant lost revenue ώerefrom, is apparently a central problem in today's plastic card based economies. The differential advantages and disadvanuges of ώis system relative to current systems will be discussed after an illustrative embodimen t is presented. Fig. 22 dlustrates ώe basic unforgeabie plastic card which is quite umque to each and every user. A digital image 940 is taken of ώe user of ώe card. A computer, which is hooked mto ώe central accounting network, 980, depicted m Fig. 26, receives ώe digital image 940, and after processmg it (as wdl be descπbed surrounding Fig. 24) produces a final rendered image which is ώen printed out onto ώe personal cash card 950 Also depicted m Fig. 22 is a straightforward idenuficauon marking, m ώis case a bar code 952, and opuonal position fiducials which may assist m simplifymg ώe scanning tolerances on ώe Reader 958 depicted ra Fig. 23. The short story is ώat ώe personal cash card 950 actually contains a very large amount of informauon umque to ώat particular card. There are no magnetic stπps mvolved, ώough ώe same pnnciples can certainly be applied to magnetic stπps, such as an implanted magnetic noise signal (see earlier ώscussion on ώe "fingerprinting" of magnetic stπps m credit cards; here, ώe fingerprinting would be prominent and proactive as opposed to passive) In any event, ώe umque mformation withm ώe image on ώe personal cash card 950 is stored along wiώ ώe basic account information m a central accounting network, 980, Fig. 26. The basis for unbreakable secuπty is ώat duπng transactions, ώe central network need only query a small fraction of ώe total mformation contained on ώe card, and never needs to query ώe same precise information on any two transactions. Hundreds if not thousands or even tens of ώousands of umque and secure "transaction tokens" are contained wiώm a single personal cash card Would-be pirates who went so far as to pick off transmissions of eiώer encrypted or even unencrypted transactions would find ώe informauon useless thereafter. This is in marked distraction to systems which have a smgle complex and complete "key" (generally encrypted) which needs to be accessed, m its entirety, over and over again. The personal cash card on ώe oώer hand contains ώousands of separate and secure keys which can be used once, wiώin πulliseconds of time, ώen forever ώrown away (as it were). The central network 980 keeps track of ώe keys and knows which have been used and which haven't. Fig. 23 depicts what a typical point-of-sale reading device, 958, might look luce. Clearly, such a device would need to be manufacturable at costs well in Ime wiώ, or cheaper ώan, cunent cash register systems, ATM systems, and credit card swipers Not depicted in Fig 23 are ώe innards of ώe optical scannmg, image processmg, and data communications components, which would simply follow normal engineering design meώods carrymg out ώe functions ώat are to be descπbed henceforth and are well wiώm ώe capability of artisans in ώese fields. The reader 958 has a numeπc punch pad 962 on it, showmg ώat a normal personal identification number system can be combmed wiώ ώe overall design of dus system addmg one more conventional layer of secuπty (generally after a ώeft of ώe physical card has occurred). It should also be pointed out ώat ώe use of ώe picture of ώe user is anoώer strong (and increasingly common) secuπty feature mtended to ώwaπ after-ώeft and illicit use. Functional elements such as ώe optical window, 960, are shown, mimicking ώe shape of ώe card, doubling as a centering mechanism for ώe scanning. Also shown is ώe dau Ime cable 966 presumably connected eiώer to a propπetor's central merchant computer system or possibly directly to ώe central network 980 Such a reader may also be attached directly to a cash register which performs ώe usual tallying of purchased items. Perhaps overtoil on secuπty would be ώe construction of ώe reader, 958, as a type of Faraday cage such ώat no electronic signals, such as ώe raw scan of ώe card, can emanate from ώe unit. The reader 958 does need to contain, preferably, digital signal processmg umts which wdl assist m swiftly calculating ώe dot product operations descπbed henceforth. It also should contain local read-only memory which stores a multitude of spaual patterns (ώe orώogonal patterns) which wdl be utdized in ώe "recogmtion" steps oudmed ra Fig. 25 and its discussion. As related in Fig. 23, a consumer usmg ώe plastic card merely places ώeir card on ώe window to pay for a transaction. A user could choose for ώemselves if ώey want to use a PIN number or not. Approval of ώe purchase would presumably happen withm seconds, provided ώat ώe signal processmg steps of Fig. 25 are properly implemented wiώ effectively parallel digital processmg hardware. Fig. 24 takes a bπef look at one way to process ώe raw digital image, 940, of a user mto an unage wiώ more useful mformation content and uniqueness It should be cieariy pomted out ώat ώe raw digital unage itself could in fact be used m ώe followmg meώods, but ώat placmg in additional orώogonal patterns mto ώe unage can significantly mcrease ώe overall system. (Orώogonal means ώat. if a given pattern is mulπplied by anoώer orώogonal pattern, ώe resultmg number is zero, where "multiplication of patterns" is meant ra ώe sense of vector dot products; ώese are all familiar terms and concepts in ώe art of digital unage processmg). Fig. 24 shows ώat ώe computer 942 can, after inteπogating ώe raw unage 970, generate a master snowy unage 972 which can be added to ώe raw image 970 to produce a yet-more umque unage which is ώe unage ώat is printed onto ώe actual personal cash card, 950 The overall effect on ώe unage is to "textunze" ώe image. In ώe case of a cash card, ravisibdiry of ώe master snowy pattern is not as much of a requirement as wiώ commercial imagery, and one of ώe only cnteπa for keepmg ώe master snowy image somewhat lighter is to not obscure ώe image of ώe user. The central network, 980, stores ώe final processed image in ώe record of ώe account of ώe user, and it is ώis umque and securely kept image which is ώe earner of the highly secure "ώrow-away transaction keys " This image will ώerefore be 'made available" to all duly connected porat-of-saie locations m ώe overall network. As will be seen, none of ώe porat-of-sale locations ever has knowledge of ώis image, ώey merely answer queπes from ώe central network. Fig 25 steps through a typical transaction sequence. The figure is laid out via inden t a t ions , where ώe first column are steps performed by ώe poiπt-of-sale readmg device 958, ώe second column has information transmission steps communicated over ώe dau ime 966, and ώe ώird column has s t eps t aken by ώe central network 980 which has ώe secured mformation about ώe user's account and ώe user's umque personal cash card 950 Though ώere is some parallelism possible in ώe implemenuuon of ώe steps, as is normally practiced m ώe engineering implemenuuon of such systems, ώe steps are nevertheless laid out accordmg to a general linear sequence of events. Step one of Fig 25 is ώe standard "scanning" of a personal cash card 950 wiώm ώe optical window 960 This can be performed usmg linear optical sensors which scan ώe window, or via a two dimensional optical detector anay such as a CCD. The resultmg scan is digitized mto a grey scale image and stored m an image frame memory buffer such as a "framegrabber," as is now common in ώe designs of optical imaging systems. Once ώe card is scanned, a first image processmg step would probably be locatmg ώe four fiducial center pomts, 954, and usmg ώese four pomts to guide all further image processmg operations (I e ώe four fiducials "register" ώe conespondmg patterns and barcodes on ώe personal cash card). Next, ώe barcode ID number would be extracted usmg common barcode readmg image processmg meώods. Generally, ώe user's account number would be determined in ώis step Step two of Fig. 25 is ώe optional typing in of ώe PIN number Presumably most users would opt to have ώis feature, except ώose users who have a hard time remembering such dungs and who are convmced ώat no one wdl ever steal their cash card Step diree of Fig. 25 entads connecting ώrough a data Ime to ώe central accounting network and dorag ώe usual commumcations handshakmg as is common in modem-based commumcations systems. A more sophisucated embodiment of ώis system would obviate ώe need for standard phone lmes, such as ώe use of optical fiber data links, but for now we can assume it is a garden vaπety belltone phone Ime and ώat ώe reader 958 hasn't forgotten ώe phone number of ώe central network. After basic commumcations are established, step four shows ώat ώe porat-of-sale location transmits ώe ID number found m step 1 , along wiώ probably an encrypted version of ώe PIN number (for added secuπty, such as usmg ώe ever more ubiquitous RSA encryption meώods), and appends ώe basic mformation on ώe merchant who operates ώe point-of-sale reader 958, and ώe amount of ώe requested transaction m monetary umts. Step five has ώe central network readmg ώe ID number, routmg ώe information accordingly to ώe actual memory location of ώat user's account, thereafter veπfying ώe PIN number and checking ώat ώe account balance is sufficient to cover ώe transaction. Along ώe way, ώe central network also accesses ώe merchant's account, checks ώat it is valid, and readies it for an anticipated credit. Step six begins wiώ ώe assumption ώat step five passed all counts. If step five didn't, ώe exit step of sending a NOT OK back to ώe merchant is aot depicted. So, if everything checks out, ώe central network generates twenty four sets of sixteen numbers, where ail numbers are mutually exclusive, and in general, ώere wdl be a large but quite definitely finite range of numbers to choose from. Fig. 25 posits ώe range being 64K or 65536 numbers. It can be any practical number, actually. Thus, set one of ώe twenty four sets might have ώe numbers 23199, 54142, 11007, 2854, 61932, 32879, 38128, 48107, 65192, 522, 55723, 27833, 19284, 39970, 19307, and 41090, for example. The next set would be similarly random, but ώe numbers of set one would be off limits now, and so on ώrough ώe twenty four sets. Thus, ώe central network would send (16x24x2 bytes) of numbers or 768 bytes. The actual amount of numbers can be determined by engineering optimization 0 f secuπty versus transmission speed issues. These random numbers are actually indexes to a set of 64K universally a pπoπ defined orthogonal patterns which are well known to boώ ώe central network and are permanendy stored in memory in all of ώe point-of-sale readers. As will be seen, a would-be ώief s knowledge of ώese patterns is of no use. Step seven ώen transmits ώe basic "OK to proceed" message to ώe reader, 958, and also sends ώe 24 sets of 16 random mdex numbers. Step eight has ώe reader receiving and storing all ώese numbers. Then ώe reader, usmg its local microprocessor and custom designed high speed digital signal processmg circuitry, steps ώrough ail twenty four sets of numbers wiώ ώe intention of derivmg 24 distract floating pomt numbers which it will send back to ώe centtal network as a "one tune key" against which ώe central network will check ώe veracity of ώe card's image. The reader does ώis by first adding togeώer ώe sixteen patterns mdexed by ώe sixteen random numbers of a given set, and ώen performing a common dot product operation between ώe resulting composite pattern and ώe scanned image of ώe card. The dot product generates a smgle number (which for simplicity we can call a floating pomt number). The reader steps ώrough all twenty four sets in like fashion, generating a unique stπng of twenty four floating point numbers. Step nine ώen has ώe reader transmitting ώese results back to ώe central network. Step ten ώen has ώe central network performing a check on ώese returned twenty four numbers, presumably doing its own exact same calculations on ώe stored image of ώe card ώat ώe central network has in its own memory. The numbers sent by ώe reader can be "normalized," meaning ώat ώe highest absolute value of ώe collective twenty four dot products can divided by itself (its unsigned value), so ώat brightness scale issues are removed. The resulting match between ώe returned values and ώe central network's calculated values will eiώer be well within given tolerances if ώe card is valid, and way off if ώe card is a phony or if ώe card is a crude reproduction. Step eleven ώen has ώe central network sending word wheώer or not ώe transacuon was OK, and letting ώe customer know ώat ώey can go home wiώ ώeir purchased goods. Step twelve ώen explicitly shows how ώe merchant's account is credited wiώ ώe transacuon amount. As already stated, ώe pπmary advantage of this plasnc card is to significantly reduce fraud, which apparently is a large cost to current systems. This system reduces ώe possibility of fraud only to those cases where ώe physical card is eiώer stolen or very carefully copied. In boώ of ώese cases, ώere still remains ώe PIN secuπty and ώe user picture secuπty (a known higher secuπty than low wage clerks analyzing signatures). Attempts to copy ώe card must be performed ώrough "temporary ώeft" of ώe card, and require photo-quality copying devices, not simple magneuc card swipers. The system is founded upon a modem 24 hour highly linked dau network. Illicit momtoπng of transactions does ώe momtoπng party no use wheώer ώe transmissions are encrypted or not. It will be appreciated ώat ώe foregomg approach to increasing ώe secuπty of transacuons involving credit and debit card systems is readily extended to any photograph-based identificauon system. Moreover, ώe pnnciples of ώe present technology may be applied to detect alterauon of photo ID documents, and to generally enhance ώe confidence and secuπty of such systems. In ώis regard, reference is made to Fig. 28, which depicts a photo-ID card or document 1000 which may be, for example, a passport, visa, permanent resident card ("green card"), dnver' s license, credit card, government employee identification, or a pπvate mdustry identificauon badge. For convenience, such photograph-based identificauon documents will be collectively refened to as photo ID documents. The photo ID document includes a photograph 1010 ώat is attached to ώe document 1000. Printed, human-readable information 1012 is incorporated m ώe document 1000, adjacent to ώe photograph 1010. Machine readable informauon, such as ώat known as "bar code" may also be mcluded adjacent to ώe photograph. Generally, ώe photo ID document is constructed so ώat tampering wiώ ώe document (for example, swappmg ώe original photograph wiώ anoώer) should cause noticeable damage to ώe card. Nevertheless, skilled forgerers are able to eiώer alter existing documents or manufacture fraudulent photo ID documents in a manner ώat is extremely difficult to detect. As noted above, ώe present technology enhances ώe secuπty associated wiώ ώe use of photo ID documents by supplementing ώe photographic image wiώ encoded information (which informauon may or may not be visually percepuble), ώereby facilitating ώe conelanon of ώe photographic unage wiώ oώer information concerning ώe person, such as ώe printed information 1012 appearing on ώe document 1000 In one embodiment, ώe photograph 1010 may be produced from a raw digital unage to which is added a master snowy image as descnbed above in connection wiώ Figs. 22-24. The above-descnbed central network and point-of-sale reading device (which device, in ώe present embodiment, may be considered as a point-of-entry or point-of-security photo ID reading device), would essentially carry out ώe same processing as described wiώ ώat embodiment, including ώe central network generation of unique numbers to serve as indices to a set of defined orthogonal patterns, ώe associated dot product operation carried out by ώe reader, and ώe comparison wiώ a similar operation carried out by ώe central network. If ώe numbers generated from ώe dot product operation carried out by ώe reader and ώe central network match, in ώis embodiment, ώe network sends ώe OK to ώe reader, indicating a legitimate or unaltered photo ID document. In anoώer embodiment, ώe photograph component 1010 of ώe identification document 1000 may be digitized and processed so ώat ώe photographic image ώat is incorporated into ώe photo ID document 1000 conesponds to ώe "distributable signal" as defined above. In ώis instance, therefore, ώe photograph includes a composite, embedded code signal, imperceptible to a viewer, but carrymg an N-bit identification code. It will be appreciated ώat ώe identification code can be extracted from ώe photo usmg any of ώe decoding techniques described above, and employing eiώer universal or custom codes, depending upon ώe level of security sought. It wdl be appreciated ώat ώe information encoded into ώe photograph may correlate to, or be redundant wiώ, ώe readable information 1012 appearing on ώe document. Accordingly, such a document could be auώenticated by placing ώe photo ID document on a scanning system, such as would be available at a passport or visa control pomt. The local computer, which may be provided wiώ ώe universal code for extracting ώe identification information, displays ώe extracted information on ώe local computer screen so ώat ώe operator is able to confirm ώe conelation between ώe encoded information and ώe readable information 1012 carried on ώe document. It wdl be appreciated ώat ώe information encoded wiώ ώe photograph need not necessarily conelate wiώ oώer information on an identification document. For example, ώe scanning system may need only to confirm ώe existence of ώe identification code so ώat ώe user may be provided wiώ a "go" or "no go" indication of wheώer ώe photograph has been tampered wiώ. It will also be appreciated ώat ώe local computer, using an encrypted digital communications line, could send a packet of information to a central verification facility, which thereafter returns an encrypted "go" or "no go" indication. In anoώer embodiment, it is contemplated ώat ώe identification code embedded in ώe photograph may be a robust digital image of biomerric dau, such as a fingerprint of ώe card bearer, which image, after scanning and display, may be employed for comparison wiώ ώe actual fingerprint of ώe bearer in very high security access points where on-ώe-spot fingeφrint recognition systems (or retinal scans, etc.) are employed. It will be appreciated ώat ώe information embedded in ώe photograph need not be visually hidden or steganographically embedded. For example, ώe photograph incorporated into ώe identification card may be a composite of an image of ώe individual and one-, or two-dimensional bar codes The bar code information would be subject to conventional optical scannmg techmques (including internal cross checks) so ώat ώe information denved from ώe code may be compared, for example, to ώe informauon prin t ed on ώe identification document It is also contemplated ώat ώe photographs of ID documents cunendy m use may be processed so ώat information conelated to ώe mdividual whose unage appears ra ώe photograph may be embedded In ώis regard, ώe reader's attention is directed to ώe foregomg portion of ώis descnption entitled "Use in Printing, Paper, Documents, Plastic -Coated Idenufication Cards, and Oώer Mateπal Where Global Embedded Codes Can Be Imprinted, " wherem ώere is descπbed numerous approaches to modulation of physical media ώat may be treated as "signals" amenable to application of ώe present technology pnnciples NETWORK LINKING METHOD USING INFORMATION EMBEDDED IN DATA OBJECTS THAT HAVE INHERENT NOISE The diagram of Fig. 27 illustrates ώe aspect of ώe technology diat provides a network linking meώod usmg informauon embedded m dau objects ώat have inherent noise. In one sense, this aspect is a network navigauon system and, more broadly, a massively distnbuted indexing system ώat embeds addresses and radices directly widun dau objects themselves. As noted, ώis aspect is particularly well- adapted for establishing hot links wiώ pages presented on ώe World Wide Web (WWW) A given data object effectively contains boώ a graphical represenuuon and embedded URL address. As m previous embodiments, this embeddmg is earned out so ώat ώe added address mformation does not affect ώe core value of ώe object so far as ώe creator and audience are concerned. As a consequence of such embedding, only one class of dau objects is present raώer ώan ώe two classes (data ob j ect and discrete header file) ώat are attendant wiώ traditional WWW links. The advantages of reducing a hot-linked data object to a smgle class were mentioned above, and are elaborated upon below In one embodiment of ώe technology, ώe World Wide Web is used as a pre-existing hot Irak based network The common apparatus of this system is networked computers and computer monitors displaying ώe results of interactions when connected to ώe web This embodiment of ώe technology contemplates steganographically embeddmg URL or oώer address-type information direcdy mto images, videos, audio, and oώer forms of dau objects ώat are presented to a web site visitor, and which have "gray scale" or "continuous tones" or "gradations" and, consequently, mherent noise As noted above, ώere are a vaπety of ways to realize basic steganographic implementations, all of which could be employed m accordance wiώ ώe present technology Wiώ particular reference to Fig 27, images, quasi-continuous tone graphics, multimedia video and audio dau are cunently ώe basic buddrag blocks of many sues 1002, 1004 on ώe World Wide Web Such data will be hereafter collectively refened to as creative data files or data objects. For illustrative purposes, a continuous-tone graphic data object 1006 (diamond nng wiώ background) is depicted in Fig. 27 Web site tools - boώ ώose ώat develop web sites 1008 and ώose ώat allow browsmg ώem 1010 - routmely deal wiώ ώe vanous file formats m which ώese data objects are packaged. It is already common to distπbute and disseminate ώese dau objects 1006 as widely as possible, often wiώ ώe hope on a creator's part to sell ώe products represented by ώe objects or to advertise creative services (e.g., an exemplary photograph, wiώ an 800 phone number displayed wiώm it, promoting a photographer's skdls and service). Usmg ώe meώods of this technology, individuals and organizauons who create and disseminate such dau objects can embed an address Irak ώat leads nght back to ώeir own node on a network, ώeir own site on ώe WWW A user at one site 1004 needs merely to pomt and click at ώe displayed object 1006 The software 1010 identifies ώe object as a hot link object. The software reads ώe URL address ώat is embedded wiώm ώe object and routes ώe user to ώe linked web site 1002, just as if ώe user had used a conventional web link. That linked site 1002 is ώe home page or network node of ώe creator of ώe object 1006, which creator may be a manufacturer The user at ώe first site 1004 is ώen presented wiώ, for example, an order form for purchasmg ώe product represented by ώe object 1006. It will be appreciated ώat ώe creators of objects 1006 havmg embedded URL addresses or radices (which objects may be refened to as "hot objects") and ώe manufacturers hopmg to advertise ώeir goods and services can now spread ώeir creative content like dandelion seeds m ώe wind across ώe WWW, knowing ώat embedded wiώin ώose seeds are links back to ώeir own home page. It is contemplated ώat ώe object 1006 may include a visible icon 1012 (such as ώe exemplary "HO" abbreviation shown m Fig. 27) incorporated as pan of ώe graphic. The icon or oώer subtle mdicia would appπse ώe user ώat ώe object is a hot object, carrymg ώe embedded URL address or oώer information ώat is accessible via ώe software 1010. Any human-perceptible indicium (e.g., a short musical tone) can serve ώe purpose of appπsmg ώe user of ώe hot object. It is contemplated, however, ώat no such indicium is required. A user's tπal- and-enor approach to clickmg on a dau object havmg no embedded address wdl merely cause ώe software to look for, but not find, ώe URL address. The automation process mherent in ώe use of ώis aspect of ώe technology is very advantageous Web software and web site development tools simply need to recognize ώis new class of embedded hot links (hot objects), operating on ώem ra real time. Conventional hot links can be modified and supplemented simply by "uploading" a hot object mto a web site repository, never requiπng a web site programmer to do a dung oώer ώan basic monitoring of traffic. A meώod of impiementmg ώe above descπbed functions of ώe present technology generally involves ώe steps of (1) creating a set of standards by which URL addresses are steganographically embedded wiώin unages, video, audio, and oώer forms of dau objects; and (2) designing web site development tools and web software such ώat ώey recognize ώis new type of data ob j ect (ώe hot ob j ect), ώe tools bemg designed such ώat when ώe objects are presented to a user and ώat user pom t s and clicks on such an object, ώe user's software knows how to read or decode ώe steganographic informa t ion and route ώe user to ώe decoded URL address. The foregomg portions of ώis descnpuon detailed a steganographic implemenuuon (see, generally, Fig. 2 and ώe text associated ώerewiώ) ώat is readily adapted to implement ώe present technology. In ώis regard, ώe oώerwise conventional site development tool 1008 is enhanced to raelude, for example, ώe capability to encode a bit-mapped image file wiώ an identification code (URL address, for example) accordmg to ώe present technology. In ώe present embodiment, it is contemplated ώat ώe commercial or transaction based hot objects may be steganographically embedded wiώ URL addresses (or oώer information) usmg any of ώe umversal codes described above. The foregoing portions of ώis description also detaded a technique for reading or decoding steganographically embedded information (see, generally, Fig. 3 and ώe text associated ώerewiώ) ώat is readily adapted to implement ώe present technology. In this regard, ώe oώerwise conventional user software 1010 is enhanced to include, for example, ώe capabdity to analyze encoded bit-mapped files and extract ώe identification code (URL address, for example). While an illustrative implemenution for steganographically embedding information on a data object has been descπbed, one of ordinary skill will appreciate ώat any one of ώe multitude of avadable steganographic techmques may be employed to cany out ώe funcuon of ώe present embodiment. It will be appreciated ώat ώe present embodiment provides an immediate and common sense mechanism whereby some of ώe fundamental budding blocks of ώe WWW, namely unages and sound, can also become hot links to oώer web sites. Also, ώe programimng of such hot objects can become fully automated merely ώrough ώe distnbution and availabdity of images and audio. No real web sue programming is required. The present embodiment provides for ώe commercial use of ώe WWW in such a way ώat non-programmers can easily spread ώeir message merely by creating and distπbutiπg creative content (herein, hot objects). As noted, one can also transition web based hot links ώemselves from a more arcane text based interface to a more natural image based interface. Encapsulated Hot Link File Format As noted above, once steganographic meώods of hot link navigation take hold, ώen, as new file formats and transmission protocols develop, more traditional meώods of "header-based" information attachment can enhance ώe basic approach built by a steganographic-based system. One way to begin extendmg ώe steganographic based hot link meώod back into ώe more traditional header-based meώod is to define a new class of file format which could effectively become ώe standard class used m network navigation systems. It will be seen ώat objects beyond unages, audio and ώe like can now become "hot objects" , including text files, mdexed graphic files, computer programs, and ώe like. The encapsulated hot link (EHL) file format simply is a small shell placed around a large range of pre-existmg file formats. The EHL header mformation takes only ώe first N bytes of a file, followed by a full and exact file m any kind of mdustry standard format. The EHL super-header merely attaches ώe conect file type, and ώe URL address or oώer mdex information associating ώat object to oώer nodes on a network or oώer databases on a network. It is possible ώat ώe EHL format could be ώe meώod by which ώe steganographic meώods are slowly replaced (but probably never completely) The slowness pays homage to ώe idea ώat file format standards often take much longer to create, implement, and get everybody to actually use (if at all). Again, ώe idea is ώat an EHL-iike format and system budt around it would bootstrap onto a system setup based on steganographic meώods. Self Extracting Web Obtects Generally speaking, ώree classes of dau can be steganographically embedded in an object: a number (e.g. a seπal or idenuficauon number, encoded in bmary), an alphanumenc message (e.g. a human readable name or telephone number, encoded m ASCII or a reduced bit code), or computer mstructions (e.g. JAVA or HTML mstructions) The embedded URLs and ώe like detailed above begm to explore ώis ώird class, but a more detailed exposition of ώe possibdities may be helpful Consider a typical web page, shown m Fig 27A. It may be viewed as including ώree basic components: unages (#1 - #6), text, and layout. Applicant's technology can be used to consolidate this mformation mto a self-extracting object, and regenerate ώe web page from this object. In accordance wiώ this example, Fig. 27B shows ώe images of ώe Fig. 27A web page fitted togeώer mto a smgle RGB mosaiced unage. A user can perform ώis operauon manually usmg existmg unage processmg programs, such as Adobe's Photoshop software, or ώe operauon can be automated by a suitable software program. Between certain of ώe unage ules in ώe Fig. 27B mosaic are empty areas (shown by cross- hatching). This mosaiced unage is ώen steganographically encoded to embed ώe layout mstructions (e.g. HTML) and ώe web page text ώerem. In ώe empty areas ώe encodmg gam can be maximized smce ώere is no image dau to corrupt. The encoded, mosaiced image is ώen JPEG compressed to foπn a self extractmg web page object. (JPEG compression is used m ώis example as a lingua franca of image representa t ions Anoώer such candidate is ώe GIF file format. Such formats are supported by a vaπety of sof t ware t ools and languages, making ώem well suited as "common earners" of embedded information Oώer image representations can of course be used.) These objects can be exchanged as any oώer JPEG images When ώe JPEG file is opened , a suitably programmed computer can detect ώe presence of ώe embedded mformation and ex t rac t ώe layou t data and text. Among oώer mformation, ώe layout data specifies where ώe images forming ώe mosaic are to be located in ώe final web page. The computer can follow ώe embedded HTML l nsrruc t ions t o recreate ώe onginal web page, complete wiώ graphics, text, and links to oώer URLs. If ώe self extracting web page object is viewed by a conventional JPEG viewer, it does not self- extract. However, ώe user will see ώe logos and artwork associated wiώ ώe web page (wiώ noise-like "groutmg" between certain of ώe unages). Artisans will recognize ώat ώis is in stark contras t t o viewing of oώer compressed dau objects (e.g. PKZIP files and self extractmg text archives) which typically appear totally unintelligible unless fully extracted. (The foregoing advantages can largely be achieved by placing ώe web page text and layout instructions m a header file associated wiώ a JPEG-compressed mosaiced unage file. However, mdustry standardization of ώe header formats needed to make such systems practical appears difficult, if not impossible.) Palettes of Steganographically Encoded Images Once web images wiώ embedded URL mformation become widespread, such web images can be collected mto "palettes" and presented to users as high level navigation tools. Navigauon is effected by clickmg on such unages (e.g logos for different web pages) raώer ώan clickmg on textual web page names. A suiubly programmed computer can decode ώe embedded URL mformation from ώe selected image, and establish ώe requested connection. In one embodiment, self-extraction of ώe above-descπbed web page objects automatically generates ώumbnad unages conespondmg to ώe extracted pages (e g represenutive logos), which are ώen stored m a subdirectory in ώe computer's file system dedicated to collecting such ώumbnads. In each such ώumbπad is embedded a URL, such as ώe URL of ώe extracted page or ώe URL of ώe site from which ώe self-extracting object was obtamed. This subdirectory can be accessed to display a palette of navigational ώumbnads for selection by ώe user. Specific Ex-implft of a Computer Svstem Linked bv Using Information in Data Obiects The present mvention allows a digital watermark to be embedded directly mto photographs, video, computer images, auώo, and oώer forms of creative property The range of applications is amazmgly wide, and includes dungs like video and movie content watermarking for proof of ownership and tracking, watermarking music and audio content, even texturing physical mateπals such as auto parts. As discussed above, ώis watermark is imperceptible to ώe eye, and imperceptible to ώe ear, but a computer analysis can read ώe watermark and discover a message it cames. The watermark is repeated diroughout ώe property. It is robust, and typically survives multiple generations of copying, modification, printing, scannmg, and compression. A watermark may carry a copynght notice, a umque seπal number, a Transaction ID, as well as oώer application specific dau. Conceptually, ώe example below is made wiώ respect to a watermark embedded by "PictureMarc" which illustrates ώe idea by carrying a unique creator id, and a set of image attributes. This creator id corresponds to complete contact information available through an on¬ line service, MarcCentre. Technically, the watermark is 128 bits long and has a data pay load of 76 bits, although ώe invention is not restricted to any particular watermark size. In this example, it is short so ώat it can be repeated many times through an image, making it possible to find the message even from a portion of the original image. Other approaches can include data such as an image serial number or a Transaction ID in ώe watermark message. It is also possible to add a person's name or other such data (to a computer, it is all data), but one application is focused on embedding a more structured message. First, to accommodate a text string, ώere would be a tendency to push for longer messages. Long messages result in not being able to fit very many copies of ώe message in an image, and thus ώe watermark becomes less robust. Also, just putting a name in an image is much like the credit line in a magazine: A. Jones tells who the person is, but not how to contact the person. With a watermark system available on line, a user can get complete and up to date contact details from each Creator ED through ώe on-line service. Accordingly, it is preferable to associate persistent digital information wiώ an unage. The core software has been designed to enable working with vendors or large customers who may want to develop specific watermark message types for special applications such as embedding the film speed and exposure in ώe image, or serializing images in a large collection for internal tracking and management.. One approach to implementing this embodiment involves "PictureMarc", which includes a writer portion to embed watermarks and a reader portion to find them and read ώeir contents. This bundle is integrated into Adobe Photoshop® 4.0 (Adobe Photoshop is a trademark of Adobe Systems, Inc.) as a plugin extension, and from the end-user's perspective is free. A free reader will also be available through a web site. PictureMarc for Photoshop is supported on Apple Macintosh and Microsoft Windows platforms. PictureMarc supports embedding a Creator ID and image attributes in an image. PictureMarc also has the ability to carry a unique Image ID or even a Transaction ID in ώe watermark. These formats will support tracking images boώ to a collection, and to ώe person and terms that were in place when the image sold. The watermark reader will accept and read ail watermark formats. As an example, ώe first step is to read an image from a CD-ROM, download a file to a local computer over ώe Internet or convert a hardcopy image, such as a photograph, into digital by scanning ώe image with any of ώe common image scanners.. Then start an image manipulation program ώat supports watermarking, and load ώe image file from a file selection menu. Select the menu item to add a watermark, and save ώe watermarked image into a file. To recover a watermarked image from a digital image on, say, a PC, just as in ώe case of embedding a watermark, convert ώe image to a suitable computer file format, scanning it in if needed. Then start either an image manipulation tool supporting watermarking, or start a stand¬ alone reader. Now open ώe image. In ώe case of a prefened embodiment image manipulation tool, the tool will automatically check for a watermark when ώe digital image file is opened, copied to ώe system clipboard or ώe image is scanned into ώe computer. If present, a copyright symbol is added to ώe title or status bar, or some other signal is provided to ώe user. By selecting a read watermark menu option, a user can discover ώe contents of ώe watermark. From ώe results dialog, a user can click on ώe Web Lookup button to retrieve complete contact information from MarcCentre, a network WWW on-line service. The application can be carried out with at least two kinds of watermarks. The first is a public watermark, which can be read by any suitably configured reader. This is ώe type of watermark supported by PictureMarc-Its purpose is communication. The second is a private watermark. Private watermarks are associated with a secret key used at ώe time ώe watermark is embedded in an image. Only ώe person with ώe secret key can find and read a private watermark. Private watermarks are used in proof of ownership applications. Both a public and a private watermark can coexist in an image. If a user attempts to read a watermark and none is ώere, the reader portion will simply report that a watermark was not found in the image. If a user attempts to watermark an image already watermarked, ώe user will be informed ώere is already a watermark in ώe imaee. and will be prevented from adding another watermark. In creating a composite image, the user is first told about ώe presence of a watermark in each watermarked original (assuming the use of a tool supporting watermarks). This addresses ώe first order problem of communicating copyright. The watermark will not prevent creating a composite work, in that a watermarked image, or a portion thereof, can be used as an element of a composite image. However, if the composite image contains multiple watermarked elements, ώe reader portion will tend to read ώe watermark from the most dominant piece. However, if the user suspects a component to be watermarked, or is simply interested in ώe source of an image element, ώe user can check for the watermark in that element by highlighting ώe area in question, and invoking ώe read watermark function. This provides a tool to inspect work to discover sources of individual elements. Copyright hierarchies in composited works and partnering wiώ companies addressing rights management can all be handled in such a network application of a watermark. Preferably, applications which include watermarking, such as Photoshop 4.0, support automatic detection ("autodetect"). This means ώat whenever an image is opened or scanned into ώe application, a quick check is done to determine if a watermark is present. If it is present, a visual indication, such as a copyright symbol, is added to ώe title or status bar. The watermark placed by PictureMarc is a 128 bits long, wiώ a data payload of 76 bits. The remaining bits are dedicated to header and control information, and enor detection and correction. However, ώe bandwidth can be increased and consequently ώe data payload can also be increased. The detect process looks for the presence of a watermark, which completes in a couple of seconds, usually under 3 seconds regardless of image size, and ώe typical time to perform a full read watermark operation is under 15 seconds. PictureMarc supports all ώe file formats of ώe host application, since it works with pixels in each of ώe major color schemes (CMYK, RGB, LAB, Grayscale). In ώe case of PictureMarc for Photoshop, all of ώe Photoshop file formats are supported. Note ώat ώe watermark itself carries version and message type information, much like a communications message. PictureMarc has been designed to support upward compatibility. Any suitably configured reader will be able to read previous versions of ώe watermark. If a reader finds a newer version watermark which it does not support, the user will be notified and told to download ώe most recent reader. In ώe present application of PictureMarc, an image generally needs to be at least 256x256 pixels to carry a watermark. Often a watermark can be found in patches as small as 100 pixels square. Arbitrarily large images can be watermarked, constrained only by available memory and disk space. The watermark does not add data to ώe image file, since it only makes very small changes to ώe luminance of pixels. This can change ώe size of a compressed image by a few percent, but ώe size of uncompressed images, such as bitmaps (.BMPs), are unchanged. While a watermark survives compression, to watermark an already compressed image, it is preferable to either work from ώe original, or uncompress, watermark, and recompress ώe image. Future versions will likely support adding watermarks directly to ώe JPEG compressed file. PictureMarc supports ώe automation features provided by Adobe Photoshop 4.0. This means ώat repetitive watermarking operations can be automated now. In oώer applications, support can be provided for unattended batch processing of images, aimed to support ώe needs of image distributors, especially ώose companies looking to watermark images as ώey are distributed with unique Transition IDs. As to ώe use of a network on line service, an image creator or distributor subscribes to the MarcCentre, on-line locator service. Subscribing includes providing a set of contact information to be made available when one of a user's watermarked images is found. Contact details include name, address, phone number, specialty, intermediary (e.g., organization, stock agency, representative, etc.) representing ώe creator's work and so on. When subscribing, ώe user is given a unique Creator ID. The user provides ώis Creator ID to a copy of PictureMarc, one time when first using ώe software-based system. This Creator ID links all of ώe user's images to facilitate contact details. Information can be queried via ώe Internet, or via a fax-back service. Querying is preferably free. A subscription to MarcCentre is priced to include unlimited access to contact information by anyone finding watermarked images. A Creator ID in the image leverages the power of a network accessible daubase, and lets the image creator communicate much more information ώan could fit in ώe image. In essence, we are creating a copyright communication system. Watermark detection in programs such as Photoshop is automatic. The Creator ID in the image, along with ώe image attributes, uniquely identify the image creator or distributor. The contact information corresponding to ώe Creator ID is always up to date, no matter how long ago a watermarked image was distributed. To make ώis copyright communication system work, we need unique ids which correspond to ώe appropriate image creator contact information. If ώere is no network connection for PictureMarc, an equivalent approach can be used: contact information can be communicated via a fax-back service. This can be carried out by punching in ώe Creator ID on a touch-tone keypad with telephony, and ώe resulting contact details for ώat image creator will be sent by fax. Contact details will be stored on a robust server being hosted by an Internet provider. The Web site is responsible for maintaining ώe locator service and ensuring ώat information is available. Individual image creators are responsible for maintaining ώeir own contact details, keeping them current as information changes. This ensures ώat all of ώe images distributed point to up-to-date information about ώe creator and ώe creator's work. Data in ώe Web site repository is stored in a way ώat when a user enter contact details, the user has control over which pieces of information are viewable by viewers.- Thus, for example, ώe user can designate whether to exclude his or her address, phone number, and other specific information. It is helpful for users to receive regular reports, generated by the Web page site software, about ώe number of people querying a specific user's information. These can be sent to the user via e-mail or fax, to help communicate the activity concerning watermarked images. Before turning to a specific embodiment of ώe present invention, it is important to stress the general applicability of embedding a watermark, ώen reading it to obtain Hatn and ώen using the data to link to a database-preferably via a computer network. This is applicable to all media for which ώis technology is useful. This includes watermarking a photograph or a series of watermarks in a series of images (e.g., television, video), in sound or oώer analog signals (e.g., recordings, cellular telephone or oώer broadcasts), and other applications indicated herein. In any of these applications, while the watermark can contain self-identifying data, a centralized repository of watermark data facilitates broad, efficient usage. Thus, ώe following specific description of a preferred embodiment of a central repository, accessible by a network, is intended to be illustrative of ώe same kind of an approach for oώer applications and media suitable for watermarking. And it is to be understood ώat while ώe following discussion is made in ώe context of image processing, ώe same general approach can be used for handling watermarks in acoustic or oώer applications. Also, while ώe Adobe Photoshop application is used to illustrate ώe generally applicable concepts, it should be appreciated ώat ώe present invention can be used in connection wiώ oώer such products from oώer vendors, as well as in stand alone form. Fig. 43 illustrates an application of the invention using such an application as Adobe Photoshop 4.0, equipped with a plug in version of ώe digital watermarking technology discussed herein and illustrated as "PictureMarc," running on a digital electrical computer. The computer running ώe Adobe application is adapted for communication over a network (such as ώe WWW) with another digital electrical computer, running ώe MarcCentre Web Page computer program. At step 1 of Fig. 43 a creator of a digitized photographic image, ώe "user" of ώe Adobe Photoshop, commences by obtaining a Creator ID. Such data is created and stored in a central repository designated in Fig. 43 as MarcCentre Locator Service, which is connectable to PictureMarc via Internet or fax. At step 2 of Fig. 43, the user creates and embeds a watermark in ώe digital image. At step 3 of Fig. 43, ώat image can be stored and/or printed, possibly subsequent to distribution over a computer system such as the Internet or WWW. When the image bearing ώe watermark is used in digital form, at step 4 of Fig. 43, that image is examined for a watermark by a computer running ώe PictureMarc computer program. Preferably, ώe examination is conducted automatically (rather than selectively) upon opening an image, e.g., upon locating the image on a computer screen clipboard. If a watermark is detected, at step 5 of Fig. 43, PictureMarc communicates with the MarcCentre Locator Service to look up creator information and present a WWW page to the user. The means by which a user obtains a Creator ID in step 1 of Fig. 43 is elaborated in Figs. 44, 45, and 46, which represent screen images of MarcCentre, a Web Page locatable on ώe WWW. Introductory information is set forth in Fig. 44. Note ώe mention of a single mouse click. This feature refers to a hot button, such as a copyright symbol in a title bar of a screen, which permits someone using ώe Web Page to obuin information about ώe image ώat was pre¬ specified by the creator. (A hot button (pointer to information stored in a MarcCentre database, which can in turn have a pointer to another database, such as ώat accessed by means of ώe network or WWW.) Figs. 45 and 46 show sequential screens of a "form" to be completed by computer entries. This information is stored in a MarcCentre database. Note too ώat ώe information entered to complete ώe form include a fee, and ώe fee account for the user is also stored in a MarcCentre database. After ώe form has been completed and stored and ώe fee has been secured, a Creator ED is issued by ώe MarcCentre service. The means by which a user with a Creator ID embeds a registered watermark in step 2 of Fig. 43 is elaborated in Figs. 47, 48, and 49. The Photoshop software uses a filter to call up ώe "Digimarc" PictureMarc software and select options to embed a watermark. Bolded bars on ώe screen of Fig. 47 illustrate a selection being made for a digital image in the background. Other information on ώe screen is typical for a windows application, ώe Adobe Photoshop application, and or generally known information about digital images, e.g., 100% shows ώe scale of ώe image, "RGB" indicates the image format, etc. Fig. 48 illustrates a "Dialog Box" used in associating information in connection with the embedding of ώe watermark. In a portion of ώe screen, ώe Copyright Information is specified, including ώe Creator ID, the Type of Use (either Restricted or Royalty Free), Adult Content, and a Personalize option. A demonstration ID for use by those who do not have a registered Creator ID is "PictureMarc Demo." The Adult Content feature permits embedding in ώe watermark data, preferably content-indicative data. Here ώe data is used for identifying ώat the photograph contains adult content, and thus a subsequent use of ώe photograph can be restricted. However, an alternative is to include other data or other content- identifying data, such as cataloging information, in ώe watermark itself. Cataloging information permits subject matter searching in a collection of photographs. Anoώer feature shown in Fig. 48 is a slidable scale for adjusting the intensity of the watermark. The more visible ώe watermark, ώe more durable the watermark is. Watermark durability (visibility) is also indicated by a number shown on ώe screen. Once ώe screen has been completed, if it is "OK", ώe user can click on ώis button and ώereby embed ώe watermark. Otherwise, the user can select to cancel and ώereby abort ώe process ώat would have led to ώe watermarking. Selecting the Personalize option of Fig. 48 calls ώe screen illustrated in Fig. 49, which is used to obtain a personalized, registered Creator ED. After ώe Creator ED is entered, selection of ώe Register option launches a WWW browser directed by ώe URL address shown entered on the screen to ώe MarcCentre registration page. In a preferred embodiment of step 4 of Fig. 43, each time an image is opened, scanned in, or copied to ώe clipboard by any application, ώe image is automatically tested for a watermark, as illustrated in Fig. 50. This "autodetect" approach generates a signal indicative of the result of the test, for example, one or more copyright symbols shown in ώe title page of ώe screen in Fig. 50. Automatic checking for a watermark takes less computer time ώan the alternative of selectively reading ώe watermark, which is illustrated in Figs. 51 and 52. However, ώe selective approach is less comprehensive in monitoring ώe use of watermarked images, which is disadvantageous if ώe application is configured to automatically report ώe detection back to the MarcCentre. In Fig. 50, ώe copyright symbol in ώe title area of the photograph is ώe previously mentioned hot button. The copyright symbol in ώe lower left of the screen can alternatively or also be a hot button. Figs. 51 and 52 elaborate the alternative approach to step 4 of Fig. 43 in which an image is opened and examined for a watermark. Preferably, each time an image is opened, there is an automatic check for a watermark. In any case. Fig. 51 is similar to ώe screen of Fig. 47, except ώat a selection is shown to read for a watermark. Fig. 52 shows a screen ώat is be produced in response to ώe reading a watermark. This screen provides ώe Creator ED of ώe owner and / or distributor of ώis image, as well as whether use of ώis image is restricted or royalty free. Note ώe Web Lookup button, which can be selected to launch a WWW browser directed by ώe URL address shown entered on the screen to ώe MarcCentre Web Page, and obtains ώe data in ώe conesponding MarcCentre daubase corresponding to ώe Creator ED. Fig. 53 illustrates a screen produced from selecting ώe Web Lookup feature of Fig. 53. This screen provides ώe contact information for ώe image creator, including phone number, E-mail address and Web links. Note the selectable buttons at ώe bottom of ώe screen illustrated in Fig. 53, including buttons ώat take ώe user to promotional offers, allow ώe user to download additional software, subscribe to ώe MarcCentre service, update contact information, search for a creator or provide feedback on ώe MarcCentre service. Selecting ώe Creator Search option leads to ώe screen shown in Fig. 54 This self-explanatory screen enables a user to obtain contact information about a particular creator by entering ώe Creator ED. In a variation on ώe above-described approach, extended information is embedded in ώe watermark and subsequently detected, as respectively illustrated in Figs.' 55 and 56. Such a feature would be inserted as an additional filter in Fig. 47. In Fig. 55., ώe dialog box permits inputting an Organization ED, an Item ED, and a Transaction ED. An Organization ID identifies an organization of other intermediary representing ώe creator and/or auώorized for distributing ώe photograph and licensing rights thereto, such as a photo stock house. An Item ED identifies the photograph in a collection, such as a CD ROM. A Transaction ID identifies ώe specific transaction ώat is authorized (e.g., publication one time only in a newspaper), which permits detection of an authorized usage. The other feature on ώe screen, The Bump Size specification, pertains to ώe smallest unit of data, in ώe horizontal and vertical directions, that ώe watermark algorithms operate on. The extended information embedded in ώe watermark can be detected in a manner analogous to that described with respect to Fig. 48, except that ώe additional information is shown as illustrated in Fig. 56. An alternative to a "plug in" embodiment is a stand alone version, which is exemplified in ώe next sequence of figures. As discussed above, for any application that involves a watermark, preferably there is an autodetect capability, and Fig. 57 illustrates a screen from a stand alone image reader wiώ a clip board triggered by merely putting an image on ώe clipboard. In the Clipboard Viewer window of Fig. 57, a watermark detected on ώe image can be used to signal the user by one of several methods that can be used individually or in a combination. In one method, a title bar icon or some oώer bar or icon alternates between "normal" and "watermark detected" states periodically (for example, at one second intervals). Another approach is to use other state changes, like having ώe title bar or a task bar alternate between highlighted and normal states periodically. Still another method is to have ώe detection initiate a special dialog box display, as illustrated in Fig. 58. The Read button in ώe special dialog box (triggered by a click of a mouse, like oώer buttons) causes reading and display of ώe watermark information in ώe image. The Close button ends ώe special dialog. (So as not to interfere wiώ image processing, it can be convenient to show ώe special dialog box for a limited amount of time, and a small window therein can show ώe time remaining before ώe special dialog box disappears.) Buttons shown on the screen include one resembling a file folder to open a file; the next button is for copying a selected image from a first application to ώe clipboard; ώe next button is for pasting an image from ώe clipboard to a second application; and ώe last is for help menus. Fig. 59 shows ώe image after pasting it from ώe clipboard, in ώe course of image processing. The Digimarc Reader - Image 1 window pops up on ώe screen to permit clicking the Web lookup button, which launches the user's web browser to result in a display of the creator's web page on ώe MarcCentre Web site, with ώe rest following as above. Note again ώat ώe foregoing approach is representative, and it can be modified for anoϋier media in which a watermark is used. For example, instead of an image, a sound can be used. POTENTIAL USE OF THE TECHNOLOGY fN THE PROTECTION AND CONTROL OF SOFTWARE PROGRAMS The illicit use, copying, and reselling of software programs represents a huge loss of revenues to ώe software industry at large. The prior art meώods for attempting to mitigate ώis problem are very broad and will not be discussed here. What will be discussed is how ώe principles of ώis technology might be brought to bear on ώis huge problem. It is entirely unclear whether ώe tools provided by ώis technology will have any economic advantage (all things considered) over ώe existing countermeasures both in place and contemplated. The state of technology over ώe last decade or more has made it a general necessity to deliver a full and complete copy of a software program in order for ώat program to function on a user's computer. In effect, $X were invested in creating a software program where X is large, and ώe entire fruits of ώat development must be delivered in its entirety to a user in order for ώat user to gain value from ώe software product. Fortunately ώis is generally compiled code, but the point is ώat this is a shaky distribution situation looked at in ώe abstract. The most mundane (and harmless in ώe minds of most perpetrators) illicit copying and use of the program can be performed raώer easily. This disclosure offers, at first, an abstract approach which may or may not prove to be economical in ώe broadest sense (where ώe recovered revenue to cost ratio would exceed that of most competing meώods, for example). The approach expands upon ώe meώods and approaches already laid out in ώe section on plastic credit and debit cards. The abstract concept begins by positing a "large set of unique patterns," unique among themselves, unique to a given product, and unique to a given purchaser of that product. This set of patterns effectively contains ώousands and even millions of absolutely unique "secret keys" to use ώe cryptology vernacular. Importantly and distinctly, ώese keys are non-deterministic, ώat is, ώey do not arise from singular sub- 1000 or sub-2000 bit keys such as with the RSA key based systems. This large set of patterns is measured in kilobytes and Megabytes, and as mentioned, is non-deterministic in nature. Furthermore, still at the most abstract level, these patterns are fully capable of being encrypted via standard techniques and analyzed within ώe encrypted domain, where the analysis is made on only a small portion of the large set of patterns, and ώat even in the worst case scenario where a would-be pirate is monitoring the step-by-step microcode instructions of a microprocessor, ώis gathered information would provide no useful information to ώe would-be pirate. This latter point is an important one when it comes to "implementation security" as opposed to "innate security" as will be briefly discussed below. So what could be ώe differential properties of ώis type of key based system as opposed to, for example, ώe RSA cryptology meώods which are already well respected, relatively simple, etc. etc? As mentioned earlier, ώis discussion is not going to attempt a commercial side-by-side analysis. Instead, we'll just focus on ώe differing properties. The main distinguishing features fall out in ώe implementation realm (the implementation security). One example is that in single low-bit-number private key systems, ώe mere local use and re-use of a single private key is an inherently weak link in an encrypted transmission system. ["Encrypted transmission systems" are discussed here in ώe sense ώat securing ώe paid-for use of software programs will in ώis discussion require de facto encrypted communication between a user of ώe software and ώe "bank" which allows ώe user to use ώe program; it is encryption in ώe service of electronic financial transactions looked at in another light.] Would-be hackers wishing to defeat so-called secure systems never attack ώe fundamental hard-wired security (ώe innate security) of the pristine usage of ώe methods, ώey attack ώe implementation of ώose meώods, centered around human nature and human oversights. It is here, still in ώe abstract, ώat ώe creation of a much larger key base, which is itself non-deterministic in nature, and which is more geared toward effectively ώrow-away keys, begins to "idiot proof the more historically vulnerable implementation of a given secure system. The huge set of keys is not even comprehensible to ώe average holder of ώose keys, and their use of those keys (i.e., ώe "implementation" of those keys) can randomly select keys, easily throw ώem out after a time, and can use ώem in a way ώat no "eavesdropper" will gain any useful information in ώe eavesdropping, especially when well within a millionth of ώe amount of time that an eavesdropper could "decipher" a key, its usefulness in ώe system would be long past. Turning ώe abstract to the semi-concrete, one possible new approach to securely delivering a software product to ONLY ώe bonafϊde purchasers of that product is the following. In a mass economic sense, this new method is entirely founded upon a modest rate realtime digital connectivity (often, but not necessarily standard encrypted) between a user's computer "" network and ώe selling company's network. At first glance ώis smells like trouble to any good marketing person, and indeed, ώis may throw ώe baby out with the baώwater if by trying to recover lost revenues, you lose more legitimate revenue along ώe way (all part of the bottom line analysis). This new meώod dictates ώat a company selling a piece of software supplies to anyone who is willing to take it about 99.8% of its functional software for local storage on a user's network (for speed and minimizing transmission needs). This "free core program" is entirely unfunctional and designed so ώat even ώe craftiest hackers can't make use of it or "decompile it" in some sense. Legitimate activation and use of ώis program is performed purely on a instruction-cycle-count basis and purely in a simple very low overhead communications basis between ώe user's network and ώe company's network. A customer who wishes to use ώe product sends payment to ώe company via any of the dozens of good ways to do so. The customer is sent, via common shipment methods, or via commonly secured encrypted data channels, ώeir "huge set of unique secret keys." If we were to look at ώis large set as if it were an image, it would look just like ώe snowy images discussed over and over again in oώer parts of ώis disclosure. (Here, ώe "signature" is ώe image, rather ώan being imperceptibly placed onto anoώer image). The special nature of ώis large set is ώat it is what we might call "ridiculously unique" and contains a large number of secret keys. (The "ridiculous" comes from the simple math on ώe number of combinations ώat are possible wiώ, say 1 Megabyte of random bit values, equaling exactly ώe number ώat "all ones" would give, ώus 1 Megabyte being approximately 10 raised to ώe ~2,400,000 power, plenty of room for many people having many throwaway secret keys). It is important to re-emphasize ώat ώe purchased entity is literally: productive use of ώe tool. The marketing of ώis would need to be very liberal in its allotment of this productivity, since per-use payment schemes notoriously turn off users and can lower overall revenues significantly. This large set of secret keys is itself encrypted using standard encryption techniques. The basis for relatively higher "implementation security" can now begin to manifest itself. Assume that ώe user now wishes to use ώe software product. They fire up ώe free core, and the free core program finds ώat ώe user has installed ώeir large set of unique encrypted keys. The core program calls the company network and does the usual handshaking. The company network, knowing ώe large set of keys belonging to that bonafide user, sends out a query on some simple set of patterns, almost exactly ώe same way as described in the section on ώe debit and credit cards. The query is such a small set of ώe whole, ώat the inner workings of ώe core program do not even need to decrypt ώe whole set of keys, only certain parts of ώe keys, thus no decrypted version of ώe keys ever exist, even within ώe machine cycles on ώe local computer itself. As can be seen, ώis does not require ώe "signatures within a picture" meώods of ώe main disclosure, instead, ώe many unique keys ARE ώe picture. The core program intenogates ώe keys by performing certain dot products, ώen sends ώe dot products back to ώe company's network for verification. See Fig. 25 and ώe accompanying discussion for typical details on a verification transaction. Generally encrypted verification is sent, and the core program now "enables" itself to perform a certain amount of instructions, for example, allowing 100,000 characters being typed into a word processing program (before another unique key needs to be transmitted to enable another 100,000). In ώis example, a purchaser may have bought ώe number of instructions which are typically used within a one year period by a single user of ώe word processor program. The purchaser of ώis product now has no incentive to "copy" ώe program and give it to ώeir friends and relatives. All of the above is well and fine except for two simple problems. The first problem can be called "the cloning problem" and ώe second "the big broώer problem." The solutions to ώese two problems are intimately linked. The latter problem will ultimately become a purely social problem, with certain technical solutions as mere tools not ends. The cloning problem is ώe following. It generally applies to a more sophisticated pirate of software rather, ώan the currently common "friend gives their distribution CD to a friend" kind of piracy. Crafty-hacker "A" knows that if she performs a system-state done of the "enabled" program in its entirety and installs ώis clone on anoώer machine, ώen ώis second machine effectively doubles ώe value received for ώe same money. Keeping ώis clone in digital storage, hacker "A" only needs to recall it and reinstall ώe clone after ώe first period is run out, thus indefinitely using ώe program for a single payment, or she can give ώe clone to ώeir hacker friend "B" for a six-pack of beer. One good solution to this problem requires, again, a raώer well developed and low cost real time digital connectivity between user site and company enabling network. This ubiquitous connectivity generally does not exist today but is fast growing through ώe Internet and ώe basic growth in digital bandwidth. Part and parcel of the "enabling" is a negligible communications cost random auditing function wherein the functioning program routinely and irregularly performs handshakes and verifications with ώe company network. It does so, on average, during a cycle which includes a rather small amount of productivity cycles of ώe program. The resulting average productivity cycle is in general much less ώan ώe raw total cost of ώe cloning process of ώe overall enabled program. Thus, even if an enabled program is cloned, ώe usefulness of ώat instantaneous clone is highly limited, and it would be much more cost effective to pay ώe asking price of ώe selling company than to repeat the cloning process on such short time periods. Hackers could break ώis system for fun, but certainly not for profit. The flip side to ώis arrangement is ώat if a program "calls up" ώe company's network for a random audit, ώe allotted productivity count for ώat user on ώat program is accounted for, and ώat in cases where bonafide payment has not been received, ώe company network simply withholds its verification and ώe program no longer functions. We're back to where users have no incentive to "give ώis away" to friends unless it is an explicit gift (which probably is quite appropriate if ώey have indeed paid for it: "do anything you like with it, you paid for it"). The second problem of "big broώer" and ώe intuitively mysterious "enabling" communications between a user's network and a company's network would as mentioned be a social and perceptual problem ώat should have all manner of potential real and imagined solutions. Even with ώe best and objectively unbeatable anti-big-broώer solutions, ώere will still be a hard-core conspiracy theory crowd claiming it just ain't so. With ώis in mind, one potential solution is to set up a single program registry which is largely a public or non-profit institution to handling and coordinating ώe realtime verification networks. Such an entity would ώen have company clients as well as user clients. An organization such as ώe Software Publishers Association for example, may choose to lead such an effort. Concluding ώis section, it should be re-emphasized ώat ώe meώods here outlined require a highly connected distributed system, in other words, a more ubiquitous and inexpensive Internet ώan exists in mid 1995. Simple trend extrapolation would argue ώat ώis is not too far off from 1995. The growth rate in raw digital communications bandwidth also argues that the above system might be more practical, sooner, ώan it might first appear. (The prospect of interactive TV brings with it ώe promise of a fast network linking millions of sites around ώe world.) USE OF CURRENT CRYPTOLOGY METHODS IN CONJUNCTION WITH THIS TECHNOLOGY It should be briefly noted ώat certain implementations of ώe principles of ώis technology probably can make good use of current cryptographic technologies. One case in point might be a system whereby graphic artists and digital photographers perform realtime registration of ώeir photographs with the copyright office. It might be advantageous to send ώe master code signals, or some representative portion thereof, directly to a third party registry. In ώis case, a photographer would want to know ώat ώeir codes were being transmitted securely and not stolen along ώe way. In this case, certain common cryptographic transmission might be employed. Also, photographers or musicians, or any users of this technology, may want to have reliable time stamping services which are becoming more common. Such a service could be advantageously used in conjunction wiώ ώe principles of ώis technology. DETAILS ON THE LEGITIMATE AND ILLEGITIMATE DETECTION AND REMOVAL OF INVISIBLE SIGNATURES In general, if a given entity can recognize the signatures hidden within a given set of empirical data, that same entity can take steps to remove those signatures. In practice, the degree of difficulty between ώe former condition and ώe latter condition can be made quite large, fortunately. On one extreme, one could posit a software program which is generally very difficult to "decompile" and which does recognition functions on empirical data. This same bit of software could not generally be used to "strip" ώe signatures (wiώout going to extreme lengths). On the oώer hand, if a hacker goes to the trouble of discovering and understanding the "public codes" used within some system of data interchange, and ώat hacker knows how to recognize the signatures, it would not be a large step for that hacker to read in a given set of signed data and create a data set with ώe signatures effectively removed. In this latter example, interestingly enough, ώere would often be telltale statistics that signatures had been removed, statistics which will not be discussed here. These and oώer such attempts to remove ώe signatures we can refer to as illicit attempts. Cunent and past evolution of the copyright laws have generally targeted such activity as coming under criminal activity and have usually placed such language, along wiώ penalties and enforcement language, into ώe standing laws. Presumably any and all practitioners of ώis signature technology will go to lengths to make sure ώat ώe same kind of a) creation, b) distribution, and c) use of ώese kinds of illicit removal of copyright protection mechanisms are criminal offenses subject to enforcement and penalty. On ώe other hand, it is an object of this technology to point out ώat through ώe recognition steps outlined in ώis disclosure, software programs can be made such ώat the recognition of signatures can simply lead to ώeir removal by inverting ώe known signatures by ώe amount equal to ώeir found signal energy in ώe recognition process (i.e., remove ώe size of ώe given code signal by exact amount found). By pointing ώis out in ώis disclosure, it is clear ώat such software or hardware which performs ώis signature removal operation will not only (presumably) be criminal, but it will also be liable to infringement to ώe extent ώat it is not properly licensed by ώe holders of the (presumably) patented technology. The case of legitimate and normal recognition of ώe signatures is straightforward. In one example, ώe public signatures could deliberately be made marginally visible (i.e. ώeir intensity would be deliberately high), and in ώis way a form of sending out "proof comps" can be accomplished. "Comps" and "proofs" have been used in ώe photographic industry for quite some time, where- a degraded image is purposely sent out to prospective customers so ώat ώey might evaluate it but not be able to use it in a commercially meaningful way. En ώe case of ώis technology, increasing ώe intensity of ώe public codes can serve as a way to "degrade" the commercial value intentionally, then through hardware or software activated by paying a purchase price for ώe material, ώe public signatures can be removed (and possibiy replaced by a new invisible tracking code or signature, public and/or private. MONITORING STATIONS AND MONITORING SERVICES Ubiquitous and cost effective recognition of signatures is a central issue to the broadest proliferation of the principles of ώis technology. Several sections of ώis disclosure deal with this topic in various ways. This section focuses on ώe idea ώat entities such as monitoring nodes, monitoring stations, and monitoring agencies can be created as part of a systematic enforcement of ώe principles of ώe technology. In order for such entities to operate, ώey require knowledge of ώe master codes, and they may require access to empirical data in its raw (unencrypted and untransformed) form. (Having access to original unsigned empirical data helps in finer analyses but is not necessary.) Three basic forms of monitoring stations fall out directly from ώe admittedly arbitrarily defined classes of master codes: a private monitoring station, a semi-public, and a public. The distinctions are simply based on ώe knowledge of ώe master codes. An example of the fully private monitoring station might be a large photographic stock house which decides to place certain basic pattems into its distributed material which it knows ώat a truly crafty pirate could decipher and remove, but it thinks this likelihood is ridiculously small on an economic scale. This stock house hires a part-time person to come in and randomly check high value ads and odier photography in ώe public domain to search for ώese relatively easy to find base pattems, as well as checking photographs ώat stock house staff members have "spotted" and think it might be infringement material. The part time person cranks through a large stack of ώese potential infringement cases in a few hours, and where ώe base pattems are found, now a more thorough analysis takes place to locate ώe original image and go through ώe full process of unique identification as outlined in ώis disclosure. Two core economic values accrue to ώe stock house in doing this, values which by definition will outweigh ώe costs of ώe monitoring service and ώe cost of ώe signing process itself. The first value is in letting ώeir customers and the world know that they are signing their material and that the monitoring service is in place, backed up by whatever statistics on ώe ability to catch iπfringers. This is the deterrent value, which probably will be the largest value eventually. A general pre-requisite to this first value is ώe actual recovered royalties derived from ώe monitoring effort and its building of a track record for being formidable (enhancing ώe first value). The semi-public monitoring stations and ώe public monitoring stations largely follow ώe same pattern, although in these systems it is possible to actually set up third party services which are given knowledge of the master codes by clients, and ώe services merely fish through ώousands and millions of "creative property" hunting for ώe codes and reporting ώe results to ώe clients. ASCAP and BMI have "lower tech" approaches to ώis basic service. A large coordinated monitoring service using ώe principles of ώis technology would classify its creative property supplier clients into two basic categories, those ώat provide master codes ώemselves and wish ώe codes to remain secure and unpublished, and ώose ώat use generally public domain master codes (and hybrids of ώe two, of course). The monitoring service would perform daily samplings (checks) of publicly available imagery, video, audio, etc., doing high level pattern checks with a bank of supercomputers. Magazine ads and images would be scanned in for analysis, video grabbed off of commercial channels would be digitized, audio would be sampled, public Internet sites randomly downloaded, etc. These basic data streams would ώen be fed into an ever-churning monitoring program which randomly looks for pattern matches between its large bank of public and private codes, and ώe data material it is checking. A small sub-set, which itself will probably be a large set, will be flagged as potential match candidates, and these will be fed into a more refined checking system which begins to attempt to identify which exact signatures may be present and to perform a more fine analysis on the given flagged material. Presumably a small set would then fall out as flagged match material, owners of ώat material would be positively identified and a monitoring report would be sent to ώe client so ώat ώey can verify ώat it was a legitimate sale of ώeir material. The same two values of ώe private monitoring service outlined above apply in ώis case as well. The monitoring service could also serve as a formal bully in cases of a found and proven infringement, sending out letters to infringing parties witnessing ώe found infringement and seeking inflated royalties so that ώe infringing party might avoid the more costly alternative of going to court. METHOD FOR EMBEDDING SUBLIMINAL REGISTRATION PATTERNS INTO IMAGES AND OTHER SIGNALS The very notion of reading embedded signatures involves the concept of registration. The underlying master noise signal must be known, and its relative position needs to be ascertained (registered) in order to initiate ώe reading process itself (e.g. ώe reading of ώe l 's and 0's of the N-bit identification word). When one has access to ώe original or a thumbnail of the unsigned signal, ώis registration process is quite straightforward. When one doesn't have access to ώis signal, which is often ώe case in universal code applications of ώis technology, then different methods must be employed to accomplish this registration step. The example of pre-marked photographic film and paper, where by definition ώere will never be an "unsigned" original, is a perfect case in point of ώe latter. Many earlier sections have variously discussed ώis issue and presented certain solutions. Notably, the section on "simple" universal codes discusses one embodiment of a solution where a given master code signal is known a priori, but its precise location (and indeed, it existence or non-existence) is not known. That particular section went on to give a specific example of how a very low level designed signal can be embedded within a much larger signal, wherein ώis designed signal is standardized so ώat detection equipment or reading processes can search for ώis standardized signal even ώough its exact location is unknown. The brief section on 2D universal codes went on to point out ώat this basic concept could be extended into 2 dimensions, or, effectively, into imagery and motion pictures. Also, ώe section on symmetric pattems and noise patterns outlined yet another approach to the two dimensional case, wherein ώe nuances associated with two dimensional scale and rotation were more explicitly addressed. Therein, ώe idea was not merely to determine ώe proper orientation and scale of underlying noise patterns, but to have information transmitted as well, e.g., ώe N rings for ώe N-bit identification word. This section now attempts to isolate ώe sub-problem of registering embedded patterns for registration's sake. Once embedded patterns are registered, we can ώen look again at how ώis registration can serve broader needs. This section presents yet another technique for embedding pattems, a technique which can be referred to as "subliminal digital graticules". "Graticules" - oώer words such as fiducials or reticles or hash marks could just as well be used -conveys the idea of calibration marks used for ώe purposes of locating and/or measuring someώing. In ώis case, ώey are employed as low level pattems which serve as a kind of gridding function. That gridding function itself can be a carrier of 1 bit of information, as in the universal 1 second of noise (its absence or presence, copy me, don't copy me), or it can simply find ώe orientation and scale of oώer information, such as embedded signatures, or it can simply orient an image or audio object itself. Figs. 29 and 30 visually summarize two related methods which illustrate applicant's subliminal digital graticules. As will be discussed, ώe meώod of Fig. 29 may have slight practical advantages over ώe method outlined in Fig. 30, but both methods effectively decompose the problem of finding ώe orientation of an image into a series of steps which converge on a solution. The problem as a whole can be simply stated as ώe following: given an arbitrary image wherein a subliminal digital graticule may have been stamped, ώen find ώe scale, rotation, and origin (offset) of ώe subliminal digital graticule. The beginning point for subliminal graticules is in defining what ώey are. Simply put, they are visual patterns which are directly added into oώer images, or as ώe case may be, exposed onto photographic film or paper. The classic double exposure is not a bad analogy, though in digital imaging ώis specific concept becomes rather stretched. These pattems will generally be at a very low brightness level or exposure level, such ώat when ώey are combined with "normal" images and exposures, they will effectively be invisible (subliminal) and just as ώe case with embedded signatures, ώey will by definition not interfere widi ώe broad value of ώe images to which ώey are added. Figs. 29 and 30 define two classes of subliminal graticules, each as represented in ώe spatial frequency domain, also known as the UV plane, 1000. Common two dimensional fourier transform algorithms can transform any given image into its UV plane conjugate. To be precise, the depictions in Figs. 29 and 30 are ώe magnitudes of ώe spatial frequencies, whereas it is difficult to depict ώe phase and magnitude which exists at every point. Fig. 29 shows the example of six spots in each quadrant along the 45 degree lines, 1002. These are exaggerated in this figure, in that these spots would be difficult to discern by visual inspection of the UV plane image. A rough depiction of a "typical" power spectrum of an arbitrary image as also shown, 1004. This power spectrum is generally as unique as images are unique. The subliminal graticules are essentially these spots. In ώis example, ώere are six spatial frequencies combined along each of the two 45 degree axes. The magnitudes of the six frequencies can be ώe same or different (we'll touch upon ώis refinement later). Generally speaking, ώe phases of each are different from ώe others, including ώe phases of one 45 degree axis relative to ώe oώer. Fig. 31 depicts ώis graphically. The phases in this example are simply randomly placed between PI and -PI, 1008 and 1010. Only two axes are represented in Fig. 31 - as opposed to the four separate quadrants, since the phase of ώe miπored quadrants are simply PI/2 out of phase with ώeir miπored counterparts. If we turned up ώe intensity on this subliminal graticule, and we transformed ώe result into ώe image domain, ώen we would see a weave-like cross-hatching pattern as related in the caption of Fig. 29. As stated, ώis weave-like pattern would be at a very low intensity when added to a given image. The exact frequencies and phases of ώe spectral components utilized would be stored and standardized. These will become the "spectral signatures" ώat registration equipment and reading processes will seek to measure. Briefly, Fig. 30 has a variation on ώis same general ώeme. Fig. 30 lays out a different class of graticules in ώat ώe spectral signature is a simple series of concentric rings raώer than spots along ώe 45 degree axes. Fig. 32 ώen depicts a quasi-random phase profile as a function along a half-circle (the other haif of ώe circle ώen being PI/2 out of phase with the first half). These are simple examples and ώere are a wide variety of variations possible in designing ώe phase profiles and ώe radii of ώe concentric rings. The transform of this type of subliminal graticule is less of a "pattern" as with ώe weave-like graticule of Fig. 29, where it has more of a random appearance like a snowy image. The idea behind both types of graticules is ώe following: embed a unique pattern into an image which virtually always will be quite distinct from the imagery into which it will be added, but which has certain properties which facilitate fast location of the pattern, as well as accuracy properties such that when the pattern is generally located, its precise location and orientation can be found to some high level of precision. A corollary to the above is to design the pattern such that the pattern on average minimally interferes with the typical imagery into which it will be added, and has maximum energy relative to ώe visibility of the pattern. Moving on to the gross summary of how the whole process works, ώe graticule type of Fig. 29 facilitates an image processing search which begins by first locating ώe rotation axes of the subliminal graticule, then locating ώe scale of the graticule, then determining the origin or offset. The last step here identifies which axes is which of ώe two 45 degree axes by determining phase. Thus even if ώe image is largely upside down, an accurate determination can be made. The first step and ώe second step can both be accomplished using only ώe power spectrum data, as opposed to ώe phase and magnitude. The phase and magnitude signals can then be used to "fine tune" ώe search for ώe correct rotation angle and scale. The graticule of Fig. 30 switches ώe first two steps above, where ώe scale is found first, ώen the rotation, followed by precise determination of ώe origin. Those skilled in ώe art will recognize ώat determining ώese outstanding parameters, along two axes, are sufficient to fully register an image. The "engineering optimization challenge" is to maximize ώe uniqueness and brightness of the pattems relative to ώeir visibility, while minimizing ώe computational overhead in reaching some specified level of accuracy and precision in registration. In ώe case of exposing photographic film and paper, clearly an additional engineering challenge is the outlining of economic steps to get the pattems exposed onto ώe film and paper in ώe first place, a challenge which has been addressed in previous sections. The problem and solution as above defined is what was meant by registration for registration's sake. It should be noted that ώere was no mention made of making some kind of value judgement on whether or not a graticule is indeed being found or not. Clearly, ώe above steps could be applied to images which do not in fact have graticules inside ώem, ώe measurements ώen simply chasing noise. Sympathy needs to be extended to ώe engineer who is assigned ώe task of setting "detection thresholds" for ώese types of pattems, or any oώers, amidst the incredibly broad range of imagery and environmental conditions in which the pattems must be sought and verified. [Ironically, ώis is where ώe pure universal one second of noise stood in a previous section, and that was why it was appropriate to go beyond merely detecting or not detecting this singular signal, i.e. adding additional information planes]. Herein is where some real beauty shows up: in ώe combination of ώe subliminal graticules wiώ ώe now-registered embedded signatures described in other parts of this disclosure. Specifically, once a "candidate registration" is found - paying due homage to the idea ώat one may be chasing noise - then the next logical step is to perform a reading process for, e.g., a 64 bit universal code signature. As further example, we can imagine that 44 bits of the 64 bit identification word are assigned as an index of registered users -- serial numbers if you will. The remaining 20 bits are reserved as a hash code - as is well known in encryption arts - on ώe 44 bit identification code thus found. Thus, in one swoop, ώe 20 bits serve as ώe "yes, I have a registered image" or "no, I don't" answer. More importantly, perhaps, this allows for a system which can allow for maximum flexibility in precisely defining ώe levels of "false positives" in any given automated identification system. Threshold based detection will always be at ώe mercy of a pleώora of conditions and situations, ultimately resting on arbitrary decisions. Give me N com flips any day. Back on point, ώese graticule pattems must first be added to an image, or exposed onto a piece of film. An exemplary program reads in an arbitrarily sized digital image and adds a specified graticule to ώe digital image to produce an output image. In ώe case of film, ώe graticule pattern would be physically exposed onto ώe film eiώer before, during, or after exposure of ώe primary image. All of ώese meώods have wide variations in how ώey might be accomplished. The searching and registeπng of subliminal graticules is ώe more interesting and involved process. This section will first describe the elements of ώis process, culminating in ώe generalized flow chart of Fig. 37. Fig. 33 depicts ώe first major "search" step in ώe registration process for graticules of ώe type in Fig. 29. A suspect image (or a scan of a suspect photograph) is first transformed in its fourier representation using well known 2D FFT routines. The input image may look like ώe one in Fig. 36, upper left image. Fig. 33 conceptually represents the case where ώe image and hence the graticules have not been rotated, though the following process fully copes with rotation issues. After ώe suspect image has been transformed, ώe power spectrum of ώe transform is ώen calculated, being simply ώe square root of the addition of ώe two squared moduli, it is also a good idea to perform a mild low pass filter operation, such as a 3x3 blur filter, on ώe resulting DOWer SDeCtrum data. SO ώat later search stens rinn't πeprl inrrprlihlv fϊn< » ς nar p H β t» » n β TVι* » n the candidate rotation angles from 0 through 90 degrees (or 0 to PI/2 in radian) are stepped through. Along any given angle, two resultant vectors are calculated, the first is the simple addition of power spectrum values at a given radius along the four lines emanating from the origin in each quadrant. The second vector is the moving average of the first vector. Then, a normalized power " "profile is calculated as depicted in boώ 1022 and 1024, the difference being ώat one plot is along an angle which does not align wiώ ώe subliminal graticules, and ώe other plot does align. The normalization stipulates ώat ώe first vector is ώe numerator and ώe second vector is ώe denominator in ώe resultant vector. As can be seen in Fig. 33, 1022 and 1024, a series of peaks (which should be "six" instead of "five" as is drawn) develops when ώe angle aligns along its proper direction. Detection of ώese peaks can be effected by setting some threshold on ώe normalized values, and integrating ώeir total along ώe whole radial line. A plot, 1026, from 0 to 90 degrees is depicted in ώe bottom of Fig. 33, showing that ώe angle 45 degrees contains ώe most energy. In practice, ώis signal is often much lower ώan that depicted in ώis bottom figure, and instead of picking ώe highest value as the "found rotation angle," one can simply find ώe top few candidate angles and submit these candidates to ώe next stages in ώe process of determining the registration. It can be appreciated by those practiced in the art that ώe foregoing was simply a known signal detection scheme, and ώat ώere are dozens of such schemes ώat can ultimately be created or boπowed. The simple requirement of the first stage process is to whittle down ώe candidate rotation angles to just a few, wherein more refined searches can ώen take over. Fig. 34 essentially outlines ώe same type of gross searching in ώe power spectral domain. Here instead we first search for ώe gross scale of ώe concentric rings, stepping from a small scale through a large scale, rather than ώe rotation angle. The graph depicted in 1032 is the same normalized vector as in 1022 and 1024, but now the vector values are plotted as a function of angle around a semi-circle. The moving average denominator still needs to be calculated in ώe radial direction, rather than ώe tangential direction. As can be seen, a similar "peaking" in ώe normalized signal occurs when ώe scanned circle coincides with a graticule circle, giving rise to ώe plot 1040. The scale can ώen be found on ώe bottom plot by matching the known characteristics of ώe concentric rings (i.e. their radii) with the profile in 1040. Fig. 35 depicts ώe second primary step in registering subliminal graticules of the type in Fig. 29. Once we have found a few rotation candidates from ώe meώods of Fig. 33, we then take the plots of ώe candidate angles of the type of 1022 and 1024 and perform what the inventor refers to as a "scaled kernel" matched filtering operation on those vectors. The scaled kernel refers to the fact that the kernel in this case is a known non-harmonic relationship of frequencies, represented as the lines with x's at the top in 1042 and 1044, and that the scale of these frequencies is swept ώrough some pre-determined range, such as 25% to 400% of some expected scale at 100%. The matched filter operation simply adds the resultant multiplied values of ώe scaled frequencies and ώeir plot counterparts. Those practiced in ώe art will recognize ώe similarity of ώis operation with ώe very well known matched filter operation. The resulting plot of the matched filter operation will look someώing like 1046 at the bottom of Fig. 35. Each candidate angle from ώe first step will generate its own such plot, and at ώis point ώe highest value of all of ώe plots will become our candidate scale, and ώe angle corresponding to ώe highest vaiue will become our primary candidate rotation angle. Likewise for graticules of ώe type in Fig. 30, a similar "scaled-kernel" matched filtering operation is performed on ώe plot 1040 of Fig. 34. This generally provides for a single candidate scale factor. Then, using ώe stored phase plots 1012, 1014 and 1016 of Fig. 32, a more traditional matched filtering operation is applied between ώese stored plots (as kernels), and ώe measured phase profiles along the half- circles at ώe previously found scale. The last step in registering graticules of the type in Fig. 29 is to perform a garden variety matched filter between ώe known (either spectral or spatial) profile of ώe graticule with ώe suspect image. Since boώ ώe rotation, scale and orientation are now known from previous steps, this matched filtering operation is straightforward. If ώe accuracies and precision of preceding steps have not exceeded design specifications in ώe process, then a simple micro-search can be performed in ώe small neighborhood about ώe two parameters scale and rotation, a matched filter operation performed, and ώe highest value found will determine a "fine tuned" scale and rotation. In this way, ώe scale and rotation can be found to within ώe degree set by the noise and cross¬ talk of ώe suspect image itself. Likewise, once ώe scale and rotation of ώe graticules of ώe type in Fig. 30 are found, then a straightforward matched filter operation can complete ώe registration process, and similar "fine tuning" can be applied. Moving on to a variant of the use of the graticules of the type in Fig. 29, Fig. 36 presents the possibility for finding the subliminal graticules without the need for performing a computationally expensive 2 dimensional FFT (fast fourier transform). In situations where computational overhead is a major issue, then the search problem can be reduced to a series of one-dimensional steps. Fig. 36 broadly depicts how to do this. The figure at the top left is an arbitrary image in which the graticules of the type of Fig. 29 have been embedded. Starting at angle 0, and finishing with an angie just below 180 degrees, and stepping by, for example 5 degrees, ώe grey values along ώe depicted columns can be simply added to create a resulting column-integrated scan, 1058. The figure in the top right, 1052, depicts one of the many angles at which this will be performed. This column-integrated scan then is transformed into its fourier representation using ώe less computationally expensive 1 dimensional FFT. This is then turned into a magnitude or "power" plot (the two are different), and a similar normalized vector version created just like 1022 and 1024 in Fig. 33. The difference now is ώat as ώe angle approaches the proper angles of ώe graticules, slowly the tell-tale peaks begin to appear in the 1024-like plots, but ώey generally show up at higher frequencies ώan expected for a given scale, since we are generally slightly off on our rotation. It remains to find ώe angle which maximizes ώe peak signals, ώus zooming in on ώe proper rotation. Once ώe proper rotation is found, then ώe scaled kernel matched filter process can be applied, followed by traditional matched filtering, all as previously described. Again, ώe sole idea of ώe "short-cut" of Fig. 36 is to greatly reduce ώe computational overhead in using ώe graticules of the type in Fig. 29. The inventor has not reduced ώis meώod of Fig. 36 to practice and cunently has no data on precisely how much computational savings, if any, will be realized. These efforts will be part of application specific development of ώe meώod. Fig. 37 simply summarizes, in order of major process steps, ώe meώods revolving around ώe graticules of ώe type in Fig. 29. In another variant embodiment, ώe graticule energy is not concentrated around the 45 degree angles in the spatial frequency domain. (Some compression algorithms, such as JPEG, tend to particularly attenuate image energy at ώis orientation.) Instead, ώe energy is more widely spatially spread. Fig. 29A shows one such distribution. The frequencies near ώe axes, and near ώe origin are generally avoided, since this is where ώe image energy is most likely concentrated. Detection of ώis energy in a suspect image again reiies on techniques like that reviewed above. However, instead of first identifying the axes, then the rotation, and ώen the scale, a more global pattern matching procedure is performed in which all are determined in a brute force effort. Those skilled in ώe art will recognize that the Fourier-Mellin transform is well suited for use in such pattern matching problems. The foregoing principles find application, for example, in photo-duplication kiosks. Such devices typically include a lens for imaging a customer- provided original (e.g. a photographic print or film) onto an opto-electronic detector, and a print-writing device for exposing and developing an emulsion substrate (again photographic paper or film) in accordance with ώe image data gathered by ώe detector. The details of such devices are well known to ώose skilled in ώe art, and are not belabored here. In such systems, a memory stores data from ώe detector, and a processor (e.g. a Pentium microprocessor with associated support components) can be used to process the memory data to detect ώe presence of copyright data steganographically encoded ώerein. If such data is detected, the print- writing is interrupted. To avoid defeat of ώe system by manual rotation of the original image off-axis, ώe processor desirably implements ώe above-described technique to effect automatic registration of the original, notwithstanding scale, rotation, and origin offset factors. If desired, a digital signal processing board can be employed to offload certain of ώe FFT processing from ώe main (e.g. Pentium) processor. After a rotated/scaled image is registered, detection of any steganographically encoded copyright notice is straightforward and assures ώe machine will not be used in violation of a photographer's copyright. While ώe techniques disclosed above have make use of applicant's preferred steganographic encoding methods, ώe principles thereof are more widely applicable and can be used in many instances in which automatic registration of an image is to be effected. USE OF EMBEDDED SIGNATURES IN VIDEO. WHEREIN A VTDEO DATA STREAM EFFECTIVELY SERVES AS A HIGH-SPEED ONE WAY MODEM Through use of the universal coding system outlined in earlier sections, and ώrough use of master snowy frames which change in a simple fashion frame to frame, a simple receiver can be designed such ώat it has pre-knowledge of ώe changes in ώe master snowy frames and can therefore read a changing N-bit message word frame by frame (or I-frame by I-frame as ώe case may be in MPEG video). In ώis way, a motion picture sequence can be used as a high speed one-way information channel, much like a one-way modem. Consider, for example, a frame of video data with N scan lines which is steganographically encoded to effect ώe transmission of an N-bit message. If ώere are 484 scan lines in a frame (N), and frames change 30 times a second, an information channel wiώ a capacity comparable to a 14.4 kilobaud modem is achieved. In actual practice, a data rate substantially in excess of N bits per frame can usually be achieved, yielding transmission rates nearer ώat of ISDN circuits. FRAUD PREVENTION IN WIRELESS COMMUNICATIONS En ώe cellular telephone industry, hundreds of millions of dollars of revenue is lost each year through theft of services. While some services are lost due to physical theft of cellular telephones, the more pernicious threat is posed by cellular telephone hackers. Cellular telephone hackers employ various electronic devices to mimic ώe identification signals produced by an authorized cellular telephone. (These signals are sometimes called authorization signals, verification numbers, signature data, etc.) Often, ώe hacker learns of ώese signals by eavesdropping on authorized cellular telephone subscribers and recording the data exchanged with the ceil cite. By artful use of ώis data, ώe hacker can impersonate an authorized subscriber and dupe ώe carrier into completing pirate calls. In ώe prior art, identification signals are segregated from the voice signals. Most commonly, they are temporally separated, e.g. transmitted in a burst at the time of call origination. Voice data passes through the channel only after a verification operation has taken place on this identification data. (Identification data is also commonly included in data packets sent during ώe transmission.) Anoώer approach is to spectrally separate ώe identification, e.g. in a spectral subband outside ώat allocated to the voice data. Oώer fraud-detenent schemes have also been employed. One class of techniques monitors characteristics of a cellular telephone's RF signal to identify the originating phone. Anoώer class of techniques uses handshaking protocols, wherein some of the data returned by ώe cellular telephone is based on an algorithm (e.g. hashing) applied to random data sent thereto. Combinations of ώe foregoing approaches are also sometimes employed. U.S. Patents 5,465,387, 5,454,027, 5,420,910, 5,448,760, 5,335,278, 5,345,595, 5, 144,649, 5,204,902, 5,153,919 and 5,388,212 detail various cellular telephone systems, and fraud detenence techniques used ώerein. The disclosures of ώese patents are incorporated by reference. As ώe sophistication of fraud deterrence systems increases, so does ώe sophistication of cellular telephone hackers. Ultimately, hackers have the upper hand since ώey recognize that all prior art systems are vulnerable to ώe same weakness: ώe identification is based on some attribute of ώe cellular telephone transmission outside ώe voice data. Since ώis attribute is segregated from ώe voice data, such systems will always be susceptible to pirates who electronically "patch" their voice into a composite electronic signal having ώe attribute(s) necessary to defeat ώe fraud deterrence system. To overcome ώis failing, prefened embodiments of ώis aspect of ώe present technology steganographically encode ώe voice signal wiώ identification data, resulting in "in-band" signalling (in-band boώ temporally and spectrally). This approach allows the carrier to monitor the user's voice signal and decode ώe identification data therefrom. In one such form of the technology, some or ail of the identification data used in the prior art (e.g. data transmitted at call origination) is repeatedly steganographically encoded in the user's voice signal as well. The carrier can thus periodically or aperiodically check ώe identification data accompanying ώe voice data wiώ that sent at call origination to ensure ώey match. If ώey do not, ώe call is identified as being hacked and steps for remediation can be instigated such as interrupting ώe call. In anouier form of ώe technology, a randomly selected one of several possible messages is repeatedly steganographically encoded on ώe subscriber's voice. An index sent to ώe cellular carrier at call set-up identifies which message to expect. If ώe message steganographically decoded by ώe cellular carrier from the subscriber's voice does not match ώat expected, ώe call is identified as fraudulent. In a preferred form of this aspect of ώe technology, ώe steganographic encoding relies on a pseudo random data signal to transform ώe message or identification data into a low level noise-like signal superimposed on ώe subscriber's digitized voice signal. This pseudo random data signal is known, or knowable, to boώ ώe subscriber's telephone (for encoding) and to ώe cellular carrier (for decoding). Many such embodiments rely on a deterministic pseudo random number generator seeded wiώ a datum known to boώ ώe telephone and ώe carrier. In simple embodiments ώis seed can remain constant from one call to ώe next (e.g. a telephone ID number). In more complex embodiments, a pseudo-one-time pad system may be used, wherein a new seed is used for each session (i.e. telephone call). In a hybrid system, ώe telephone and cellular carrier each have a reference noise key (e.g. 10,000 bits) from which ώe telephone selects a field of bits, such as 50 bits beginning at a randomly selected offset, and each uses this excerpt as ώe seed to generate the pseudo random data for encoding. Data sent from ώe telephone to ώe carrier (e.g. the offset) during call set-up allows the carrier to reconstruct ώe same pseudo random data for use in decoding. Yet further improvements can be derived by borrowing basic techniques from ώe art of cryptographic communications and applying ώem to ώe steganographically encoded signal detailed in this disclosure. Details of applicant's prefened techniques for steganographic encoding/decoding with a pseudo random data stream are more particularly detailed in ώe previous portions of this specification, but ώe present technology is not limited to use with such techniques. The reader is presumed to be familiar wiώ cellular communications technologies. Accordingly, details known from prior art in ώis field aren ' t belabored herein. Referring to Fig. 38, an illustrative cellular system includes a telephone 2010, a cell site 2012, and a central office 2014. Conceptually, ώe telephone may be viewed as including a microphone 2016, an A/D converter 2018, a data formatter 2020, a modulator 2022, an RF section 2024, an antenna 2026, a demodulator 2028, a data unformatter 2030, a D/A converter 2032, and a speaker 2034. In operation, a subscriber's voice is picked up by ώe microphone 2016 and converted to digital form by ώe A/D converter 2018. The data formatter 2020 puts ώe digitized voice into packet form, adding synchronization and control bits thereto. The modulator 2022 converts ώis digital data stream into an analog signal whose phase and/or amplitude properties change in accordance with ώe data being modulated. The RF section 2024 commonly translates ώis time- varying signal to one or more intermediate frequencies, and finally to a UHF transmission frequency. The RF section thereafter amplifies it and provides ώe resulting signal to the antenna 2026 for broadcast to ώe cell site 2012. The process works in reverse when receiving. A broadcast from ώe cell cite is received through the antenna 2026. RF section 2024 amplifies and translates the received signal to a different frequency for demodulation. Demodulator 2028 processes ώe amplitude and/or phase variations of ώe signal provided by the RF section to produce a digital data stream conesponding thereto. The data unformatter 2030 segregates ώe voice data from the associated synchronization/control data, and passes ώe voice data to ώe D/A converter for conversion into analog form. The output from ώe D/A converter drives the speaker 2034, through which the subscriber hears ώe other party's voice. - I l l - The cell site 2012 receives broadcasts from a plurality of telephones 2010. and relays the data received to the central office 2014. Likewise, the cell site 2012 receives outgoing data from the central office and broadcasts same to the telephones. The central office 2014 performs a variety of operations, including call authentication, switching, and cell hand-off. (In some systems, ώe functional division between ώe cell site and ώe central station is different ώan ώat outlined above. Indeed, in some systems, all of this functionality is provided at a single site.) In an exemplary embodiment of this aspect of ώe technology, each telephone 2010 additionally includes a steganographic encoder 2036. Likewise, each cell site 2012 includes a steganographic decoder 2038. The encoder operates to hide an auxiliary data signal among ώe signals representing ώe subscriber's voice. The decoder performs ώe reciprocal function, discerning ώe auxiliary data signal from ώe encoded voice signal. The auxiliary signal serves to verify ώe legitimacy of ώe call. An exemplary steganographic encoder 2036 is shown in Fig. 39. The illustrated encoder 2036 operates on digitized voice data, auxiliary data, and pseudo¬ random noise (PRN) data. The digitized voice data is applied at a port 2040 and is provided, e.g., from A/D converter 2018. The digitized voice may comprise 8-bit samples. The auxiliary data is applied at a port 2042 and comprises, in one form of ώe technology, a stream of binary data uniquely identifying ώe telephone 2010. (The auxiliary data may additionally include administrative data of the sort conventionally exchanged with a cell site at call set-up.) The pseudo-random noise data is applied at a port 2044 and can be, e.g., a signal that randomly alternates between "-I" and "1" values. (More and more cellular phones are incorporating spread spectrum capable circuitry, and ώis pseudo-random noise signal and other aspects of ώis technology can often "piggy-back" or share ώe circuitry which is already being applied in the basic operation of a cellular unit). For expository convenience, it is assumed ώat all ώree data signals applied to the encoder 2036 are clocked at a common rate, although ώis is not necessary in practice. In operation, ώe auxiliary data and PRN data streams are applied to the two inputs of a logic circuit 2046. The output of circuit 2046 switches between - 1 and + 1 in accordance wiώ the following table: (If ώe auxiliary data signal is conceptualized as switching between -1 and 1, instead of 0 and 1, it will be seen ώat circuit 2046 operates as a one-bit multiplier.) The output from gate 2046 is thus a bipolar data stream whose instantaneous value changes randomly in accordance with ώe conesponding values of ώe auxiliary data and ώe PRN data. It may be regarded as noise. However, it has ώe auxiliary data encoded therein. The auxiliary data can be extracted if ώe conesponding PRN data is known. The noise-like signal from gate 2046 is applied to ώe input of a scaier circuit 2048. Scaier circuit scales (e.g. multiplies) ώis input signal by a factor set by a gain control circuit 2050. In ώe illustrated embodiment, this factor can range between 0 and 15. The output from scaier circuit 2048 can thus be represented as a five-bit data word (four bits, plus a sign bit) which changes each clock cycle, in accordance with the auxiliary and PRN data, and the scale factor. The output from ώe scaier circuit may be regarded as "scaled noise data" (but again it is "noise" from which the auxiliary data can be recovered, given the PRN data). The scaled noise data is summed with the digitized voice data by a summer 2051 to provide ώe encoded output signal (e.g. binarily added on a sample by sample basis). This output signal is a composite signal representing boώ ώe digitized voice data and ώe auxiliary data. The gain control circuit 2050 controls ώe magnitude of ώe added scaled noise data so its addition to ώe digitized voice data does not noticeably degrade ώe voice data when converted to analog form and heard by a subscriber. The gain control circuit can operate in a variety of ways. One is a logarithmic scaling function. Thus, for example, voice data samples having decimal values of 0, 1 or 2 may be correspond to scale factors of unity, or even zero, whereas voice data samples having values in excess of 200 may correspond to scale factors of 15. Generally speaking, ώe scale factors and ώe voice data values correspond by a square root relation. That is, a four-fold increase in a value of ώe voice data corresponds to approximately a two-fold increase in a value of ώe scaling factor associated ώerewiώ. Anoώer scaling function would be linear as derived from the average power of the voice signal. (The parenthetical reference to zero as a scaling factor alludes to cases, e.g., in which the digitized voice signal sample is essentially devoid of information content.) More satisfactory ώan basing ώe instantaneous scaling factor on a single voice data sample, is to base ώe scaling factor on ώe dynamics of several samples. That is, a stream of digitized voice data which is changing rapidly can camouflage relatively more auxiliary data ώan a stream of digitized voice data which is changing slowly. Accordingly, ώe gain control circuit 2050 can be made responsive to the first, or preferably ώe second- or higher-order derivative of ώe voice data in setting the scaling factor. In still other embodiments, ώe gain control block 2050 and scaier 2048 can be omitted entirely. (Those skilled in the art will recognize the potential for "rail enors" in the foregoing systems. For example, if the digitized voice data consists of 8-bit samples, and the samples span the entire range from 0 to 255 (decimal), then ώe addition or subtraction of scaled noise to/from the input signal may produce output signals that cannot be represented by 8 bits (e.g. -2, or 257). A number of well-understood techniques exist to rectify ώis situation, some of them proactive and some of ώem reactive. Among these known techniques are: specifying ώat the digitized voice data shall not have samples in ώe range of 0-4 or 241-255, ώereby safely permitting combination with ώe scaled noise signal; and including provision for detecting and adaptively modifying digitized voice samples ώat would otherwise cause rail enors.) Returning to ώe telephone 2010, an encoder 2036 like ώat detailed above is desirably inteφosed between ώe A/D converter 2018 and ώe data formatter 2020, ώereby serving to steganographically encode all voice transmissions with ώe auxiliary data. Moreover, ώe circuitry or software controlling operation of the telephone is arranged so ώat the auxiliary data is encoded repeatedly. That is, when all bits of ώe auxiliary data have been encoded, a pointer loops back and causes the auxiliary data to be applied to ώe encoder 2036 anew. (The auxiliary data may be stored at a known address in RAM memory for ease of reference.) It will be recognized ώat ώe auxiliary data in the illustrated embodiment is transmitted at a rate one-eighth ώat of the voice data. That is, for every 8-bit sample of voice data, scaled noise data corresponding to a single bit of ώe auxiliary data is sent. Thus, if voice samples are sent at a rate of 4800 samples/second, auxiliary data can be sent at a rate of 4800 bits/second. If the auxiliary data is comprised of 8-bit symbols, auxiliary data can be conveyed at a rate of 600 symbols/second. If ώe auxiliary data consists of a string of even 60 symbols, each second of voice conveys ώe auxiliary data ten times. (Significantly higher auxiliary data rates can be achieved by resorting to more efficient coding techniques, such as limited-symbol codes (e.g. 5- or 6-bit codes), Huffman coding, etc.) This highly redundant transmission of ώe auxiliary data permits lower amplitude scaled noise data to be used while still providing sufficient signal-to- noise headroom to assure reliable decoding — even in the relatively noisy environment associated with radio transmissions. Tuming now to Fig. 40, each cell site 2012 has a steganographic decoder 2038 by which it can analyze the composite data signal broadcast by the telephone 2010 to discern and separate the auxiliary data and digitized voice data therefrom. (The decoder desirably works on unformatted data (i.e. data with ώe packet overhead, control and administrative bits removed; ώis is not shown for clarity of illustration). The decoding of an unknown embedded signal (i.e. ώe encoded auxiliary signal) from an unknown voice signal is best done by some form of statistical analysis of ώe composite data signal. The techniques therefor discussed above are equally applicable here. For example, ώe entropy-based approach can be utilized. In this case, ώe auxiliary data repeats every 480 bits (instead of every 8). As above, ώe entropy-based decoding process treats every 480th sample of ώe composite signal in like fashion. In particular, ώe process begins by adding to ώe 1st, 481st, 861st, etc. samples of ώe composite data signal the PRN data wiώ which ώese samples were encoded. (That is, a set of sparse PRN data is added: ώe original PRN set, wiώ all but every 480th datum zeroed out.) The localized entropy of ώe resulting signal around these points (i.e. the composite data signal with every 480th sample modified) is ώen computed. The foregoing step is ώen repeated, this time subtracting the PRN data conesponding ώereto from ώe 1st, 481st, 961st, etc. composite data samples. One of these two operations will counteract (e.g. undo) ώe encoding process and reduce the resulting signal's entropy; ώe oώer will aggravate it. If adding ώe sparse PRN data to the composite data reduces its entropy, ώen this data must earlier have been subtracted from the original voice signal. This indicates ώat the corresponding bit of ώe auxiliary data signal was a "0" when ώese samples were encoded. (A "0" at ώe auxiliary data input of logic circuit 46 caused it to produce an inverted version of the corresponding PRN datum as its output datum, resulting in subtraction of ώe conesponding PRN datum from ώe voice signal.) Conversely, if subtracting ώe sparse PRN data from ώe composite data reduces its entropy, then ώe encoding process must have earlier added ώis noise. This indicates that the value of the auxiliary data bit was a " 1 " when samples 1, 481, 961, etc., were encoded. By noting in which case entropy is lower by (a) adding or (b) subtracting a sparse set of PRN data to/from ώe composite data, it can be determined whether ώe first bit of the auxiliary data is (a) a "0", or (b) a "1." (In real life applications, in the presence of various distorting phenomena, ώe composite signal may be sufficiently corrupted so that neiώer adding nor subtracting ώe sparse PRN data actually reduces entropy. Instead, both operations will increase entropy. In this case, the "correct" operation can be discerned by observing which operation increases the entropy less.) The foregoing operations can then be conducted for ώe group of spaced samples of the composite data beginning with ώe second sample (i.e. 2, 482, 962, ...). The entropy of ώe resulting signals indicate whether the second bit of ώe auxiliary data signal is a "0" or a "1." Likewise with ώe following 478 groups of spaced samples in the composite signal, until all 480 bits of the code word have been discerned. As discussed above, correlation between ώe composite data signal and ώe PRN data can also be used as a statistical detection technique. Such operations are facilitated in ώe present context since ώe auxiliary data whose encoded representation is sought, is known, at least in large part, a priori. (In one form of ώe technology, ώe auxiliary data is based on ώe authentication data exchanged at call set-up, which ώe cellular system has already received and logged; in another form (detailed below), ώe auxiliary data comprises a predetermined message.) Thus, the problem can be reduced to determining whether an expected signal is present or not (rather ώan looking for an entirely unknown signal). Moreover, data formatter 2020 breaks ώe composite data into frames of known length. (In a known GSM implementation, voice data is sent in time slots which convey 114 data bits each.) By padding ώe auxiliary data as necessary, each repetition of ώe auxiliary data can be made to start, e.g., at ώe beginning of such a frame of data. This, too, simplifies ώe correlation determinations, since 113 of every 1 14 possible bit alignments can be ignored (facilitating decoding even if none of ώe auxiliary data is known a priori). Again, ώis wireless fraud detection poses ώe now-familiar problem of detecting known signals in noise, and the approaches discussed earlier are equally applicable here. Where, as here, the location of the auxiliary signal is known a priori (or more accurately, known to fall within one of a few discrete locations, as discussed above), ώen the matched filter approach can often be reduced to a simple vector dot product between a set of sparse PRN data, and mean-removed excerpts of the composite signal corresponding thereto. (Note that the PRN data need not be sparse and may arrive in contiguous bursts, such as in British patent publication 2, 196, 167 mentioned earlier wherein a given bit in a message has contiguous PRN vaiues associated with it.) Such a process steps through all 480 sparse sets of PRN data and performs conesponding dot product operations. If ώe dot product is positive, ώe conesponding bit of the auxiliary data signal is a "1;" if ώe dot product is negative, the corresponding bit of ώe auxiliary data signal is a "0." If several alignments of ώe auxiliary data signal wiώin ώe framed composite signal are possible, ώis procedure is repeated at each candidate alignment, and ώe one yielding ώe highest conelation is taken as true. (Once ώe correct alignment is determined for a single bit of ώe auxiliary data signal, ώe alignment of all ώe oώer bits can be determined therefrom. "Alignment," perhaps better known as "synchronization," can be achieved by primarily through ώe very same mechanisms which lock on and track ώe voice signal itself and allow for the basic functioning of ώe cellular unit). Security Considerations Security of ώe presently described embodiments depends, in large part, on security of the PRN data and/or security of ώe auxiliary data. In ώe following discussion, a few of many possible techniques for assuring ώe security of these data are discussed. In a first embodiment, each telephone 2010 is provided wiώ a long noise key unique to the telephone. This key may be, e.g., a highly unique 10,000 bit string stored in ROM. (In most applications, keys substantially shorter than ώis may be used.) The central office 2014 has access to a secure disk 2052 on which such key data for all authorized telephones are stored. (The disk may be remote from ώe office itself.) Each time ώe telephone is used, fifty bits from this noise key are identified and used as the seed for a deterministic pseudo random number generator. The data generated by this PRN generator serve as the PRN data for that telephone call. The fifty bit seed can be determined, e.g., by using a random number generator in ώe telephone to generate an offset address between 0 and 9,950 each time ώe telephone is used to place a call. The fifty bits in ώe noise key beginning at ώis offset address are used as ώe seed. During call setup, ώis offset address is transmitted by ώe telephone, through the cell site 2012, to ώe central office 2014. There, a computer at ώe central office uses ώe offset address to index its copy of ώe noise key for ώat telephone. The central office ώereby identifies ώe same 50 bit seed as was identified at ώe telephone. The central office 2014 ώen relays ώese 50 bits to ώe cell site 2012, where a deterministic noise generator like ώat in ώe telephone generates a PRN sequence corresponding to ώe 50 bit key and applies same to its decoder 2038. By ώe foregoing process, ώe same sequence of PRN data is generated boώ at ώe telephone and at ώe cell site. Accordingly, the auxiliary data encoded on ώe voice data by the telephone can be securely transmitted to, and accurately decoded by, ώe cell site. If this auxiliary data does not match ώe expected auxiliary data (e.g. data transmitted at call set-up), ώe call is flagged as fraudulent and appropriate remedial action is taken. It will be recognized ώat an eavesdropper listening to radio transmission of call set-up information can intercept only ώe randomly generated offset address transmitted by ώe telephone to the cell site. This data, alone, is useless in pirating calls. Even if ώe hacker had access to ώe signals provided from ώe central office to the cell site, ώis data too is essentially useless: all ώat is provided is a 50 bit seed. Since ώis seed is different for nearly each call (repeating only 1 out of every 9,950 calls), it too is unavailing to ώe hacker. In a related system, the entire 10,000 bit noise key can be used as a seed. An offset address randomly generated by ώe telephone during call set-up can be used to identify where, in ώe PRN data resulting from ώat seed, ώe PRN data to be used for that session is to begin. (Assuming 4800 voice samples per second, 4800 PRN data are required per second, or about 17 million PRN data per hour. Accordingly, the offset address in this variant embodiment will likely be far larger than ώe offset address described above.) In ώis variant embodiment, ώe PRN data used for decoding is preferably generated at me central station from the 10,000 bit seed, and relayed to the cell site. (For security reasons, ώe 10,000 bit noise key should not leave ώe security of ώe central office.) In variants of ώe foregoing systems, ώe offset address can be generated by ώe central station or at the cell site, and relayed to the telephone during call set-up, raώer than vice versa. In anoώer embodiment, ώe telephone 2010 may be provided with a list of one-time seeds, matching a list of seeds stored on ώe secure disk 2052 at ώe central office. Each time ώe telephone is used to originate a new call, ώe next seed in ώe list is used. By ώis arrangement, no data needs to be exchanged relating to ώe seed; ώe telephone and ώe carrier each independently know which seed to use to generate ώe pseudo random data sequence for ώe current session. In such an embodiment, ώe carrier can determine when ώe telephone has nearly exhausted its list of seeds, and can transmit a substitute list (e.g. as part of administrative data occasionally provided to ώe telephone). To enhance security, ώe carrier may require ώat ώe telephone be returned for manual reprogramming, to avoid radio transmission of this sensitive information. Alternatively, ώe substitute seed list can be encrypted for radio transmission using any of a variety of well known techniques. In a second class of embodiments, security derives not from ώe security of ώe PRN data, but from security of ώe auxiliary message data encoded ώereby. One such system relies on transmission of a randomly selected one of 256 possible messages. In ώis embodiment, a ROM in ώe telephone stores 256 different messages (each message may be, e.g., 128 bits in length). When ώe telephone is operated to initiate a call, ώe telephone randomly generates a number between I and 256, which serves as an index to ώese stored messages. This index is transmitted to the cell site during call set-up, allowing ώe central station to identify the expected message from a matching database on secure disk 2052 containing the same 256 messages. (Each telephone has a different collection of messages.) (Alternatively, the carrier may randomly select the index number during call set-up and transmit it to ώe telephone, identifying ώe message to be used during ώat session.) In a theoretically pure world where proposed attacks to a secure system are only mathematical in nature, much of ώese additional layers of security might seem superfluous. (The addition of these extra layers of security, such as differing ώe messages themselves, simply acknowledge ώat ώe designer of actual public- functioning secure systems will face certain implementation economics which might compromise ώe mathematical security of ώe core principles of ώis technology, and ώus ώese auxiliary layers of security may afford new tools against the inevitable attacks on implementation). Thereafter, all voice data transmitted by the telephone for ώe duration of ώat call is steganographically encoded with ώe indexed message. The cell site checks ώe data received from ώe telephone for ώe presence of ώe expected message. If ώe message is absent, or if a different message is decoded instead, ώe call is flagged as fraudulent and remedial action is taken. In ώis second embodiment, ώe PRN data used for encoding and decoding can be as simple or complex as desired. A simple system may use ώe same PRN data for each call. Such data may be generated, e.g., by a deterministic PRN generator seeded with fixed data unique to the telephone and known also by ώe central station (e.g. a telephone identifier), or a universal noise sequence can be used (i.e. ώe same noise sequence can be used for all telephones). Or ώe pseudo random data can be generated by a deterministic PRN generator seeded with data that changes from call to call (e.g. based on data transmitted during call set-up identifying, e.g., the destination telephone number, etc.). Some embodiments may seed ώe pseudo random number generator wiώ data from a preceding call (since ώis data is necessarily known to the telephone and the carrier, but is likely not known to pirates). Naturally, elements from ώe foregoing two approaches can be combined in various ways, and supplemented by other features. The foregoing embodiments are exemplary only, and do not begin to catalog the myriad approaches which may be used. Generally speaking, any data which is necessarily known or knowable by both the telephone and the cell site/central station, can be used as the basis for eiώer the auxiliary message data, or the PRN data by which it is encoded. Since ώe preferred embodiments of ώis aspect of the present technology each redundantly encodes ώe auxiliary data throughout ώe duration of the subscriber's digitized voice, the auxiliary data can be decoded from any brief sample of received audio. In the preferred forms of this aspect of the technology, the carrier repeatedly checks the steganographically encoded auxiliary data (e.g. every 10 seconds, or at random intervals) to assure ώat it continues to have ώe expected attributes. While ώe foregoing discussion has focused on steganographically encoding a transmission from a cellular telephone, it will be recognized ώat transmissions to a cellular telephone can be steganographically encoded as well. Such arrangements find applicability, e.g., in conveying administrative data (i.e. non-voice data) from ώe carrier to individual telephones. This administrative data can be used, for example, to reprogram parameters of targeted cellular telephones (or all cellular telephones) from a central location, to update seed lists (for systems employing ώe above-described on-time pad system), to apprise "roaming" cellular telephones of data unique to an unfamiliar local area, etc. In some embodiments, ώe carrier may steganographically transmit to ώe cellular telephone a seed which ώe cellular phone is to use in its transmissions to ώe carrier during ώe remainder of ώat session. While ώe- foregoing discussion has focused on steganographic encoding of ώe baseband digitized voice data, artisans will recognize ώat intermediate frequency signals (whether analog or digital) can likewise be steganographically encoded in accordance with principles of the technology. An advantage of post-baseband encoding is that ώe bandwidth of these intermediate signals is relatively large compared with the baseband signal, allowing more auxiliary data to be encoded ώerein, or allowing a fixed amount of auxiliary data to be repeated more frequently during transmission. (If steganographic encoding of an intermediate signal is employed, care should be taken ώat ώe perturbations introduced by the encoding are not so large as to interfere with reliable transmission of the administrative data, taking into account any error coπecting facilities supported by ώe packet format). Those skilled in ώe art will recognize that the auxiliary data, itself, can be arranged in known ways to support error detecting, or error correcting capabilities by the decoder 38. The interested reader is referred, e.g., to Rorabaugh, Error Coding Cookbook, McGraw Hill, 1996, one of many readily available texts detailing such techniques. While ώe prefened embodiment of this aspect of ώe technology is illustrated in ώe context of a cellular system utilizing packetized data, other wireless systems do not employ such conveniently framed data. In systems in which framing is not available as an aid to synchronization, synchronization marking can be achieved within ώe composite data signal by techniques such as ώat detailed in applicant's prior applications. In one class of such techniques, ώe auxiliary data itself has characteristics facilitating its synchronization. In anoώer class of techniques, ώe auxiliary data modulates one or more embedded carrier pattems which are designed to facilitate alignment and detection. As noted earlier, ώe principles of ώe technology are not restricted to use wiώ ώe particular forms of steganographic encoding detailed above. Indeed, any steganographic encoding technique previously known, or hereafter invented, can be used in ώe fashion detailed above to enhance ώe security or functionality of cellular (or other wireless, e.g. PCS) communications systems. Likewise, these principles are not restricted to wireless telephones; any wireless transmission may be provided wiώ an "in-band" channel of ώis type. It will be recognized ώat systems for implementing applicant's technology can comprises dedicated hardware circuit elements, but more commonly comprise suitably programmed microprocessors with associated RAM and ROM memory (e.g. one such system in each of the telephone 2010, cell-site 2012, and central office 2014). ENCODING BY BIT CELLS The foregoing discussions have focused on incrementing or decrementing the values of individual pixels, or of groups of pixels (bumps), to reflect encoding of an auxiliary data signal combined with a pseudo random noise signal. The following discussion details a variant embodiment wherein ώe auxiliary data ~ widiout pseudo randomization — is encoded by patterned groups of pixels, here termed bit cells. Referring to Figs 41A and 41B, two illustrative 2x2 bit cells are shown. Fig. 4IA is used to represent a "0" bit of ώe auxiliary data, while Fig. 41B is used to represent a "1" bit. In operation, ώe pixels of ώe underlying image are tweaked up or down in accordance wiώ ώe +/- values of ώe bit cells to represent one of ώese two bit values. (The magnitude of ώe tweaking at any given pixel, bit cell or region of ώe image can be a function of many factors, as detailed below. It is ώe sign of ώe tweaking ώat defines ώe characteristic pattern.) In decoding, ώe relative biases of ώe encoded pixels are examined (using techniques described above) to identify, for each corresponding region of ώe encoded image, which of the two patterns is represented. . While ώe auxiliary data is not explicitly randomized in ώis embodiment, it will be recognized ώat ώe bit cell pattems may be viewed as a "designed" carrier signal, as discussed above. The substitution of ώe previous embodiments' pseudo random noise with ώe present "designed" information carrier affords an advantage: ώe bit cell patterning manifests itself in Fourier space. Thus, ώe bit cell patterning can act like the subliminal digital graticules discussed above to help register a suspect image to remove scale/rotation enors. By changing ώe size of ώe bit ceil, and ώe pattern ώerein, ώe location of ώe energy ώereby produced in ώe spatial transform domain can be tailored to optimize independence from typical imagery energy and facilitate detection. (While ώe foregoing discussion contemplates ώat the auxiliary data is encoded directly without randomization by a PRN signal, in oώer embodiments, randomization can of course be used.) MORE ON PERCEPTUALLY ADAPTTVE SIGNING In several of ώe above-detailed embodiments, ώe magnitude of ώe signature energy was tailored on a region-by-region basis to reduce its visibility in an image (or its audibility in a sound, etc.). In ώe following discussion, applicant more particularly considers ώe issue of hiding signature energy in an image, ώe separate issues ώereby posed, and solutions to each of ώese issues. The goal of ώe signing process, beyond simply functioning, is to maximize ώe "numeric detectability" of an embedded signature while meeting some form of fixed "visibility/acceptability threshold" set by a given user/creator. In service to design toward this goal, imagine ώe following three axis parameter space, where two of ώe axes are only half-axes (positive only), and the ώird is a full axis (both negative and positive). This set of axes define two of ώe usual eight octal spaces of euclidean 3-space. As things refine and "deservedly separable" parameters show up on ώe scene (such as "extended local visibility metrics"), ώen they can define ώeir own (generally) half-axis and extend ώe following example beyond three dimensions. The signing design goal becomes optimally assigning a "gain" to a local bump based on its coordinates in ώe above defined space, whilst keeping in mind ώe basic needs of doing ώe operations fast in real applications. To begin with, ώe three axes are ώe following. We'll call ώe two half axes x and y, while the full axis will be z. The x axis represents ώe luminance of the singular bump. The basic idea is that you can squeeze a little more energy into bright regions as opposed to dim ones. It is important to note that when true "psycho- linear - device independent" luminance values (pixel DN's) come along, this axis might become superfluous, unless of course if ώe luminance value couples into the other operative axes (e.g. C*xy). For now, ώis is here as much due to ώe sub-optimality of current quasi-linear luminance coding. The y axis is ώe "local hiding potential" of ώe neighborhood within which ώe bump finds itself. The basic idea is ώat flat regions have a low hiding potential since the eye can detect subtle changes in such regions, whereas complex textured regions have a high hiding potential. Long lines and long edges tend toward ώe lower hiding potential since "breaks and choppiness" in nice smooώ long lines are also somewhat visible, while shorter lines and edges, and mosaics thereof, tend toward ώe higher hiding potential. These latter notions of long and short are directly connected to processing time issues, as well to issues of ώe engineering resources needed to carefully quantify such parameters. Developing ώe working model of ώe y-axis will inevitably entail one part theory to one part picky-artist-empiricism. As ώe parts of the hodge-podge y-axis become better known, ώey can splinter off into ώeir own independent axes if it's worth it. The z-axis is ώe "with or against the grain" (discussed below) axis which is the full axis - as opposed to ώe other two half-axes. The basic idea is ώat a given input bump has a pre-existing bias relative to wheώer one wishes to encode a T or a '0' at its location, which to some non-triviai extent is a function of ώe reading algorithms which will be employed, whose (bias) magnitude is semi-conelated to ώe "hiding potential" of ώe y-axis, and, fortunately, can be used advantageously as a variable in determining what magnitude of a tweak value is assigned to the bump in question. The concomitant basic idea is ώat when a bump is already your friend (i.e. its bias relative to its neighbors already tends towards ώe desired delta value), then don't change it much. Its natural state already provides ώe delta energy needed for decoding, wiώout altering ώe localized image value much, if at all. Conversely, if a bump is initially your enemy (i.e. its bias relative to its neighbors tends away from the delta sought to be imposed by ώe encoding), then change it an exaggerated amount. This later operation tends to reduce the excursion of ώis point relative to its neighbors, making the point less visibly conspicuous (a highly localized blurring operation), while providing additional energy detectable when decoding. These two cases are termed "with the grain" and "against the grain" herein. The above general description of ώe problem should suffice for many years. Clearly adding in chrominance issues will expand the definitions a bit, leading to a bit more siεnature bang for the visibility, and human visibility research which is applied to ώe problem of compression can equally be applied to this area but for diametrically opposed reasons. Here are guiding principles which can be employed in an exemplary application. For speed's sake, local hiding potential can be calculated only based on a 3 by 3 neighborhood of pixels, ώe center one being signed and its eight neighbors. Beyond speed issues, there is also no data or coherent theory to support anything larger as well. The design issue boils down to canning ώe y-axis visibility thing, how to couple ώe luminance into this, and a little bit on ώe friend/enemy asymmetry thing. A guiding principle is to simply make a flat region zero, a classic pure maxima or minima region a " 1.0" or the highest value, and to have "local lines", "smooώ slopes", "saddle points" and whatnot fall out somewhere in between. The exemplary application uses six basic parameters: 1) luminance; 2) difference from local average; 3) ώe asymmetry factor (with or against ώe grain); 4) minimum linear factor (our crude attempt at flat v. lines v. maxima); 5) bit plane bias factor; and 6) global gain (ώe user's single top level gain knob). The Luminance, and Difference from Local Average parameters are straight forward, and their use is addressed elsewhere in ώis specification. The Asymmetry factor is a single scalar applied to the "against ώe grain" side of the difference axis of number 2 directly above. The Minimum Linear factor is admittedly erode but it should be of some service even in a 3 by 3 neighborhood setting. The idea is ώat true 2D local minima and maxima will be highly perturbed along each of the four lines travelling through ώe center pixel of ώe 3 by 3 neighborhood, while a visual line or edge will tend to flatten out at least one of the four linear profiles. [The four linear profiles are each 3 pixels in length, i.e., the top left pixei - center - bottom right; the top center - center - bottom center; the top right - center - bottom left; the right center - center - left center;]. Let's choose some metric of entropy as applied to three pixels in a row, perform this on ail four linear profiles, then choose the minimum value for our ultimate parameter to be used as our 'y-axis. ' The Bit Plane Bias factor is an interesting creature with two faces, ώe pre-emptive face and the post-emptive face. In the former, you simply "read" the unsigned image and see where ail the biases fall out for ail the bit planes, ώen simply boost the "global gain" of the bit planes which are, in total, going against your desired message, and leave the others alone or even slightly lower ώeir gain. In the post-emptive case, you chum out the whole signing process replete wiώ ώe pre-emptive bit plane bias and ώe other 5 parameters listed here, and ώen you, e.g., mn ώe signed image through heavy JPEG compression AND model ώe "gestait distortion" of line screen printing and subsequent scanning of ώe image, and ώen you read ώe image and find out which bit planes are struggling or even in enor, you appropπately beef up the bit plane bias, and you mn dirough ώe process again. If you have good data driving ώe beefing process you should only need to perform ώis step once, or, you can easily Van-Cittertize ώe process (arcane reference to reiterate the process with some damping factor applied to the tweaks). Finally, ώere is ώe Globai Gain. The goal is to make ώis single variable ώe top level "intensity knob" (more typically a slider or oώer control on a graphical user interface) that ώe slightly curious user can adjust if ώey want to. The very curious user can navigate down advanced menus to get their experimental hands on ώe other five variables here, as well as others. Visible Watermark In certain applications it is desirable to apply a visible indicia to an image to indicate ώat it includes steganographically encoded data. In one embodiment, ώis indicia can be a lightly visible logo (sometimes termed a "watermark") applied to one comer of ώe image. This indicates that ώe image is a "smart" image, conveying data in addition to ώe imagery. A lightbulb is one suitable logo. If marking of images becomes widespread (e.g., by software compatible wiώ Adobe's image processing software), a user of such software can decode ώe embedded data from an image and consult a public registry to identify the proprietor of ώe image. In some embodiments, ώe registry can serve as ώe conduit through which appropriate royalty payments are forwarded to the proprietor for ώe user's use of an image. (In an illustrative embodiment, ώe registry is a server on the Internet, accessible via ώe World Wide Web, coupled to a database. The database includes detailed information on catalogued images (e.g. name, address, phone number of proprietor, and a schedule of charges for different types of uses to which the image may be put), indexed by identification codes with which the images themselves are encoded. A person who decodes an image queries the registry with the codes thereby gleaned to obtain the desired data and, if appropriate, to forward electronic payment of a copyright royalty to the image's proprietor.) Particular Data Formats While ώe foregoing steganography techniques are broadly applicable, ώeir commercial acceptance will be aided by establishment of standards setting forth which pixels/bit cells represent what. The following discussion proposes one set of possible standards. For expository convenience, ώis discussion focuses on decoding of ώe data; encoding follows in a straightforward manner. Referring to Fig. 42, an image 1202 includes a plurality of tiled "signature blocks" 1204. (Partial signature blocks may be present at ώe image edges.) Each signature block 1204 includes an 8 x 8 array of sub-blocks 1206. Each sub-block 1206 includes an 8 x 8 array of bit ceils 1208. Each bit cell comprises a 2 x 2 anay of "bumps" 1210. Each bump 1210, in rum, comprises a square grouping of 16 individual pixels 1212. The individual pixels 1212 are ώe smallest quanta of image data. In ώis arrangement, however, pixel values are not, individually, the data carrying elements. Instead, ώis role is served by bit cells 1208 (i.e. 2 x 2 arrays of bumps 1210). In particular, ώe bumps comprising the bits cells are encoded to assume one of ώe two pattems shown in Fig. 41. As noted earlier, the pattern shown in Fig. 41 A represents a "0" bit, while ώe pattern shown in Fig. 4 IB represents a "1" bit. Each bit cell 1208 (64 image pixels) ώus represents a single bit of ώe embedded data. Each sub-block 1206 includes 64 bit cells, and ώus conveys 64 bits of embedded data. (The nature of ώe image changes effected by the encoding follows the techniques set forth above under the heading MORE ON PERCEPTUALLY ADAPTIVE SIGNING; ώat discussion is not repeated here.) In ώe illustrated embodiment, the embedded data includes two parts: control bits and message bits. The 16 bit cells 1208 A in the center of each sub-block 1206 serve to convey 16 control bits. The surrounding 48 bit cells 1208B serve to convey 48 message bits. This 64-bit chunk of data is encoded in each of ώe sub-blocks 1206, and is repeated 64 times in each signature block 1204. A digression: in addition to encoding of the image to redundantly embed the 64 control/message bits ώerein, ώe values of individual pixels are additionally adjusted to effect encoding of subliminal graticules through ώe image. In ώis embodiment, ώe graticules discussed in conjunction with Fig. 29A are used, resulting in an imperceptible texturing of ώe image. When ώe image is to be decoded, ώe image is transformed into the spatial domain, ώe Fourier- Mellin technique is applied to match ώe graticule energy points wiώ ώeir expected positions, and the processed data is ώen inverse-transformed, providing a registered image ready for decoding. (The sequence of first tweaking the image to effect encoding of ώe subliminal graticules, or first tweaking ώe image to effect encoding of ώe embedded data, is not believed to be critical. As presently practiced, the local gain factors (discussed above) are computed: ώen ώe data is encoded; ώen ώe subliminal graticule encoding is performed. (Both of ώese encoding steps make use of ώe local gain factors.)) Returning to ώe data format, once ώe encoded image has been ώus registered, ώe locations of ώe control bits in sub-block 1206 are known. The image is ώen analyzed, in ώe aggregate (i.e. considering ώe "northwestern-most" sub-block 1206 from each signature block 1204), to determine the value of control bit #1 (represented in sub-block 1206 by bit cell I208Aa). If ώis value is determined (e.g. by statistical techniques of ώe sort detailed above) to be a "1," this indicates ώat the format of ώe embedded data conforms to ώe standard detailed herein (the Digimarc Beta Data Format). According to this standard, control bit #2 (represented by bit ceils 1208Ab) is a flag indicating whether ώe image is copyrighted. Control bit #3 (represented by bit cells 1208Ac) is a flag indicating whether the image is unsuitable for viewing by children. Certain of the remaining bits are used for enor detection/conection purposes. — « The 48 message bits of each sub block 1206 can be put to any use; ώey are not specified in ώis format. One possible use is to define a numeric "owner" field and a numeric "image/item" field (e.g. 24 bits each). If this data format is used, each sub-block 1206 contains the entire control/message data, so same is repeated 64 times within each signature block of ώe image. If control bit #1 is not a " 1," ώen ώe format of the embedded data does not conform to the above described standard. In ώis case, ώe reading software analyzes ώe image data to determine ώe value of control bit #4. If ώis bit is set (i.e. equal to "1"), ώis signifies an embedded ASCII message. The reading software ώen examines control bits #5 and #6 to determine ώe length of ώe embedded ASCII message. If control bits #5 and #6 boώ are "0," ώis indicates ώe ASCII message is 6 characters in length. In ώis case, ώe 48 bit cells 1208B sunounding ώe control bits 1208 A are interpreted as six ASCII characters (8 bits each). Again, each sub-block 1206 contains ώe entire control/message data, so same is repeated 64 times within each signature block 1204 of the image. If control bit #5 is "0" and control bit #6 is "1," ώis indicates ώe embedded ASCII message is 14 characters in length. In ώis case, ώe 48 bit cells 1208B sunounding the control bits 1208 A are interpreted as the first six ASCII characters. The 64 bit ceils 1208 of the immediately-adjoining sub-block 1220 are interpreted as ώe final eight ASCII characters. Note ώat in this arrangement, ώe bit-cells 1208 in ώe center of sub-block 1220 are not interpreted as control bits. Instead, ώe entire sub-block serves to convey additional message bits. In this case there is just one group of control bits for two sub-blocks. Also note than in this arrangement, pairs of sub-blocks 1206 contains the entire control/message data, so same is repeated 32 times within each signature block 1204 of the image. Likewise if control bit #5 is "1" and control bit #6 is "0." This indicates the embedded ASCII message is 30 characters in length. In this case, 2 x 2 arrays of sub-blocks are used for each representation of the data. The 48 bit cells 1208B sunounding control bits 1208A are interpreted as the first six ASCII characters. The 64 bit cells of each of adjoining block 1220 are inteφreted as representing ώe next 8 additional characters. The 64 bits cells of sub-block 1222 are inteφreted as representing ώe next 8 characters. And ώe 64 bit cells of sub-block 1224 are inteφreted as representing ώe final 8 characters. In ώis case, there is just one group of control bits for four sub-blocks. And ώe control/message data is repeated 16 times wiώin each signature block 1204 of ώe image. If control bits #5 and #6 are both "l "s, ώis indicates an ASCII message of programmable length. In ώis case, the reading software examines the first 16 bit cells 1208B sunounding ώe control bits. Instead of inteφreting ώese bit cells as message bits, ώey are inteφreted as additional control bits (the opposite of the case described above, where bit cells normally used to represent control bits represented message bits instead). In particular, the reading software inteφrets ώese 16 bits as representing, in binary, the length of ώe ASCII message. An algorithm is then applied to ώis data (matching a similar algorithm used during ώe encoding process) to establish a corresponding tiling pattern (i.e. to specify which sub-blocks convey which bits of the ASCII message, and which convey control bits.) In ώis programmable-length ASCII message case, control bits are desirably repeated several times within a single representation of the message so ώat, e.g., there is one set of control bits for approximately every 24 ASCII characters. To increase packing efficiency, the tiling algorithm can allocate (divide) a sub-block so ώat some of its bit-cells are used for a first representation of the message, and others are used for another representation of the message. Reference was earlier made to beginning the decoding of the registered image bv considering ώe "northwestern-most" sub-block 1206 in each signature block 1204. This bears elaboration. Depending on the data format used, some of the sub-blocks 1206 in each signature block 1204 may not include control bits. Accordingly, ώe decoding software desirably determines the data format by first examining ώe "northwestern-most" sub-block 1206 in each signature block 1204; the 16 bits cells in ώe centers of these sub-blocks will reliably represent control bits. Based on the value(s) of one or more of these bits (e.g. ώe Digimarc Beta Data Format bit), ώe decoding software can identify all oώer locations throughout each signature block 1204 where ώe control bits are also encoded (e.g. at ώe center of each of ώe 64 sub-blocks 1206 comprising a signature block 1204), and can use ώe larger statistical base of data ώereby provided to extract ώe remaining control bits from ώe image (and to confirm, if desired, ώe earlier control bit(s) determination). After all control bits have ώereby been discerned, ώe decoding software determines (from ώe control bits) ώe mapping of message bits to bit cells throughout ώe image. To reduce ώe likelihood of visual artifacts, the numbering of bit cells within sub-blocks is alternated in a checkerboard-like fashion. That is, the "northwestem-most" bit cell in the "northwestern- most" sub-block is numbered "0." Numbering increases left to right, and successively through ώe rows, up to bit cell 63. Each sub-block diametrically adjoining one of its comers (i.e. sub-block 1224) has ώe same ordering of bit cells. But sub-blocks adjoining its edges (i.e. sub-blocks 1220 and 1222) have ώe opposite numbering. That is, the "northwestem- most" bit cell in sub-blocks 1220 and 1222 is numbered "63." Numbering decreases left to right, and successively through ώe rows, down to 0. Likewise throughout each signature block 1204. In a variant of ώe Digimarc beta format, a pair of sub-blocks is used for each representation of ώe data, providing 128 bit ceils. The center 16 bit cells 1208 in the first sub- block 1206 are used to represent control bits. The 48 remaining bit cells in ώat sub-block, together with all 64 bit cells 1208 in ώe adjoining sub-block 1220, are used to provide a 1 12-bit message field. Likewise for every pair of sub-blocks throughout each signature block 1204. In such an arrangement, each signature block 1204 thus includes 32 complete representations of the encoded data (as opposed to 64 representations in the earlier-described standard). This additional length allows encoding of longer data strings, such as a numeric IP address (e.g. URL). Obviously, numerous alternative data formats can be designed. The particular format used can be indicated to ώe decoding software by values of one or more control bits in ώe encoded image. Other Applications Before concluding, it may be instructive to review some of ώe oώer fields where principles of applicant's technology can be employed. One is smart business cards, wherein a business card is provided with a photograph having unobtrusive, machine-readable contact data embedded ώerein. (The same function can be achieved by changing ώe surface microtopology of ώe card to embed ώe data therein.) Another promising application is in content regulation. Television signals, images on ώe internet, and oώer content sources (audio, image, video, etc.) can have data indicating their "appropriateness" (i.e. ώeir rating for sex, violence, suitability for children, etc.) actually embedded in ώe content itself rather ώan externally associated ώerewiώ. Television receivers, intemet surfing software, etc., can discern such appropriateness ratings (e.g. by use of universal code decoding) and can take appropriate action (e.g. not permitting viewing of an image or video, or play-back of an audio source). In a simple embodiment of ώe foregoing, the embedded data can have one or more "flaa" bits, as discussed earlier. One flag bit can signify "inappropriate for children." (Others can be. e.g., "ώis image is copyrighted" or "this image is in the public domain.") Such flag bits can be in a field of control bits distinct from an embedded message, or can — themselves — be ώe message. By examining ώe state of ώese flag bits, the decoder software can quickly apprise the user of various attributes of ώe image. (As discussed earlier, control bits can be encoded in known locations in ώe image - known relative to ώe subliminal graticules — and can indicate ώe format of ώe embedded data (e.g. its length, its type, etc.) As such, ώese control bits are analogous to data sometimes conveyed in prior art file headers, but in ώis case ώey are embedded within an image, instead of prepended to a file.) The field of merchandise marking is generally well served by familiar bar codes and universal product codes. However, in certain applications, such bar codes are undesirable (e.g. for aesthetic considerations, or where security is a concern). In such applications, applicant's technology may be used to mark merchandise, eiώer through in innocuous carrier (e.g. a photograph associated with ώe product), or by encoding the microtopology of ώe merchandise's surface, or a label thereon. There are applications — too numerous to detail — in which steganography can advantageously be combined with encryption and/or digital signature technology to provide enhanced security. Medical records appear to be an area in which authentication is important. Steganographic principles — applied either to film-based records or to ώe microtopology of documents — can be employed to provide some protection against tampering. Many industries, e.g. automobile and airline, rely on tags to mark critical parts. Such tags, however, are easily removed, and can often be counterfeited. In applications wherein better security is desired, industrial parts can be steganographically marked to provide an inconspicuous identification/authentication tag. In various of ώe applications reviewed in ώis specification, different messages can be steganographically conveyed by different regions of an image (e.g. different regions of an image can provide different internet URLs, or different regions of a photocollage can identify different photographers). Likewise with other media (e.g. sound). Some software visionaries look to ώe day when data blobs will roam ώe datawaves and interact wiώ other data blobs. In such an era, it will be necessary for such blobs to have robust and incorruptible ways of identifying themselves. Steganographic techniques again hold much promise here. Finally, message changing codes — recursive systems in which steganographically encoded messages actually change underlying steganographic code patterns — offer new levels of sophistication and security. Such message changing codes are particularly well suited to applications such as plastic cash cards where time-changing elements are important to enhance security. Again, while applicant prefers ώe particular forms of steganographic encoding detailed above, the diverse applications disclosed in ώis specification can largely be practiced wiώ other steganographic marking techniques, many of which are known in ώe prior art. Likewise, while ώe specification has focused on applications of ώis technology to images, ώe principles thereof are generally equally applicable to embedding such information in audio, physical media, or any oώer carrier of information. Finally, while ώe specification has been illustrated wiώ particular embodiments, it will be recognized that elements, components and steps from ώese embodiments can be recombined in different arrangements to serve different needs and applications, as will be readily apparent to those of ordinary skill in the art. POINTER TO A HEADER, AND/OR, POINTER TO A PROCESS The basic philosophy sunounding the use of a "header" in a digital file has already been discussed, along with the idea that steganographically encoded information can serve many of ώese functions independently or in parallel with a traditional header. This section attempts to further clarify this notion as well as further clarify the notion that attached information can act as a "pointer" to a remote database containing ώe information that might be otherwise contained in a header. Refer to the sections on header verification, ώe "bodier", and network linking meώods and hot objects in general. This section also expands upon the idea that header information and other kinds of attached information can be more than simply passive descriptive information, but can be fully executable code or other forms of "active information and network links." As previously developed in the section on hot objects, given that realtime connection of a desktop computer, a television set or a stero system onto a network is becoming more and more commonplace, the absolute need for an attached header replete with full information about a data object is diminishing, and the possibility of simply pointing to a remote yet connected database is becoming more and more practical. The persistent and immediate connection to the information is more fundamental than strictly needing the full information attached to the data object (where time to change information presents a particular problem for object-attached information). Referring back to Fig. 16, and harkening back to the discussion on networked linking meώods, we find the situation depicted in Fig. 60. The header information "joe's image" is now only located on some accessible remote database, and the information attached to the object in either the actual header, or steganographically embedded, or both, now contains simply an address to the remote database location. Software applications would then be manufactured such that a networked connection would be assumed to be either active or capable of becoming so rather swiftly, and that when a data object such as an image or a video clip was read in, ώe embedded address of the header information would be read, a quick retrieve of ώe database information would be performed, and the remote header information would be used just as if it were locally attached to ώe data object. It is important to note that the stegonographic embedded information does not necessarily need to be present in order to make such a system function, and vice versa, the traditional header need not be present if steganographic information is present. The two together represent a value-added redundancy and greater assurance ώat the link between the remote information and the object remain intact. Likewise, even if ώere does exist some "master remote header" for a given data object, an "static-in-time copy" (by definition) of that master header file can still travel with the data object for ώe puφoses of speed or for the puφoses of servicing non-networked sties or applications, with reference back to the master if need be for any latest updates. Likewise, though the principles of ώis application focus largely on audio-visual signals and data objects, many of these design principles apply to text files, software programs, basic data records, and the like, where steganography per se may not even be applicable. Likewise, any oώer kind of information can certainly still be attached to an object in addition to the above mentioned "header" information. Another way to look at it is that this new "header address" can simply be appended to or incoφorated into any existing header format such as JPEG, TIFF, etc. It is also important to note that ώe information stored remotely need not simply be "passive information" such as a person's name or ώe latest price for some peddled item. It can also be computer instructions or software executable code that is associated with ώe data object. Thus a software application can read in a data object, reading at the same time the remote database address, retrieve via a network the remote connection, then either display the remote information along with the data object information itself, or actually launch some local application much like JAVA based systems currently function, or the remote database can actually ship executable code which may ώen be invoked locally on the machine where the data object has been read. In this fashion, a data object becomes a first stepping stone into some computing experience usually related to the content of the object itself, much as a cover of a book is a quick visual summary of the book's content where the challenge becomes enticing third parties to enter. The notion of a singular string of attached bits referring to a remote database wherein a master header is located we shall henceforth refer to as an "identity pointer". We shall refer to ώe database location containing the master information connected with that data object as the "master identity." As is well known even in the prior art of traditional mail, addresses can be hierarchical in nature, with ever finer levels of granularity in searching for the ultimate central location or master identity. So too with identity pointers, they can actually be a single string of hierarchical information parsed according to where an application or machine needs to go in order to retrieve the full information associated wiώ the data object and/or audio/visual signal. Thus, a sub-string inside ώe identity pointer might indicate, through proper programming of an application or instrument including address redirection tables, ώat the master identity for such and such object exists inside one's own computer at such and such directory and in such and such file. On the other hand, a first section of the identity pointer might indicate a search is required on the world wide web for a particular rights management database or commerce server database, a second section of the identity pointer ώen "points" to an address at this particular database, wherein what is pointed to may be the ultimate location of the master identity, or the pointed-to location is actually a database of further pointers to a multitude of databases, indicating that a third section of the identity pointer now has an address location of the ultimate master identity. This formula can clearly recurse further into fourth, fifth, and so on layers. The point is that it is not strictly required to have ώe identity pointer only point to some singular master database, but that there will in fact be a multitude of interconnected databases which will be the rule rather than the exception. This includes databases which are more traditionally viewed as belonging in some kind of network which is not necessarily as large as the internet, such as local area networks, or even a personal computers series of hard drives, or whatever. The main overall point is that the identity pointer contains sufficient information in order to be able to find the master identity, where it is a given that an application or instrument can read the master pointer, inteφret it, and do the searching and retrieving of the master identity. Those skilled in the art may notice that so far in this section no mention has been made about encrypting either the identity pointer or the master identity or any communications involved in the overall process. Such improvements can certainly be made and enhance more of the "secure identification" aspects of an attached — - information system, or the related "verification" function that many commerce applications will be seeking. The basic principles of the invention can nevertheless be followed. The identity pointer can also point to locations which are not (currently) viewed as strictly a database. For example, a data object can contain a sub-string which points to a particular application that needs to be launched in order to resolve ώe remainder of the identity pointer string. A more specific example of the general example might be that a sub-string in the identity pointer instructs a machine to launch a web browser, and the remainder of the string would be a traditional URL to some web page containing basic contact information to the creator of a video production. Users on a local machine might program ώe machine to their own liking in that they would "allow" content with ώese abilities to go ahead and launch browsers when instructed, or they can tell their local machine to ignore ώese requests by a given piece of content. Likewise, an sub¬ string in ώe identity pointer might instruct a multimedia database interface program to be launched and the location of the object in question sought, found, and displayed. It can be readily seen that the general principles of the invention would indicate that applications and instruments which deal with data objects require less and less local intelligence regarding "inteφreting and operating upon" attached information, yet instead they become better and better at being realtime connected to a broader network and know how to properly find and retrieve information from various locations on ώat broader network. Moreover, applications and instruments so enabled would also make use of well-known-in-the-art methods of launching new execution threads or entire new applications based on ώe retrieved information. The physical location of the master identity, as well as the "level" it exists on in the network hierarchy (from CPU RAM, through Operating System, through local storage disks, through local networks, up through global networks such as the Internet) becomes immaterial so long as an application or instmment is equipped with the realtime ability to find and retrieve the pertinent information. We now turn toward a more detailed description of the drawings. Fig. 60 depicts the essential concept behind ώe "master identity object" and a system built around it. ("Master Identity Object", or MIO, is here used as a convenience and should not be construed as limiting ώe kind of information pointed to by an embedded pointer; i.e., the MIO could easily point to executable code or executable sequences for example rather than identity information). Indeed, ώe system is essential to ώe definition of ώe object. Quite simply, a header is replaced by a pointer to a header, and the header information itself is located in some accessible location. The pointer 1690 represents whatever complete address information is needed in order for an instrument or application to locate the header information, 1694, located in some binary storage location (or database) 1692. The data stream 802 is whatever normal data that might be contained in a typical digital image file or binary record or software program or whatever. The pointer, 1690, can also be embedded steganographically as depicted, or alternatively, it can ONLY be associated with the data stream steganographically. Fig. 61 begins to dissect Fig. 60, trying to depict a closer view of a practical MIO ("master identity object"). Reverence is paid to the idea ώat common data objects and common data formats will be used for some time to come, and in many ways ώe MIO information will be appended. Thus we start with a traditional header 1700 and the associated data stream 802. The MIO pointer is now split out into three pieces, a "tag" which identifies the following information as a pointer to a remote database. The pointer is now conveniently split into a pointer to a pointer 1704, effectively giving the address of some database in a sea of potential databases, while pointer 1706 is the ultimate address on the found database pointed to by 1704. Indeed, 1704 could also be split out, just as in surface mail type addressing. Fig. 61 also indicates that multiple layers of MIO information can be added to an object. One example of this would be that an organization would track and account for all incoming and outgoing data objects and wish to provide a pointer to that given objects lifetime within the organizations data network, yet respect any previous MIO information that ώe creator or distributor of a data object already attached to that object. There are numerous other examples, including derivative work applications or situations where a data object evolves. Fig. 62 takes a closer look at the connectivity components of an MIO system, looked at through the eyes of an instrument or application needing to retrieve the master identity information for a given MIO-enabled data object. An arbitrary data object 1710 enters a highly genericized "node" 1712. Typically, this node might be an "open file" procedure in a software application, or it might be a "search for all image files" operation initiated in a computer's operating system, or it might be a digital receiver in a television set. The node 1712 is meant to represent the place where a given data object enters a given instrument or application and is scanned for MIO traits. The node 1712's output is simply ώat the data object either is or is not an MIO type object. If it discovers an MIO object as in 1716, it must next ask the simple question of whether or not the given instruments cares or not, essentially asking whether or not to go fetch the information pointed to by ώe embedded pointer. If it is not configured to do so, it goes on with its work, 1718. If it is configured to go fetch the associated remote information (1720), it then simply goes through a basic address resolution process to locate the master identity. A first low level search might be typical wherein the "master identity" (which we have amply explained might actually be a search for further instructions rather than only "looking for a name") is potentially found on a usef s own desktop computer or local instrument. This might be as low level as a call to some memory location in the CPU system 1724, perhaps retrieving and therefor instigating instructions to prepare for video information to follow. Or it might be a pointer 1726 to some location in a display device, perhaps as a precursor to understanding the cunent display settings that a user is operating under. The need for multiple layers of MIO information as depicted in Fig. 61 becomes more apparent in ώese examples as well, in that the lower level retrieving will often be precursors to more advanced actions. Likewise, an address might be found in the operating system 1728, ROM, RAM, hard drives, etc. (1730). A token indication of a firewall 1732 is depicted showing that some addresses will only be accessible to a "local object" by design. Finally, perhaps the more common use of true "identity" will come from a route to a broader network 1734. Depicted is a certain remote database 1736 where the given data object might have its master identity. These two basic ideas of "local" identity and "remote" identity are depicted below as 1738 and 1740, but it should be appreciated that these can become quite blurry in their distinctions. It should be understood that one object of this invention is that, for many practical systems, ώe locating process of finding and retrieving the master identity information or instructions is meant to take place in realtime, that is, is typically less than a second or two ideally, and no more than ten seconds or twenty seconds even in case where there is in the multiples of kilobytes to retrieve. This kind of time performance is generally possible today on the internet even with sub- 100Kbit per second modems, though less than a second is generally out of reach of cunent modem- based systems using standard phone lines and standard internet communications protocols. Fig. 63 traces out a generic process flow within an instrument or application with a slightly different twist than depicted in Fig. 62. Again, any device capable of processing a data object reads said object in, 1750, and asks ώe simple question of whether or not remote associated information exists for that object 1752. If not, fine, 1754, go about your way. If so, it asks should I care, 1758, if not (1760, go about your way. If so, 1762, go fetch the information (or series of information if ώere is recursive MIO information). Box 1766 ώen explicitly shows that the user's device will still effect what happens even after the remote data is retrieved. The permutations and combinations of operations from this point forward are as broad as general computing principles permit. As one tiny example, the user may have configured ώe device such that if ώe retrieved information does not match certain "key words" that the user has chosen, then don't even bother displaying an image (assuming that is the data object in question). Essentially, the retrieved information from 1764 and the user configuration 1766 set up a generally unlimited set of new instructions to ώe device. Another example might be that the user configuration 1766 says to go ahead and follow whatever instructions come back with the retrieved information, such as "launch a browser and follow an URL to some JAVA web site and don't mind sending out my credit card information if they ask". This is deliberately melodramatic to illustrate that the basic branching at 1770 is as broad as general computing principles permit. Fig. 64 briefly illustrates that both embedded information as well as pointed-to information can actually sequences of instructions, including decision branched instructions, not merely singular instructions or singular blocks of passive descriptive information. This idea is encapsulated in the word "script" as is generally well known in the art of computing. Fig. 65 essentially depicts an MIO system operated in "reverse" from that of ώe previous few figures. In general, ώe idea here is ώat the master identity wants to know and collect all migration activities of its associated data object, and the system depicted in Fig. 65 is essentially a way to do ώis. Again, we see a generic data object 1820 entering some generic application or instrument 1822, here referred to as a "reporting node". The first question still is "are you an MIO?" 1824. 1826 alludes to the idea of reporting non-hits to some kind of master MIO system database 1830, a database which might track the broad traffic pattems of MIO object vs. Non-MIO objects. If the node finds an MIO, ώe next logical question is "may" I send a message to its master identity in order to inform the master identity that this object passed this location at this particular time? (1834). This is important since there will be many situations due to privacy and security considerations where such reporting would be intolerable 1834. However, ώere may also be many situations where ώis reporting information is valuable to all users of MIO based systems, and a "count" message will be sent to ώe master identity location, where locating the master identity follows previous descriptions. Details of how the master identity collects messages are left silent here as this form of data collection technology is readily available in today's marketplace and computing systems, where ultra-mundane methods such as sending a self-contained e- mail to some e-mail address might even suffice as a methodology of counting (horribly inefficient but indicative of the vast anays of meώods to have a master identity database keep track of hundreds if not millions of copies of "itself). In view of the wide variety of implementations and applications to which the principles of this technology can be put, it should be apparent that the detailed embodiments are illustrative only and in no way limit the scope of my invention. I claim as my invention all such embodiments as come within the scope and spirit of the following claims and equivalents thereto.

Claims: CLAIMS 1. A multi-computer system including a network for embedding and reading a watermark, the system comprising: a first digital electrical computer system comprising a first digital electrical computer electrically connected to a first input device, to a first output device, and to a first memory storing a plurality of creator identifiers and creator contact data corresponding to each of the creator identifiers; a second digital electrical computer system comprising a second digital electrical computer electrically connected to a second input device and to a second output device, the second digital electrical computer being programmed to embed a watermark in a digital photographic image, the watermark including one of the plurality of creator identifiers; a third digital electrical computer system comprising a third digital electrical computer electrically connected to a third input device and to a third output device, the third digital electrical computer being programmed to read the watermark in the digital photographic image to reveal the one of the plurality of creator identifiers; a network for communicating the revealed one of the plurality of creator identifiers to the first digital electrical computer to obtain the creator contact data conesponding to the one of the plurality of creator identifiers form the memory. 2. The system of claim 1, wherein: the second digital electrical computer is programmed to automatically detect for a watermark when an image is first examined by the second digital electrical computer. 3. The system of claim 1, wherein: the second digital electrical computer is programmed to selectably detect for a watermark when an image is examined by the second digital electrical computer. 4. The system of claim 1, wherein: the network includes the Internet; the watermark includes information identifying a World Wide Web site; and wherein the third digital electrical computer system is programmed to load a World Wide Web browser and connect to the World Wide Web site in response to the revealed one of the plurality of creator identifiers. 5. The system of claim 1, wherein: the watermark includes extended data including at least one member from the group consisting of an organization identifier, a transaction identifier, and an item identifier. 6. In a television set, audio stereo system, computing system, or other system capable of receiving, storing, transmitting, or processing an audio or visual signal, an improvement comprising: means for adding to an initial audio or visual signal an additional digital information associated with the initial signal, ώe additional information containing, in whole or in part, an identification number which is uniquely assigned to the individual initial signal or copies thereof, wherein the identification number represents an address to a location in a remote database wherein a prestored set of information associated with the initial signal is contained, and means coupled to the system containing a processor responsive to basic operating instructions for enabling the reading of the additional information and enabling the realtime retrieving from ώe remote database a subset of the prestored information via a communications link with the remote site. 7. The system of claim 6, wherein the prestored set of information comprises identification and description information about the signal or an object contained in or represented by the signal. 8. The system of claim 6, wherein the prestored set of information comprises a set of machine instructions executable to perform a task relative to the initial signal or about an object contained in the initial signal. 9. The system of claim 6, wherein the prestored set of information comprises a menu of options or commands. 10. The system of claim 1, including means for steganographically embedding the additional information in the initial signal. 1 1. The system of claim 1, wherein there also exists a second number as part of the additional information, wherein the second number is the address or identifier of the database itself to which the first identification points inside, in the context of multiple databases serving a multitude of audio and visual signals. 12. The system of claim 4, first and second numbers steganographically embedded in the initial signal. 13. The system of claim 1 , wherein there is additionally an instrument or receiving station which monitors a multiple number of audio/visual public channels searching and recording the identification numbers and reporting sitings. 14. In an image processing method that includes steganographically decoding an input two-dimensional image to extract a multi-bit code ώerein, the image comprising a two-dimensional array of pixels, an improvement comprising: transforming the image into the spatial frequency domain; pattern matching the transformed image so spatial frequencies obtained by said transforming step coincide with reference spatial frequencies, to thereby effect registration of the transformed image; inverse-transforming the transformed image to yield a registered image; identifying, in the registered image, a plurality of regions that encode a first control bit, said regions being distributed through the registered image in a regular array; performing a statistical analysis over at least said plurality of regions to determine whether the first control bit has first or second values; if said control bit has the first value, performing a first decoding process on ώe image to extract the code therefrom; and if said control bit has the second value, performing a second decoding process on the image to extract the code therefrom, the second decoding process being different than the first.

Inventors: RHOADS GEOFFREY B, RHOADS, GEOFFREY, B.
Applicants: DIGIMARC CORP, RHOADS GEOFFREY B, DIGIMARC CORPORATION, RHOADS, GEOFFREY, B.
Application year: 1997
Main classification: G06K9
IPC codes: G06K9, G06T1, G10L19, H04N1