Patent Application: US-25713699-A

Abstract:
the spread spectrum image steganography of the present invention is a data hiding / secret communication steganographic system which uses digital imagery as a cover signal . ssis provides the ability to hide a significant quantity of information bits within digital images while avoiding detection by an observer . the message is recovered with low error probability due the use of error control coding . ssis payload is , at a minimum , an order of magnitude greater than of existing watermarking methods . furthermore , the original image is not needed to extract the hidden information . the proposed recipient need only possess a key in order to reveal the secret message . the very existence of the hidden information is virtually undetectable by human or computer analysis . finally , ssis provides resiliency to transmission noise , like that found in a wireless environment and low levels of compression .

Description:
a block diagram of an image steganographic system is depicted in fig1 . a message is embedded in a digital signal such as a signal representing a digital image , by the stegosystem encoder 10 which uses numeric keys or passwords . the resulting stegosignal , i . e ., the stegoimage , is transmitted in some fashion over a channel to an intended recipient where it is processed by the stegosystem decoder 12 using the same key . during transmission the stegoimage may be monitored by viewers who will notice only the transmittal of the innocuous image without discovering the existence of the hidden message . techniques of spread spectrum communication , error control coding , and image processing are combined to accomplish ssis . the major processes of the stegosystem encoder 10 are portrayed in fig2 . the message , after optional encryption 20 with key 1 , is encoded via an error correcting code 22 producing the encoded message , m . the sender enters key 2 into a wideband pseudorandom noise generator 24 , generating a spreading sequence , n . subsequently , the modulation scheme 26 is used to spread the narrowband spectrum of m with the spreading sequence n , thereby composing the embedded signal , s , which is then input into an interleaver 28 . the operation of the interleaver 28 may be dictated by key 3 . the interleaved signal is now combined with the cover image , to produce the stegoimage g which has been appropriately quantized by quantizer 30 to preserve the initial dynamic range of the cover image . the stegoimage is then transmitted in some manner to the recipient . at the receiver , the stegosystem decoder 12 , shown in fig3 uses an image restoration filter 40 followed by deinterleaving module 42 to construct an estimate of the embedded signal ŝ from the received stegoimage ĝ . the recipient , maintaining the same key 2 as the sender , replicates the spreading sequence n with pseudorandom noise generator 44 . the encoded message is then demodulated at demodulator 46 with the spreading sequence , and an estimate of the encoded message , { circumflex over ( m )} is constructed . the estimate of the message is then decoded via the error correcting decoder 48 , optionally decrypted at decrypter 50 , and revealed to the recipient . the ability of ssis to hide information is due to the existence of noise encountered during image acquisition . ssis uses inherent noise to hide information within the digital image . since wideband thermal noise , inherent to imagery of natural scenes captured by photoelectronic systems , can be modeled as additive white gaussian noise ( awgn ) this type of noise is included in the ssis system . ssis is able to mimic this inherent noise to hide the secret information within the digital image . in other types of coherent imaging , the noise can be modeled as speckle noise { 17 }, which is produced by coherent radiation from the microwave to visible regions of the spectrum . the concepts of ssis can be extended to imagery with other noise characteristics than those modeled by awgn . the additional noise which conceals the hidden message is a natural phenomenon of the image and therefore , if kept at typical levels , is not perceived by the casual observer or detectable by computer analysis . spread spectrum communication is the process of spreading the bandwidth of a narrowband signal across a wide band of frequencies . this can be accomplished by modulating the narrowband waveform with a wideband waveform , such as white noise . after spreading , the energy of the narrowband signal in any one frequency band is low and therefore difficult to detect . ssis uses a variation of this technique to embed a message , typically a binary signal , within samples of a low power white gaussian noise sequence consisting of real numbers . the resulting signal , perceived as noise , is then added to the cover image to produce the stegoimage . since the power of the embedded signal is much lower compared to the power of the cover image , the snr is also low , indicating low perceptibility and low probability of detection by an observer . subsequently , if embedded signal power is much less than the power of an image , an observer should be unable to visually distinguish the original image from the stegoimage . to construct the embedded signal the present invention incorporates the concept of a stored reference spread spectrum communications system { 18 }. the stored reference principle requires independent generation of identical pseudorandom wideband waveforms at both the transmitter and receiver . this can easily be accomplished by a private or public key { 19 } and identical pseudorandom waveform generators . in addition , the pseudorandom number generators can be cryptographically secure . first , we describe a simple sign modulation scheme to provide an example of our spread spectrum process . this method is similar to the technique used in { 20 }. assume that the message signal , m , is a bilevel signal consisting of {− 1 ,+ 1 } and the spreading sequence , n , is a sequence of real numbers that have a normal distribution with zero mean and some variance , n . the two signals are modulated , or multiplied as in equation ( 1 ), resulting in a sequence of real numbers . in this simple example , the sign of each noise sample is changed corresponding to the value of the message bit to be embedded . the white gaussian characteristics of the signal are preserved . the decoding process is also elementary . the sequence n is replicated at the receiver , and the sign of this sequence is compared to the sign of the received embedded sequence , ŝ , to recover an estimated value of the message signal , { circumflex over ( m )}, as shown in equation ( 2 ). although this very simple system meets the necessary requirements of producing a gaussian sequence regardless of the message signal values , a major deficiency lies within the detection of this signal in the presence of noise . this noise usually results from poor embedded signal estimation but also can be contributed to by the channel during transmission . since only the variation of the sign of embedded signal samples indicates the message , a majority of the values occur in the vicinity of zero . moreover , the distance between s when m =− 1 and s when m =+ 1 is typically small , leading to the problematic detection of m . therefore , in order to improve detection performance , a nonlinear modulation scheme was developed for ssis . this modulation technique provides an increase in the minimum euclidean distance between the possible modulated values , thereby enabling an improved estimate of the embedded signal over traditional sign modulation . a flow chart is shown in fig4 . this is accomplished by first generating a uniformly distributed random sequence u with uniform pseudorandom noise generator 60 using key 2 . a second sequence , u ′, is generated by applying the piecewise linear transformation of equation ( 3 ) to u with transformation procedure 62 . the embedded signal , s , is then formed by selecting bits from these two sequences arbitrated by the message bits m , by switch arbitrator 64 as shown in equation ( 4 ), where φ represents the cumulative distribution function of a standard gaussian random variable , equation ( 5 ), where the inverse of equation ( 5 ) is calculated using the methods presented in { 21 }. to adjust the power of the embedded signal a scale factor may be applied to the embedded signal s in order to further improve detection performance . this signal is then added to the cover image , the result after quantization is the stegoimage . u ′ = { u + 0 . 5 , 0  u ≺ 0 . 5 u - 0 . 5 , 0 . 5  u  1 . 0 ( 3 ) s = { φ - 1  ( u ) , m = + 1 φ - 1  ( u ′ ) , m = - 1 ( 4 ) φ  ( x ) = 1 2   π  ∫ - ∞ x   - y 2 2    y ( 5 ) once obtained at the decoder , the estimate of the embedded signal , ŝ , is then compared with identical copies of the pseudorandom wideband waveforms , u and u ′, used at the encoder to produce an estimate of the hidden message { circumflex over ( m )}. the generation of the identical pseudorandom wideband waveforms is accomplished by the possession of a common key 2 known only to the sender and receiver , that is used as a seed for duplicate random number generators . the method of key encryption may be chosen depending on the level of security desired . at the decoder , the stegoimage is obtained and image processing techniques are used to estimate the embedded signal without knowledge of the original cover in order to avoid the need for a cover image escrow . by exercising image restoration techniques at restoration filter 40 an estimate of the embedded signal can be obtained by subtracting a version of the restored image { circumflex over ( f )} from the stegoimage ĝ , as shown in fig3 . since the pixels of a digital image are highly correlated among neighboring pixels in natural scenes , filtering operations can be used to restore the stegoimage so that it resembles the original image . the restored image { circumflex over ( f )} can be obtained with a variety of image processing filters , such as mean or median filters , or wavelet shrinkage techniques { 22 }, and adaptive wiener filtering techniques { 23 }. however , the most favorable performance , determined by the quality of the recovered embedded signal , was obtained experimentally with alpha - trimmed mean filtering { 27 }. even though the image restoration yields good performance , the estimate of such a low power signal necessary to provide the degree of imperceptibility essential for a steganographic system , is still rather poor . therefore , in order to compensate for the suboptimal performance of the signal estimation process , we have incorporated the use of error control coding . the probability of error encountered during the estimation process will be referred to as the embedded signal bit error rate ( ber ). the use of error correcting coding by ssis compensates for the suboptimal estimation of the embedded signal and combats distortion which may be encountered during the transmission process . the demodulated message signal may have a substantial number of bit errors , indicated by a high embedded signal ber . when a large number of errors are expected to occur in a block of data , an error - correcting code must be used to correct them . error correcting codes within the ssis system allow the hidden message to be recovered without error when the transmission channel is noiseless , thus compensating for the noise estimation process . when the transmission channel is expected to be noisy , the appropriate low rate error correcting code can be selected to provide desired performance . any error correcting code that has the capability to correct the signal estimation ber can be used within ssis . for example , binary expansions of reed - solomon codes { 25 } were successfully implemented with the ssis system of this application . these codes can correct many binary errors if a decoder is used that corrects bits instead of reed - solomon code symbols . the decoders described in { 25 } are based on a simple idea of bossert and hergert { 26 }: if we have a large number of low weight parity checks , then the number of failing parity checks tends to be proportional to the number of errors . using this idea , we can change whichever bits reduce the number of failing parity checks until no checks fail . this algorithm works very well with binary expansions of low rate reed - solomon codes because they have a large number of low weight parity checks . with some other improvements described in { 25 }, these decoders can correct far more binary errors than conventional reed - solomon decoders for the same codes . for example , the ( 2040 , 32 ) decoder corrects most error patterns with fewer than 763 bit errors , while a conventional reed - solomon decoder would be limited to 125 symbol errors , which is typically about 165 bit errors . the rate of this ( 2040 , 32 ) code is similar to that of a ( 64 , 1 ) repetition code , but because it has a much longer block length , its decoded error rate drops much more quickly as the fraction of errors per block is reduced . the ssis system of the present has been implemented successfully on a standard unix computer . the source code of a preferred implementation is attached as appendix a . in general , by increasing the snr , the performance of embedded signal estimation is improved at the cost of some imperceptibility . in order to provide more insight into the presented methodology , a comparison between the original image pixels of a test image and the stegoimage pixels is presented in fig5 . here a single row of pixels has been extracted from both an original 512 × 512 image of an lav - 25 military vehicle containing 256 kilobytes of data and the corresponding stegoimage embedded with high snr . in this example the hidden message is a compressed ascii file containing the 1783 treaty of paris which ended the american revolutionary war . the steganographic snr , the ratio of embedded signal power to cover image power , for the cover images is − 35 db . the embedded signal ber is 0 . 25 , requiring the use of a ⅙ convolutional code { 28 } with soft - decision decoding with a viterbi algorithm { 29 } and the use of side information obtained from the edges of the stegoimage . this coder can correct a block that is 27 % in error . this yields a payload of 5 . 4 kilobytes of hidden information . depending on the error correction method used , still higher payloads may be obtained . it is evident from the graph that slight discrepancies between the two exist . however these discrepancies are undetectable by human observer . furthermore , without possession of the original image , the embedded signal is undetectable by statistical analysis by computer . additional protection can be provided for scenarios where additional errors are expected from the transmission process , such as those encountered in wireless environments or in lossy image compression , by using lower rate codes than those dictated by the signal estimation ber . while the primary thrust of the invention of this application is directed to embedding of messages in digital images , the methods of the present invention could be used embed messages in virtually any kind of broadband digital signal such as high fidelity digital audio . the restoration filter would be designed to recover the particular information transmitted , such as digital audio . other such modifications would be readily apparent to those of skill in the art . it will be readily seen by one of ordinary skill in the art that the present invention fulfills all of the objects set forth above . after reading the foregoing specification , one of ordinary skill will be able to effect various changes , substitutions of equivalents and various other aspects of the present invention as broadly disclosed herein . it is therefore intended that the protection granted hereon be limited only by the definition contained in the appended claims and equivalents thereof . having thus shown and described what is at present considered to be preferred embodiments of the present invention , it should be noted that the same have been made by way of illustration and not limitation . accordingly , all modifications , alterations and changes coming within the spirit and scope of the present invention are herein meant to be included . { 1 } d . kahn . the codebreakers — the story of — secret writing . scribner , new york , n . y ., 1967 . { 2 } b . pfitzmann . trials of traced traitors . in r . anderson , editor , information hiding , first international workshop . lecture notes in computer science , pages 49 - 64 . springer - verlag , berlin , 1996 . { 3 } c . osborne r . van schyndel , a . tirkel . a digital watermark . proceedings of 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