Patent Publication Number: US-2023137496-A1

Title: Multimedia piracy detection with multi-phase sampling and transformation

Description:
TECHNICAL FIELD 
     The disclosed subject matter relates to piracy detection and, more particularly, to multimedia piracy detection with multi-phase sampling and transformation. 
     BACKGROUND 
     Media piracy is a longstanding issue. When video home system (VHS), tapes were in mainstream use, some fraudsters purchased or rented a VHS tape and made physical copies for resale, undercutting the sale of authorized copies. More recently, fraudsters engaged in the illicit creation of digital video discs (DVDs) by renting or buying a single original copy, and then making unauthorized copies for resale, again, undercutting authorized copies for sale. 
     The proliferation of the internet, along with increases in internet speeds, have enabled fraudsters to profit from significantly more exploited, unauthorized content. For example, a fraudster, being a legitimate subscriber of a digital media content provider’s services, can obtain media contents (intended for the subscriber’s viewing only) from a valid source, such as the content provider’s streaming web or mobile application. The fraudster can capture the content using a number of available methods, then resell the contents illegally. One such method is camripping of a just-released movie, then streaming the content to the fraudster’s subscribers via the internet in near real-time, thus generating a large profit for the fraudster at the expense of the original media content owner/producer. Additionally, music and movies can be illegally shared on file sharing platforms. Such examples, among others, can lead to tremendous financial and reputational losses for content creators and media corporations, as pirated videos have been reported to receive over 230 billion views per year. 
     The above-described background relating to piracy detection is merely intended to provide a contextual overview of some current issues and is not intended to be exhaustive. Other contextual information may become further apparent upon review of the following detailed description. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a block diagram of an exemplary system in accordance with one or more embodiments described herein. 
         FIG.  2    is a block diagram of an exemplary system in accordance with one or more embodiments described herein. 
         FIG.  3    is a block diagram of an exemplary system in accordance with one or more embodiments described herein. 
         FIG.  4    is a block diagram of an exemplary system in accordance with one or more embodiments described herein. 
         FIG.  5    is a block diagram of an exemplary system in accordance with one or more embodiments described herein. 
         FIG.  6    is a block diagram of digital media distribution in accordance with one or more embodiments described herein. 
         FIG.  7    is a block diagram of piracy medium distribution in accordance with one or more embodiments described herein. 
         FIG.  8    is a block diagram of multi-stage machine training phases in accordance with one or more embodiments described herein. 
         FIG.  9    is a block diagram of multi-stage sampling during real-time rendering phases in accordance with one or more embodiments described herein. 
         FIG.  10    is a flowchart for a process associated with multimedia piracy detection in accordance with one or more embodiments described herein. 
         FIG.  11    is a block flow diagram for a process associated with multimedia piracy detection in accordance with one or more embodiments described herein. 
         FIG.  12    is a block flow diagram for a process associated with multimedia piracy detection in accordance with one or more embodiments described herein. 
         FIG.  13    is a block flow diagram for a process associated with multimedia piracy detection in accordance with one or more embodiments described herein. 
         FIG.  14    is an example, non-limiting computing environment in which one or more embodiments described herein can be implemented. 
         FIG.  15    is an example, non-limiting networking environment in which one or more embodiments described herein can be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     The subject disclosure is now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the subject disclosure. It may be evident, however, that the subject disclosure may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing the subject disclosure. 
     As alluded to above, multimedia piracy detection can be improved in various ways, and various embodiments are described herein to this end and/or other ends. 
     According to an embodiment, a system can comprise a processor, and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising: determining (e.g., based on a time domain and/or a frequency domain associated with original digital media content and/or respective digital transportation and using a counterfeit digital media content model) whether digital media content comprises a counterfeit of an authorized release of the original digital media content, wherein the counterfeit digital media content model has been generated based on machine learning applied to time domains and/or to frequency domains of past original digital media content other than the original digital media content (e.g., and respective digital transportation) and to past digital media content (e.g., and respective digital transportation) other than the digital media content (e.g., and respective digital transportation), and wherein the past digital media content comprises counterfeit digital media content, and based on a determination that the digital media content comprises the counterfeit, flagging the digital media content as being counterfeit. In further embodiments, the operations can further comprise determining and/or flagging a likelihood or probability (e.g., using the counterfeit digital media content model) that the digital media content is counterfeit. 
     In various embodiments, the counterfeit digital media content model can be further generated based on an output of a process, comprising: concurrently sampling, in the time domain, the past original digital media content and the past digital media content, and based on a first past result of concurrently sampling, in the time domain, the past original digital media content and the past digital media content, determining a second past result, wherein the second past result comprises the frequency domain associated with the first past result. In various implementations, the counterfeit digital media content model can be further based on associated digital transportation of respective digital media content. 
     In one or more embodiments, the counterfeit digital media content model has been further generated by filtering the first past result using a first signal filter and the second past result using a second signal filter. 
     In some embodiments, the counterfeit digital media content model has been further generated based on a result of a comparison between an attribute associated with the past original digital media content (e.g., and respective digital transportation) and an attribute associated with the past digital media content (e.g., and respective transportation). In this regard, determining whether the digital media content comprises the counterfeit can be further based on a result of a comparison between an attribute associated with the original digital media content (e.g., and respective digital transportation) and an attribute associated with the digital media content (e.g., and respective digital transportation). 
     It is noted that the attribute associated with the original digital media content can comprise an implicit attribute. In various embodiments, the implicit attribute can comprise a compression attribute associated with the original digital media content and/or a start time associated with the original digital media content or an end time associated with the original digital media content. 
     It is additionally noted that the attribute associated with the original digital media content can comprise an explicit attribute. In various embodiments, the explicit attribute can comprise a start time or an end time of an event associated with the original digital media content and/or advertisement information representative of an advertisement associated with the original digital media content. 
     In another embodiment, a non-transitory machine-readable medium can comprise executable instructions that, when executed by a processor, facilitate performance of operations, comprising: determining, based on a frequency domain associated with original digital media content and using a counterfeit digital media content model, whether digital media content comprises a counterfeit of an approved release of the original digital media content, wherein the counterfeit digital media content model has been generated based on machine learning applied to time domains and frequency domains of past original digital media content other than the original digital media content and to past digital media content other than the digital media content, and wherein the past digital media content comprises counterfeit digital media content, and based on a determination that the digital media content threshold satisfies a defined counterfeit criterion, determining that the digital media content comprises potentially counterfeit digital media content. 
     In various embodiments, the original digital media content can comprise a segment of video or audio content. 
     In one or more embodiments, the above operations can further comprise: in response to the determination that the digital media content threshold satisfies the defined counterfeit criterion, suspending network connectivity between a device determined to be associated with the digital media content and a network employed by the device to transmit content comprising the digital media content. 
     In some embodiments, the above operations can further comprise: in response to the determination that the digital media content threshold satisfies the defined counterfeit criterion, bandwidth throttling a connection between a device determined to be associated with the digital media content and a network via which the device is determined to transmit content comprising the digital media content, wherein bandwidth throttling the connection comprises limiting onward communication speed between the device and the network to less than a communication speed determined to be sufficient for uninterrupted transmission of the digital media content via the network. 
     In an embodiment, the above operations can further comprise: in response to the determination that the digital media content threshold satisfies the defined counterfeit criterion, facilitating a non-broadcasted playback of the potentially counterfeit digital media content, comparing the potentially counterfeit digital media content to the original digital media content, and in response to a determination that the potentially counterfeit digital media content and original digital media content threshold satisfy a similarity criterion, determining that the potentially counterfeit digital media content comprises counterfeit digital media content. 
     It is noted that the digital media content can comprise a live streaming broadcast of an event, prerecorded digital media content, or other content. 
     According to yet another embodiment, a method can comprise: determining, by network equipment comprising a processor, based on a frequency domain associated with original digital media content and using a counterfeit digital media content model, whether digital media content comprises a counterfeit of the original digital media content, wherein the counterfeit digital media content model has been generated based on machine learning applied to time domains and frequency domains of past original digital media content other than the original digital media content and to past digital media content other than the digital media content, and wherein the past digital media content comprises counterfeit digital media content, based on a determination that the digital media content threshold satisfies a defined counterfeit criterion, determining, by the network equipment, that the digital media content comprises potentially counterfeit digital media content, and in response to the determining that the digital media content comprises potentially counterfeit digital media content, sending, by the network equipment to a device communicatively coupled to the network equipment, an alert signal representative of the determination that the digital media content comprises potentially counterfeit digital media content. 
     In various embodiments, the counterfeit digital media content model can be further generated by: concurrently sampling, by the network equipment, in a time domain, the past original digital media content and the past digital media content, and based on a first past result of concurrently sampling in the time domain, the past original digital media content and the past digital media content, determining, by the network equipment, a second past result, wherein the second past result comprises the frequency domain associated with the first past result. 
     In some embodiments, the counterfeit digital media content model can be further generated, by the network equipment, based on a result of a comparison between an attribute associated with the past original digital media content and an attribute associated with the past digital media content, and determining whether the digital media content comprises the counterfeit can be further based on a result of a comparison, by the network equipment, between an attribute associated with the original digital media content and an attribute associated with the digital media content. 
     It should be appreciated that additional manifestations, configurations, implementations, protocols, etc. can be utilized in connection with the following components described herein or different/additional components as would be appreciated by one skilled in the art. 
     Turning now to  FIG.  1   , there is illustrated an example, non-limiting system  102  in accordance with one or more embodiments herein. System  102  can comprise a computerized tool, which can be configured to perform various operations relating to multimedia piracy detection. The system  102  can comprise one or more of a variety of components, such as memory  104 , processor  106 , bus  108 , counterfeit determination component  110 , machine learning (M.L.) component  112 , flag component  114 , and/or alert component  116 . 
     In various embodiments, one or more of the memory  104 , processor  106 , bus  108 , counterfeit determination component  110 , M.L. component  112 , flag component  114 , and/or alert component  116  can be communicatively or operably coupled (e.g., over a bus or wireless network) to one another to perform one or more functions of the system  102 . 
     According to an embodiment, the counterfeit determination component  110  can determine (e.g., based on a time domain and/or a frequency domain associated with original digital media content and/or using a counterfeit digital media content model) whether digital media content comprises a counterfeit of an authorized or approved release of the original digital media content. It is noted that an authorized or approved release can comprise paid and/or licensed stream or copy of such original digital media content. According to an embodiment, the counterfeit digital media content model can be generated based on machine learning (e.g., using M.L. component  112 ) applied to time domains and/or frequency domains of past original digital media content other than the original digital media content and/or to past digital media content other than the digital media content. For example, the M.L. component  112  can analyze both time domains and frequency domains of original movies, TV shows, sports games, or other past original digital media content and past digital media content (e.g., known to be pirated copies of the past original digital media content.) The foregoing can be utilized (e.g., by the M.L. component  112 ) to generate the counterfeit digital media content model which can be leveraged by the counterfeit determination component  110  to determine future pirated/counterfeit digital media content. It is noted that said original digital media content (e.g., audio and/or video content) can comprise a segment of the original media content. In this regard, the system  102  or a respective component, such as the M.L. component  112 , can segment original content into smaller sections (e.g., clips) of said original content. In various embodiments, digital media content herein can comprise live streaming broadcasts of event(s) (e.g., live sports events or debuts of new movies, TV shows, or songs), prerecorded digital media (e.g., existing movies, TV shows, or songs), or other digital media content. 
     According to an embodiment, the flag component  114  can (e.g., based on a determination by the counterfeit determination component  110  that the digital media content comprises the counterfeit) flag digital media content as being counterfeit. In further embodiments, the flag component and/or M.L. component  112  can determine a likelihood/probability (e.g., using the counterfeit digital media content model) that the digital media content is counterfeit. Such a flag and/or probability can be appended to or associated with a file associated with the counterfeit digital media content. In further embodiments, the alert component  116  can, in response to a determination (e.g., by the counterfeit determination component  110 ) that digital media content comprises counterfeit or potentially counterfeit digital media content, send, to a device communicatively coupled to the network equipment, an alert signal representative of the determination that the digital media content comprises counterfeit or potentially counterfeit digital media content. Such a device can comprise a mobile device, computer, server, or another device communicatively coupled to the network equipment. 
     According to an embodiment, the M.L. component  112  can concurrently sample (e.g., in a time domain) past original digital media content and past digital media content. In this regard, the M.L. component  112  can, based on a first past result of concurrently sampling (e.g., in the time domain) the past original digital media content and the past digital media content, determine a second past result. In this regard, the second past result can comprise the frequency domain associated with the first past result. 
     According to an embodiment, the M.L. component  112  can further generate the counterfeit digital media content model based on a result of a comparison between an attribute associated with the past original digital media content and an attribute associated with the past digital media content. In this regard, the determination (e.g., by the counterfeit determination component  110 ) of whether the digital media content comprises the counterfeit or is potentially counterfeit or pirated can be further based on a result of a comparison between an attribute associated with the original digital media content and an attribute associated with the digital media content. It is noted that attributes herein can comprise one or more implicit attributes and/or one or more explicit attributes. For example, implicit attributes can comprise compression, audio or video format, quality, resolution, compression, file size, protocol utilized, start time, end time, or other suitable implicit attributes. Explicit attributes can comprise, for instance, event start time, event end time, duration, ads used or ad lengths, or other suitable explicit attributes. In this regard, the counterfeit determination component  110  can determine whether a threshold similarity exists between attributes of digital media content herein (e.g., between original digital media content and potentially pirated or counterfeit digital media content). In response to such a threshold similarity being determined to exist (e.g., by the M.L. component  112 ), the counterfeit determination component  110  can then determine that the digital media content comprises the counterfeit or is at least potentially counterfeit or pirated. 
     Various embodiments herein can employ artificial-intelligence or machine learning systems and techniques to facilitate learning user behavior, context-based scenarios, preferences, etc. in order to facilitate taking automated action with high degrees of confidence. Utility-based analysis can be utilized to factor benefit of taking an action against cost of taking an incorrect action. Probabilistic or statistical-based analyses can be employed in connection with the foregoing and/or the following. 
     It is noted that systems and/or associated controllers, servers, or machine learning components herein can comprise artificial intelligence component(s) which can employ an artificial intelligence (A.I.) model and/or M.L. or an M.L. model that can learn to perform the above or below described functions (e.g., via training using historical training data and/or feedback data). 
     In some embodiments, M.L. component  112  can comprise an A.I. and/or M.L. model that can be trained (e.g., via supervised and/or unsupervised techniques) to perform the above or below-described functions using historical training data comprising various context conditions that correspond to various augmented network optimization operations. In this example, such an A.I. and/or M.L. model can further learn (e.g., via supervised and/or unsupervised techniques) to perform the above or below-described functions using training data comprising feedback data, where such feedback data can be collected and/or stored (e.g., in memory) by the M.L. component  112 . In this example, such feedback data can comprise the various instructions described above/below that can be input, for instance, to a system herein, over time in response to observed/stored context-based information. 
     A.I./M.L. components herein can initiate an operation(s) associated with a based on a defined level of confidence determined using information (e.g., feedback data). For example, based on learning to perform such functions described above using feedback data, performance information, and/or past performance information herein, an M.L. component  112  herein can initiate an operation associated with determining various thresholds herein (e.g., a QoS threshold, a privacy threshold, a hardware threshold, or another suitable threshold). 
     In an embodiment, the M.L. component  112  can perform a utility-based analysis that factors cost of initiating the above-described operations versus benefit. In this embodiment, the M.L. component  112  can use one or more additional context conditions to determine various thresholds herein. 
     To facilitate the above-described functions, a M.L. component  112  herein can perform classifications, correlations, inferences, and/or expressions associated with principles of artificial intelligence. For instance, the M.L. component  112  can employ an automatic classification system and/or an automatic classification. In one example, the M.L. component  112  can employ a probabilistic and/or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to learn and/or generate inferences. The M.L. component  112  can employ any suitable machine-learning based techniques, statistical-based techniques and/or probabilistic-based techniques. For example, the M.L. component  112  can employ expert systems, fuzzy logic, support vector machines (SVMs), Hidden Markov Models (HMMs), greedy search algorithms, rule-based systems, Bayesian models (e.g., Bayesian networks), neural networks, other non-linear training techniques, data fusion, utility-based analytical systems, systems employing Bayesian models, and/or the like. In another example, the M.L. component  112  can perform a set of machine-learning computations. For instance, the M.L. component  112  can perform a set of clustering machine learning computations, a set of logistic regression machine learning computations, a set of decision tree machine learning computations, a set of random forest machine learning computations, a set of regression tree machine learning computations, a set of least square machine learning computations, a set of instance-based machine learning computations, a set of regression machine learning computations, a set of support vector regression machine learning computations, a set of k-means machine learning computations, a set of spectral clustering machine learning computations, a set of rule learning machine learning computations, a set of Bayesian machine learning computations, a set of deep Boltzmann machine computations, a set of deep belief network computations, and/or a set of different machine learning computations. 
     Turning now to  FIG.  2   , there is illustrated an example, non-limiting system  202  in accordance with one or more embodiments herein. System  202  can comprise a computerized tool, which can be configured to perform various operations relating to multimedia piracy detection. The system  202  can be similar to system  102 , and can comprise one or more of a variety of components, such as memory  104 , processor  106 , bus  108 , counterfeit determination component  110 , M.L. component  112 , flag component  114 , and/or alert component  116 . The system  202  can additionally comprise a filter component  204 . 
     In various embodiments, one or more of the memory  104 , processor  106 , bus  108 , counterfeit determination component  110 , M.L. component  112 , flag component  114 , alert component  116 , and/or filter component  204  can be communicatively or operably coupled (e.g., over a bus or wireless network) to one another to perform one or more functions of the system  202 . 
     According to an embodiment, the M.L. component  112  can further generate the counterfeit digital media content model based on a result of filtering, by the filter component  204 , the first past result using a first signal filter and the second past result using a second signal filter. According to an embodiment, the filter component  204  can comprise a signal filter. In this regard, such filtering can comprise using such a signal filter to remove data points representative of outliers or noise. For example, a signal representative of IP packet flow can be filtered using such a signal filter herein. It is also noted that utilizing a Fourier Transformation, a Laplace Transformation, and/or another suitable transformation to isolate content transmitted over a transport network, which can have a filtering effect in that greater insight into types of content transmitted over the transport network can be obtained. Additionally, such digital signal filter can be configured to filter out signals that may have been added to an original video or audio file or transmission in an attempt to make the content appear different from the original digital media content. 
     Turning now to  FIG.  3   , there is illustrated an example, non-limiting system  302  in accordance with one or more embodiments herein. System  302  can comprise a computerized tool, which can be configured to perform various operations relating to multimedia piracy detection. The system  302  can be similar to system  202 , and can comprise one or more of a variety of components, such as memory  104 , processor  106 , bus  108 , counterfeit determination component  110 , M.L. component  112 , flag component  114 , alert component  116 , and/or filter component  204 . The system  302  can additionally comprise a communication component  304 . 
     In various embodiments, one or more of the memory  104 , processor  106 , bus  108 , counterfeit determination component  110 , M.L. component  112 , flag component  114 , alert component  116 , filter component  204 , and/or communication component  304  can be communicatively or operably coupled (e.g., over a bus or wireless network) to one another to perform one or more functions of the system  302 . 
     According to an embodiment, the communication component  304  can, in response to the determination (e.g., by the counterfeit determination component  110  and/or M.L. component  112 ) that the digital media content threshold satisfies the defined counterfeit criterion, suspend network connectivity between a device determined to be associated with the digital media content and a network employed by the device to transmit content comprising the digital media content. In this regard, transmission of the counterfeit digital media content can be halted, thus preventing its future dissemination. 
     In additional embodiments, the communication component  304  can be utilized to retrieve content from a transport network herein. For example, the communication component  304  can intercept digital media content transmitted (e.g., by a fraudster) over said transport network (e.g., a fiber-based network, a cable-based network, a wireless radio access network, or another suitable network or combination of networks). For example, the communication component  304  can access a digital transport network (e.g., access IP packets transmitted over the digital content network) and analyze the network traffic for various digital media content herein. Such digital media content can thereby be analyzed by a system herein in order to determine whether such content comprises or potentially comprises counterfeit (e.g., pirated, illicit, unauthorized) digital media content. 
     It is noted that the communication component  304  can comprise the hardware required to implement a variety of communication protocols (e.g., infrared (“IR”), shortwave transmission, near-field communication (“NFC”), Bluetooth, Wi-Fi, long-term evolution (“LTE”), 3G, 4G, 5G, 6G, global system for mobile communications (“GSM”), code-division multiple access (“CDMA”), satellite, visual cues, radio waves, etc.) 
     Turning now to  FIG.  4   , there is illustrated an example, non-limiting system  402  in accordance with one or more embodiments herein. System  402  can comprise a computerized tool, which can be configured to perform various operations relating to multimedia piracy detection. The system  402  can be similar to system  302 , and can comprise one or more of a variety of components, such as memory  104 , processor  106 , bus  108 , counterfeit determination component  110 , M.L. component  112 , flag component  114 , alert component  116 , filter component  204 , and/or communication component  304 . The system  402  can additionally comprise a throttle component  404 . 
     In various embodiments, one or more of the memory  104 , processor  106 , bus  108 , counterfeit determination component  110 , M.L. component  112 , flag component  114 , alert component  116 , filter component  204 , communication component  304 , and/or throttle component  404  can be communicatively or operably coupled (e.g., over a bus or wireless network) to one another to perform one or more functions of the system  402 . 
     According to an embodiment, the throttle component  404  can, in response to the determination (e.g., by the counterfeit determination component  110 ) that the digital media content threshold satisfies the defined counterfeit criterion, bandwidth throttle a connection between a device determined to be associated with the digital media content and a network (e.g., a transport network herein) via which the device is determined to transmit content comprising the digital media content. In this regard, bandwidth throttling the connection can comprise limiting onward communication speed between the device and the network to less than a communication speed determined to be sufficient for uninterrupted transmission of the digital media content via the network. If, for example, the throttle component  404  determines that the counterfeit digital media content requires 10 megabits per second (Mbps) to stream (e.g., without buffering), the throttle component can bandwidth throttle the connection between the device determined to be associated with the digital media content and the network 5 Mbps, thus limiting the devices’ ability to effectively stream the counterfeit digital media content. It is noted that some streaming configurations can be responsive to changes in available bandwidth. In this regard, a fraudster entity could attempt to modify the streamed resolution of the counterfeit digital media content in an attempt to circumvent the bandwidth throttling. In this regard, the throttle component  404  continuously update the bandwidth throttling to account for changes in the stream of the counterfeit digital media content. For example, if a fraudster entity modifies the stream of the counterfeit digital media content from 4K to 720P, the throttle component  404  can further bandwidth throttle the connection in order to prevent transmission at 720P resolution. 
     Turning now to  FIG.  5   , there is illustrated an example, non-limiting system  502  in accordance with one or more embodiments herein. System  502  can comprise a computerized tool, which can be configured to perform various operations relating to multimedia piracy detection. The system  502  can be similar to system  402 , and can comprise one or more of a variety of components, such as memory  104 , processor  106 , bus  108 , counterfeit determination component  110 , M.L. component  112 , flag component  114 , alert component  116 , filter component  204 , communication component  304 , and/or throttle component  404 . The system  502  can additionally comprise a playback component  504 . 
     In various embodiments, one or more of the memory  104 , processor  106 , bus  108 , counterfeit determination component  110 , M.L. component  112 , flag component  114 , alert component  116 , filter component  204 , communication component  304 , throttle component  404 , and/or playback component  504  can be communicatively or operably coupled (e.g., over a bus or wireless network) to one another to perform one or more functions of the system  502 . 
     According to an embodiment, in response to the determination (e.g., by the counterfeit determination component  110 ) that the digital media content threshold satisfies the defined counterfeit criterion, the playback component  504  can facilitate (e.g., non-broadcasted) playback of the potentially counterfeit digital media content. In this regard, such playback can be local to the system  502  (e.g., not publicly accessible). Further in this regard, the counterfeit determination component  110  and/or M.L. component  112  can compare the potentially counterfeit digital media content (e.g., played via the playback component  504 ) to the original digital media content, and in response to a determination (e.g., by the counterfeit determination component  110  and/or M.L. component  112 ) that the counterfeit or potentially counterfeit digital media content and original digital media content threshold satisfy a similarity criterion, the counterfeit determination component  110  and/or M.L. component  112  can determine that the counterfeit or potentially counterfeit digital media content comprises counterfeit digital media content. 
     With reference to  FIG.  6   , there is illustrated a block diagram  600  of digital media distribution in accordance with one or more embodiments described herein. For example, consumers (e.g., digital media content consumer entities with subscription or purchase  604 ) often subscribe to one or more media providers services (e.g., Netflix, Hulu, Amazon Prime Video, Disney+, Discovery Plus, Paramount Plus, HBO Max, YouTube TV, fuboTV, DirecTV Stream, or other digital media provider entities (e.g., digital media content producer or copyright owner entity  602 ). It is noted that digital media content and associated distribution comprise explicit attributes  608  and/or implicit attributes  610 , and some of such attributes can be determined and/or analyzed during rendering of associated content. For example, an internet service provider (ISP) can observe, transport network attributes  606  (e.g., over a transport network), such as a higher multicasting internet protocol (IP) traffic rate during the release of a new movie. Other attributes are specific to the digital content formats, digital storage formats (e.g., mpeg-4, with respective sets of files, directories, and compression algorithms). It is noted that the time to stream digital media content from the digital media content producer or copyright owner entity to the digital media content consumer entity with subscription or purchase can be nearly instantaneous (e.g., data transmitted at the speed of light over a fiber optic connection). 
       FIG.  7    illustrates a block diagram  700  of piracy medium distribution in accordance with one or more embodiments described herein. According to an example, a fraudster entity (e.g., piracy digital media content producer entity  704 ), being a legitimate subscriber of a media content provider’s services, can obtain media content (for one or more subscriber entity’s viewing only) from a valid source (e.g., digital media content producer or copyright owner entity  702 ) via the internet (e.g., transport network  706 ). The fraudster entity then can capture the contents using one or more of a variety of methods, then resell the contents illegally. One such method can comprise camripping a just-released movie, then streaming it out to the fraudster entity’s subscribers (e.g., via internet.) It is noted that rebroadcasting digital media content can require time to execute, especially if alternations are made to the digital media content (e.g., changes in resolution, format, addition of watermarks, or other alterations or modifications). Thus, while digital media content can be transmitted at T1, counterfeit digital media content would be transmitted at T2, later in time than T1, due the inherent time required to convert, camrip, or perform other alterations of original digital media content in the attempt to evade detection. In this regard, though original media content can be transmitted from a digital media content producer or copyright owner entity  702  to a piracy digital media content producer entity  704  or a legitimate digital media subscriber entity, a counterfeit media content consumer entity  708  would not receive the counterfeit digital media content at the same time that the piracy digital media content producer entity  704  or the legitimate digital media subscriber entity received the original digital media content. Thus, the difference in time of streaming can be representative of an attribute that differentiates a broadcast or stream of an original digital media content from a counterfeit or potentially counterfeit digital media broadcast or stream. 
     Turning now to  FIG.  8   , there is illustrated a block diagram  800  of multi-stage machine training phases in accordance with one or more embodiments described herein. It is noted that each phase herein can be conducted simultaneously. In a first phase, various samples of original media content can be gathered and/or generated (e.g., by a system  102  and/or associated component). In this regard, original digital media content  802 , reformatted digital media content  804 , camripped digital media content  806 , segmented digital media content  808 , and/or IP packets from the transport network at  810  can each be retrieved or generated by a system herein (e.g., using a communication component  304 ). It is noted that each of the regard original digital media content  802 , reformatted digital media content  804 , camripped digital media content  806 , segmented digital media content  808 , and/or IP packets can be associated with respective set(s) of operations. In this regard, a system herein (e.g., system  102 , system  202 , system  302 , system  402 , or system  502 ) can perform simultaneous sampling (e.g., via the M.L. component  112 ) of internet distributable digital media content in different stages, in which each phase is a time series, Si(N, dt), in which i represents the media transferring stage, N represents the quantity of samples, and dt represents the sampling interval. According to an example, original digital media content can comprise a movie in an mpeg-4 format or a live broadcast. A system herein can convert the original movie format into, for instance, one or more of .webm, .mkv, .flv, .vob, .ogv, .ogg, .drc, .gif, .jpg, .gifv, .mng, .avi, .MTS, .M2TS, .TS, .mov, .qt, .wmv, .yuv, .rm, .rmvb, .viv, .asf, .amv, .mpg, .mpg, .svi, .3pg, .3g2, .mxf, .roq, .nsv, .f4v, .frp, .f4a, .f4b, or other suitable formats. Likewise, audio content can be converted across various audio file formats, such as .3gp, .aa, .aac, .aax, .act, .aiff, .alac, .amr, .ape, .au, .awb, .dss, .dvf, .flac, .gsm, .iklax, .ivs, .m4a, m4b, .m4p, m4p, .mmf, .mp3, .mpc, .msv, nmf, .ogg, .oga, .mogg, .opus, .ra, .rm, .raw, .rf64, .sln, .tta, .voc, .vox, .wav, .wma, .wv, .webm, .8svx, .cda, or other suitable formats. Similarly, video resolution or audio quality can be converted. (e.g., video resolution can be changed from 4K to 1080P or 720P and/or to/from other suitable resolutions). A system herein can further generate a camrip of original digital media content (e.g., a movie) at  806 . Likewise, a system herein can segment original, reformatted, camripped, or other original digital media content into smaller sections or clips of said content for use in the generation of data insights herein. It is noted that the transport network  810  also carries other traffic (e.g., other than the original digital media content and/or counterfeit digital media content. For example, under normal circumstances, the transport network  810  can also transmit other streaming content, IP phone calls, web browsing, online gaming, internet radio streaming, and other content along with the above-noted original digital media content. Therefore, a Fourier Transformation function, a Laplace Transformation function, and/or another suitable transformation function can be utilized to isolate content and/or filter noise to differentiate the content transmitted over the transport network  810 . The foregoing can also be utilized to determine patterns for user entities and/or determine a suspicion score representative of a level of suspicion that a user profile associated with a user entity is engaged in digital media piracy. If, for instance, user profile achieves a level of suspicion that exceeds a defined suspicion threshold, then a corresponding action can be taken (e.g., flagging, alarm generation, throttling, suspension, or other suitable actions). The foregoing operations can be provisioned dynamically, for instance, based on the stage, implicit or explicit attributes of respective media, and/or transport network attributes. Additionally, a rule engine can supply (e.g., using the M.L. component  112 ) a set of machine-learning rules to optimize the sampling process and its required computing resources usage. According to a nonlimiting example, a system herein can utilize use N=2 10 , t=1 second for an mpeg-4 movie, with 20 directories and files, using the HTTP protocol, for a 156 minute duration movie. 
     A second phase can comprise converting samples to different domains (e.g., from a time domain to a frequency domain) (e.g., using a Fourier Transform). During this second phase, a filter (e.g., filter component  204 ) can be utilized to remove data points representative outliners or noise. For example, such a filter (e.g., a signal filter) can be configured to filter out signals that may have been added to an original video or audio file or transmission in an attempt to make the content appear different from the original. 
     In a third phase, a system herein can consolidate all samples in both domains (e.g., time domain and frequency domain) into their corresponding data insights. Data insights can be utilized by a system herein, for instance, to determine whether digital media content comprises counterfeit digital media content or potentially counterfeit digital media content. For example, such insights can comprise data representative of comparisons of implicit and/or explicit attributes herein. In this regard, such insights can comprise data representative of a comparison between an attribute associated with the original digital media content and an attribute associated with the digital media content. 
     With reference to  FIG.  9   , there is illustrated a block diagram  900  of multi-stage sampling during real-time rendering phases in accordance with one or more embodiments described herein. According to an embodiment, block diagram  900  can comprise a fourth phase, fifth phase, and sixth phase. During phase four, samples similar to those taken or generated in phase two can be taken (e.g., in real-time or near real-time) during the active rendering of the original media contents, for example, during the time of an initial release a new movie, or a live broadcasting of a sport event. In a fifth phase, real-time samples can be transformed (e.g., using a Fourier Transformation) into a different domain (e.g., into a frequency domain from a time domain). Next, real-time rendering samples can be utilized in a sixth phase, which can comprise utilizing machine learning herein (e.g., using the M.L. component  112 ) to conduct pattern-matching with data form the aforementioned insights. In this regard, an M.L. component  112  can determine probabilities that certain digital media content is “HOT” (potentially involved with a fraudulent event or person). It is noted that historical HOT data insights can be utilized by the M.L. component  112  herein. Further, counterfeit digital media content models herein can be updated (e.g., by the M.L. component  112 ) based on acquired digital media content and respective insights. 
     Turning now to  FIG.  10   , there is illustrated a flow chart of a process  1000  relating to multimedia piracy detection in accordance with one or more embodiments described herein. At  1002  a counterfeit digital media content model can be initialized (e.g., using an M.L. component  112 ). At  1004 , digital media content can be retrieved (e.g., using communication component  304 ). For example, such digital media content can be retrieved from web streams, file sharing sites, or otherwise obtained. At  1006 , the retrieved content can be reformatted (e.g., using an M.L. component  112 ). For example, such reformatting can comprise file format changes, changes in resolution, changes in quality, application or removal of watermarks, or other suitable reformatting. At  1008 , the retrieved content can be camripped (e.g., using an M.L. component  112 ). It is noted that such camripping can comprise virtual camripping configured to be similar to the effect of physical camripping. At  1010 , the retrieved content can be segmented (e.g., using an M.L. component  112 ). At  1012 , one or more of the reformatted content, camripped content, or segmented content can be filtered (e.g., using a signal filter such as a filter component  204 ). At  1014 , data insights in a time domain can be determined (e.g., using an M.L. component  112 ). At  1016 , a Fourier Transform can be performed (e.g., using an M.L. component  112 ) on a time domain, which can enable data insights in a frequency domain to be determined (e.g., using an M.L. component  112 ) at  1020  based on, for instance, attributes associated with the digital media content. At  1017 , machine learning can be utilized (e.g., using an M.L. component  112 ) using the time domain data insights from  1014 , the frequency domain data insights at  1020 , and/or historical HOT data insights from  1018  in order to determine at  1022  whether digital media content retrieved at  1004  comprises counterfeit digital media content. If the content is determined (e.g., using the counterfeit determination component  110  and/or M.L. component  112 ) not to comprise counterfeit digital media content, the process can return to  1004  or end. If the content is determined to comprise counterfeit digital media content, the process can proceed to  1024  at which an alarm can be generated (e.g., using the alert component  116 ). At  1026 , if any further actions are to be taken by a system herein (e.g., according to a defined setting or as determined by the M.L. component  112 ), the process can proceed to  1028 . Otherwise, the process can return to  1004  or end. At  1028 , if a report is to be generated (e.g., according to a defined setting or as determined by the M.L. component  112 ), signal representative of the report can be generated at  1030  (e.g., using the communication component  304 ). At  1032 , if bandwidth throttling is to be conducted (e.g., according to a defined setting or as determined by the M.L. component  112 ), said bandwidth throttling can be performed at  1034  (e.g., using the throttle component  404 ). At  1036 , if network connectivity is to be suspended (e.g., according to a defined setting or as determined by the M.L. component  112 ), said suspension can be performed at  1038  (e.g., using the throttle component  404 , communication component  304 , or another suitable component). 
       FIG.  11    illustrates a block flow diagram for a process  1100  associated with multimedia piracy detection in accordance with one or more embodiments described herein. At  1102 , the process  1100  can comprise determining (e.g., using a counterfeit determination component  110 ), based on a time domain and a frequency domain associated with original digital media content and using a counterfeit digital media content model, whether digital media content comprises a counterfeit of an authorized release of the original digital media content, wherein the counterfeit digital media content model has been generated (e.g., using an M.L. component  112 ) based on machine learning applied to time domains and frequency domains of past original digital media content other than the original digital media content and to past digital media content other than the digital media content, and wherein the past digital media content comprises counterfeit digital media content. At  1104 , the process  1100  can comprise based on a determination that the digital media content comprises the counterfeit, flagging (e.g., using the flag component  114 ) the digital media content as being counterfeit. 
       FIG.  12    illustrates a block flow diagram for a process  1200  associated with multimedia piracy detection in accordance with one or more embodiments described herein. At  1202 , the process  1200  can comprise determining (e.g., using a counterfeit determination component  110 ), based on a frequency domain associated with original digital media content and using a counterfeit digital media content model, whether digital media content comprises a counterfeit of an approved release of the original digital media content, wherein the counterfeit digital media content model has been generated (e.g., using an M.L. component  112 ) based on machine learning applied to time domains and frequency domains of past original digital media content other than the original digital media content and to past digital media content other than the digital media content, and wherein the past digital media content comprises counterfeit digital media content. At  1204 , the process  1200  can comprise based on a determination that the digital media content threshold satisfies a defined counterfeit criterion, determining that the digital media content comprises potentially counterfeit digital media content (e.g., using the counterfeit determination component  110  and/or flag component  114 ). 
       FIG.  13    illustrates a block flow diagram for a process  1300  associated with multimedia piracy detection in accordance with one or more embodiments described herein. At  1302 , the process  1300  can comprise determining (e.g., using a counterfeit determination component  110 ), by network equipment comprising a processor, based on a frequency domain associated with original digital media content and using a counterfeit digital media content model, whether digital media content comprises a counterfeit of the original digital media content, wherein the counterfeit digital media content model has been generated (e.g., using the M.L. component  112 ) based on machine learning applied to time domains and frequency domains of past original digital media content other than the original digital media content and to past digital media content other than the digital media content, and wherein the past digital media content comprises counterfeit digital media content. At  1304 , the process  1300  can comprise based on a determination that the digital media content threshold satisfies a defined counterfeit criterion, determining (e.g., using the counterfeit determination component  110  and/or flag component  114 ), by the network equipment, that the digital media content comprises potentially counterfeit digital media content. At  1306 , the process  1300  can comprise in response to the determining that the digital media content comprises potentially counterfeit digital media content, sending (e.g., using an alert component  116  and/or communication component  304 ), by the network equipment to a device communicatively coupled to the network equipment, an alert signal representative of the determination that the digital media content comprises potentially counterfeit digital media content. 
     In order to provide additional context for various embodiments described herein,  FIG.  14    and the following discussion are intended to provide a brief, general description of a suitable computing environment  1400  in which the various embodiments of the embodiment described herein can be implemented. While the embodiments have been described above in the general context of computer-executable instructions that can run on one or more computers, those skilled in the art will recognize that the embodiments can be also implemented in combination with other program modules and/or as a combination of hardware and software. 
     Generally, program modules include routines, programs, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the various methods can be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, distributed computing systems, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices. 
     The illustrated embodiments of the embodiments herein can be also practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices. 
     Computing devices typically include a variety of media, which can include computer-readable storage media, machine-readable storage media, and/or communications media, which two terms are used herein differently from one another as follows. Computer-readable storage media or machine-readable storage media can be any available storage media that can be accessed by the computer and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer-readable storage media or machine-readable storage media can be implemented in connection with any method or technology for storage of information such as computer-readable or machine-readable instructions, program modules, structured data or unstructured data. 
     Computer-readable storage media can include, but are not limited to, random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD-ROM), digital versatile disk (DVD), Blu-ray disc (BD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, solid state drives or other solid state storage devices, or other tangible and/or non-transitory media which can be used to store desired information. In this regard, the terms “tangible” or “non-transitory” herein as applied to storage, memory, or computer-readable media, are to be understood to exclude only propagating transitory signals per se as modifiers and do not relinquish rights to all standard storage, memory or computer-readable media that are not only propagating transitory signals per se. 
     Computer-readable storage media can be accessed by one or more local or remote computing devices, e.g., via access requests, queries, or other data retrieval protocols, for a variety of operations with respect to the information stored by the medium. 
     Communications media typically embody computer-readable instructions, data structures, program modules or other structured or unstructured data in a data signal such as a modulated data signal, e.g., a carrier wave or other transport mechanism, and includes any information delivery or transport media. The term “modulated data signal” or signals refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in one or more signals. By way of example, and not limitation, communication media include wired media, such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. 
     With reference again to  FIG.  14   , the example environment  1400  for implementing various embodiments of the aspects described herein includes a computer  1402 , the computer  1402  including a processing unit  1404 , a system memory  1406  and a system bus  1408 . The system bus  1408  couples system components including, but not limited to, the system memory  1406  to the processing unit  1404 . The processing unit  1404  can be any of various commercially available processors. Dual microprocessors and other multi-processor architectures can also be employed as the processing unit  1404 . 
     The system bus  1408  can be any of several types of bus structure that can further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory  1406  includes ROM  1410  and RAM  1412 . A basic input/output system (BIOS) can be stored in a non-volatile memory such as ROM, erasable programmable read only memory (EPROM), EEPROM, which BIOS contains the basic routines that help to transfer information between elements within the computer  1402 , such as during startup. The RAM  1412  can also include a high-speed RAM such as static RAM for caching data. 
     The computer  1402  further includes an internal hard disk drive (HDD)  1414  (e.g., EIDE, SATA), one or more external storage devices  1416  (e.g., a magnetic floppy disk drive (FDD)  1416 , a memory stick or flash drive reader, a memory card reader, etc.) and an optical disk drive  1420  (e.g., which can read or write from a CD-ROM disc, a DVD, a BD, etc.). While the internal HDD  1414  is illustrated as located within the computer  1402 , the internal HDD  1414  can also be configured for external use in a suitable chassis (not shown). Additionally, while not shown in environment  1400 , a solid-state drive (SSD) could be used in addition to, or in place of, an HDD  1414 . The HDD  1414 , external storage device(s)  1416  and optical disk drive  1420  can be connected to the system bus  1408  by an HDD interface  1424 , an external storage interface  1426  and an optical drive interface  1428 , respectively. The interface  1424  for external drive implementations can include at least one or both of Universal Serial Bus (USB) and Institute of Electrical and Electronics Engineers (IEEE)  1394  interface technologies. Other external drive connection technologies are within contemplation of the embodiments described herein. 
     The drives and their associated computer-readable storage media provide nonvolatile storage of data, data structures, computer-executable instructions, and so forth. For the computer  1402 , the drives and storage media accommodate the storage of any data in a suitable digital format. Although the description of computer-readable storage media above refers to respective types of storage devices, it should be appreciated by those skilled in the art that other types of storage media which are readable by a computer, whether presently existing or developed in the future, could also be used in the example operating environment, and further, that any such storage media can contain computer-executable instructions for performing the methods described herein. 
     A number of program modules can be stored in the drives and RAM  1412 , including an operating system  1430 , one or more application programs  1432 , other program modules  1434  and program data  1436 . All or portions of the operating system, applications, modules, and/or data can also be cached in the RAM  1412 . The systems and methods described herein can be implemented utilizing various commercially available operating systems or combinations of operating systems. 
     Computer  1402  can optionally comprise emulation technologies. For example, a hypervisor (not shown) or other intermediary can emulate a hardware environment for operating system  1430 , and the emulated hardware can optionally be different from the hardware illustrated in  FIG.  14   . In such an embodiment, operating system  1430  can comprise one virtual machine (VM) of multiple VMs hosted at computer  1402 . Furthermore, operating system  1430  can provide runtime environments, such as the Java runtime environment or the .NET framework, for applications  1432 . Runtime environments are consistent execution environments that allow applications  1432  to run on any operating system that includes the runtime environment. Similarly, operating system  1430  can support containers, and applications  1432  can be in the form of containers, which are lightweight, standalone, executable packages of software that include, e.g., code, runtime, system tools, system libraries and settings for an application. 
     Further, computer  1402  can be enable with a security module, such as a trusted processing module (TPM). For instance, with a TPM, boot components hash next in time boot components, and wait for a match of results to secured values, before loading a next boot component. This process can take place at any layer in the code execution stack of computer  1402 , e.g., applied at the application execution level or at the operating system (OS) kernel level, thereby enabling security at any level of code execution. 
     A user can enter commands and information into the computer  1402  through one or more wired/wireless input devices, e.g., a keyboard  1438 , a touch screen  1440 , and a pointing device, such as a mouse  1442 . Other input devices (not shown) can include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control, or other remote control, a joystick, a virtual reality controller and/or virtual reality headset, a game pad, a stylus pen, an image input device, e.g., camera(s), a gesture sensor input device, a vision movement sensor input device, an emotion or facial detection device, a biometric input device, e.g., fingerprint or iris scanner, or the like. These and other input devices are often connected to the processing unit  1404  through an input device interface  1444  that can be coupled to the system bus  1408 , but can be connected by other interfaces, such as a parallel port, an IEEE  1394  serial port, a game port, a USB port, an IR interface, a BLUETOOTH® interface, etc. 
     A monitor  1446  or other type of display device can be also connected to the system bus  1408  via an interface, such as a video adapter  1448 . In addition to the monitor  1446 , a computer typically includes other peripheral output devices (not shown), such as speakers, printers, etc. 
     The computer  1402  can operate in a networked environment using logical connections via wired and/or wireless communications to one or more remote computers, such as a remote computer(s)  1450 . The remote computer(s)  1450  can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer  1402 , although, for purposes of brevity, only a memory/storage device  1452  is illustrated. The logical connections depicted include wired/wireless connectivity to a local area network (LAN)  1454  and/or larger networks, e.g., a wide area network (WAN)  1456 . Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to a global communications network, e.g., the Internet. 
     When used in a LAN networking environment, the computer  1402  can be connected to the local network  1454  through a wired and/or wireless communication network interface or adapter  1458 . The adapter  1458  can facilitate wired or wireless communication to the LAN  1454 , which can also include a wireless access point (AP) disposed thereon for communicating with the adapter  1458  in a wireless mode. 
     When used in a WAN networking environment, the computer  1402  can include a modem  1460  or can be connected to a communications server on the WAN  1456  via other means for establishing communications over the WAN  1456 , such as by way of the Internet. The modem  1460 , which can be internal or external and a wired or wireless device, can be connected to the system bus  1408  via the input device interface  1444 . In a networked environment, program modules depicted relative to the computer  1402  or portions thereof, can be stored in the remote memory/storage device  1452 . It will be appreciated that the network connections shown are example and other means of establishing a communications link between the computers can be used. 
     When used in either a LAN or WAN networking environment, the computer  1402  can access cloud storage systems or other network-based storage systems in addition to, or in place of, external storage devices  1416  as described above. Generally, a connection between the computer  1402  and a cloud storage system can be established over a LAN  1454  or WAN  1456  e.g., by the adapter  1458  or modem  1460 , respectively. Upon connecting the computer  1402  to an associated cloud storage system, the external storage interface  1426  can, with the aid of the adapter  1458  and/or modem  1460 , manage storage provided by the cloud storage system as it would other types of external storage. For instance, the external storage interface  1426  can be configured to provide access to cloud storage sources as if those sources were physically connected to the computer  1402 . 
     The computer  1402  can be operable to communicate with any wireless devices or entities operatively disposed in wireless communication, e.g., a printer, scanner, desktop and/or portable computer, portable data assistant, communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, store shelf, etc.), and telephone. This can include Wireless Fidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, the communication can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices. 
     Referring now to  FIG.  15   , there is illustrated a schematic block diagram of a computing environment  1500  in accordance with this specification. The system  1500  includes one or more client(s)  1502 , (e.g., computers, smart phones, tablets, cameras, PDA’s). The client(s)  1502  can be hardware and/or software (e.g., threads, processes, computing devices). The client(s)  1502  can house cookie(s) and/or associated contextual information by employing the specification, for example. 
     The system  1500  also includes one or more server(s)  1504 . The server(s)  1504  can also be hardware or hardware in combination with software (e.g., threads, processes, computing devices). The servers  1504  can house threads to perform transformations of media items by employing aspects of this disclosure, for example. One possible communication between a client  1502  and a server  1504  can be in the form of a data packet adapted to be transmitted between two or more computer processes wherein data packets may include coded analyzed headspaces and/or input. The data packet can include a cookie and/or associated contextual information, for example. The system  1500  includes a communication framework  1506  (e.g., a global communication network such as the Internet) that can be employed to facilitate communications between the client(s)  1502  and the server(s)  1504 . 
     Communications can be facilitated via a wired (including optical fiber) and/or wireless technology. The client(s)  1502  are operatively connected to one or more client data store(s)  1508  that can be employed to store information local to the client(s)  1502  (e.g., cookie(s) and/or associated contextual information). Similarly, the server(s)  1504  are operatively connected to one or more server data store(s)  1510  that can be employed to store information local to the servers  1504 . 
     In one exemplary implementation, a client  1502  can transfer an encoded file, (e.g., encoded media item), to server  1504 . Server  1504  can store the file, decode the file, or transmit the file to another client  1502 . It is noted that a client  1502  can also transfer uncompressed file to a server  1504  and server  1504  can compress the file and/or transform the file in accordance with this disclosure. Likewise, server  1504  can encode information and transmit the information via communication framework  1506  to one or more clients  1502 . 
     The illustrated aspects of the disclosure may also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices. 
     The above description includes non-limiting examples of the various embodiments. It is, of course, not possible to describe every conceivable combination of components or methods for purposes of describing the disclosed subject matter, and one skilled in the art may recognize that further combinations and permutations of the various embodiments are possible. The disclosed subject matter is intended to embrace all such alterations, modifications, and variations that fall within the spirit and scope of the appended claims. 
     With regard to the various functions performed by the above-described components, devices, circuits, systems, etc., the terms (including a reference to a “means”) used to describe such components are intended to also include, unless otherwise indicated, any structure(s) which performs the specified function of the described component (e.g., a functional equivalent), even if not structurally equivalent to the disclosed structure. In addition, while a particular feature of the disclosed subject matter may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application. 
     The terms “exemplary” and/or “demonstrative” as used herein are intended to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent structures and techniques known to one skilled in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive - in a manner similar to the term “comprising” as an open transition word - without precluding any additional or other elements. 
     The term “or” as used herein is intended to mean an inclusive “or” rather than an exclusive “or.” For example, the phrase “A or B” is intended to include instances of A, B, and both A and B. Additionally, the articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless either otherwise specified or clear from the context to be directed to a singular form. 
     The term “set” as employed herein excludes the empty set, i.e., the set with no elements therein. Thus, a “set” in the subject disclosure includes one or more elements or entities. Likewise, the term “group” as utilized herein refers to a collection of one or more entities. 
     The description of illustrated embodiments of the subject disclosure as provided herein, including what is described in the Abstract, is not intended to be exhaustive or to limit the disclosed embodiments to the precise forms disclosed. While specific embodiments and examples are described herein for illustrative purposes, various modifications are possible that are considered within the scope of such embodiments and examples, as one skilled in the art can recognize. In this regard, while the subject matter has been described herein in connection with various embodiments and corresponding drawings, where applicable, it is to be understood that other similar embodiments can be used or modifications and additions can be made to the described embodiments for performing the same, similar, alternative, or substitute function of the disclosed subject matter without deviating therefrom. Therefore, the disclosed subject matter should not be limited to any single embodiment described herein, but rather should be construed in breadth and scope in accordance with the appended claims below.