By K. J. Ray Liu, Wade Trappe, Jane Z. Wang, Min Wu, Hong Zhao
The recognition of multimedia content material has resulted in the common distribution and intake of electronic multimedia information. end result of the relative ease with which people may possibly now adjust and repackage electronic content material, making sure that media content material is hired via approved clients for its meant function is turning into a topic of eminent significance to either governmental defense and advertisement purposes. electronic fingerprinting is a category of multimedia forensic applied sciences to trace and establish entities fascinated by the unlawful manipulation and unauthorized utilization of multimedia content material, thereby retaining the delicate nature of multimedia info in addition to its advertisement worth after the content material has been brought to a recipient. "Multimedia Fingerprinting Forensics for Traitor Tracing" covers the fundamental points of analysis during this rising know-how, and explains the newest improvement during this box. It describes the framework of multimedia fingerprinting, discusses the demanding situations which may be confronted whilst imposing utilization polices, and investigates the layout of fingerprints that focus on new households of multiuser assaults that could be fastened opposed to media fingerprints. The dialogue supplied within the ebook highlights difficult difficulties in addition to destiny tendencies during this learn box, offering readers with a broader view of the evolution of the younger box of multimedia forensics.
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Additional info for Multimedia Fingerprinting Forensics for Traitor Tracing (Eurasip Signal Processing and Communications)
Code modulation has the potential to provide a compact way to represent fingerprints. As we will see, systematically constructing them with high tracing capability and collusion resistance for multimedia data is a challenging research issue. One important application of fingerprinting is identifying a user who is redistributing marked content y j by detecting the watermark associated with the user to whom y j was sold. By identifying a user, the content owner may be able to more closely monitor future actions of that user, or gather evidence supporting that user’s illicit usage of the content.
This simplification allows us to focus on the unique issues of fingerprint encoding and colluder detection for the anticollusion fingerprinting problem. From a layered viewpoint on data hiding systems , the modules of fingerprint encoding and colluder detection are built on top of the modules of one-bit watermark embedding and detection. The design and optimization of the former and latter modules have some degree of independence in system development. We can replace the simplified model used here with more sophisticated single-watermark detectors considering more realistic noise such as those in [56, 57] to improve the performance in the watermark detection layer and in turn enhance the overall performance.
D. (independent and identically distributed) random variables and they have probability density function (pdf) f (x) and cumuP[Xmin ≤ x] denote the lative distribution function (cdf) F(x). Let Fmin (x) cumulative distribution function of Xmin , and we have Fmin (x) = P min X1 , X2 , . . , Xn ≤ x = 1 − P min X1 , X2 , . . 5) = 1 − P X1 > x, X2 > x, . . , Xn > x . Since X1 , . . , Xn are independently and identically distributed, n Fmin (x) = 1 − n P Xi > x = 1 − 1 − F(x) . 6) i=1 The probability density function of Xmin is fmin (x) = dFmin (x) = n f (x) 1 − F(x) dx n−1 .