@article {18213,
	title = {Fingerprinting Compressed Multimedia Signals},
	journal = {Information Forensics and Security, IEEE Transactions on},
	volume = {4},
	year = {2009},
	month = {2009/09//},
	pages = {330 - 345},
	abstract = {Digital fingerprinting is a technique to deter unauthorized redistribution of multimedia content by embedding a unique identifying signal in each legally distributed copy. The embedded fingerprint can later be extracted and used to trace the originator of an unauthorized copy. A group of users may collude and attempt to create a version of the content that cannot be traced back to any of them. As multimedia data is commonly stored in compressed form, this paper addresses the problem of fingerprinting compressed signals. Analysis is carried out to show that due to the quantized nature of the host signal and the embedded fingerprint, directly extending traditional fingerprinting techniques for uncompressed signals to the compressed case leads to low collusion resistance. To overcome this problem and improve the collusion resistance, a new technique for fingerprinting compressed signals called Anti-Collusion Dither (ACD) is proposed, whereby a random dither signal is added to the compressed host before embedding so as to make the effective host signal appear more continuous. The proposed technique is shown to reduce the accuracy with which attackers can estimate the host signal, and from an information theoretic perspective, the proposed ACD technique increases the maximum number of users that can be supported by the fingerprinting system under a given attack. Both analytical and experimental studies confirm that the proposed technique increases the probability of identifying a guilty user and can approximately quadruple the collusion resistance compared to conventional Gaussian fingerprinting.},
	keywords = {coding;, coding;copy, collusion;digital, communication;video, compression;digital, compression;video, dither;collusion, domain, fingerprinting;anti-collusion, fingerprinting;multimedia, Gaussian, management;multimedia, protection;data, resistance;compressed, rights},
	isbn = {1556-6013},
	doi = {10.1109/TIFS.2009.2025860},
	author = {Varna,A.L. and He,Shan and Swaminathan,A. and M. Wu}
}
@article {18181,
	title = {Collusion-Resistant Video Fingerprinting for Large User Group},
	journal = {Information Forensics and Security, IEEE Transactions on},
	volume = {2},
	year = {2007},
	month = {2007/12//},
	pages = {697 - 709},
	abstract = {Digital fingerprinting protects multimedia content from illegal redistribution by uniquely marking copies of the content distributed to users. Most existing multimedia fingerprinting schemes consider a user set on the scale of thousands. However, in such real-world applications as video-on-demand distribution, the number of potential users can be as many as 10-100 million. This large user size demands not only strong collusion resistance but also high efficiency in fingerprint construction, and detection, which makes most existing schemes incapable of being applied to these applications. A recently proposed joint coding and embedding fingerprinting framework provides a promising balance between collusion resistance, efficient construction, and detection, but several issues remain unsolved for applications involving a large group of users. In this paper, we explore how to employ the joint coding and embedding framework and develop practical algorithms to fingerprint video in such challenging settings as to accommodate more than ten million users and resist hundreds of users{\textquoteright} collusion. We investigate the proper code structure for large-scale fingerprinting and propose a trimming detection technique that can reduce the decoding computational complexity by more than three orders of magnitude at the cost of less than 0.5\% loss in detection probability under moderate to high watermark-to-noise ratios. Both analytic and experimental results show a high potential of joint coding and embedding to meet the needs of real-world large-scale fingerprinting applications.},
	keywords = {code, coding-embedding, coding;video, communication;video, complexity;decoding;digital, complexity;digital, computational, content;trimming, demand;watermarking;, DETECTION, distribution;computational, fingerprinting, fingerprinting;decoding, fingerprinting;joint, framework;large, group;multimedia, identification;multimedia, on, signatures;fingerprint, structure;collusion-resistant, technique;video-on-demand, user, video},
	isbn = {1556-6013},
	doi = {10.1109/TIFS.2007.908179},
	author = {He,Shan and M. Wu}
}
