Fingerprinting compressed multimedia signals
IEEE Transactions on Information Forensics and Security
Compressed video stream watermarking for peer-to-peer based content distribution network
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
High-rate random-like spherical fingerprinting codes with linear decoding complexity
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Interleaving embedding scheme for ECC-based multimedia fingerprinting
IWDW'10 Proceedings of the 9th international conference on Digital watermarking
A novel collusion attack strategy for digital fingerprinting
IWDW'10 Proceedings of the 9th international conference on Digital watermarking
A collusion attack optimization strategy for digital fingerprinting
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special Issue on Multimedia Security
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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' 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.