@conference {18243,
	title = {Modeling and analysis of ordinal ranking in content fingerprinting},
	booktitle = {Information Forensics and Security, 2009. WIFS 2009. First IEEE International Workshop on},
	year = {2009},
	month = {2009/12//},
	pages = {116 - 120},
	abstract = {Content fingerprinting provides a compact representation of multimedia objects for copy detection. This paper analyzes the robustness of the ordinal ranking module frequently used in content fingerprinting by examining the changes in ranks as local variations are introduced in feature values. Closed-form expressions to measure such sensitivity are derived when feature values are jointly Gaussian-distributed. The results show that sensitivity depends on the strength of local variation, the total number of blocks, and the correlations among block-based feature values. Experiments with both synthesized data and image data validate the analysis and provide interesting insights, inspiring an approach to reduce the sensitivity.},
	keywords = {detection;, detection;multimedia, fingerprinting;copy, Gaussian-distributed;content, identification;multimedia, objects;ordinal, processes;fingerprint, ranking;Gaussian, systems;object},
	doi = {10.1109/WIFS.2009.5386472},
	author = {Chuang,Wei-Hong and Varna,A.L. and M. Wu}
}
@conference {18174,
	title = {Colluding Fingerprinted Video using the Gradient Attack},
	booktitle = {Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on},
	volume = {2},
	year = {2007},
	month = {2007/04//},
	pages = {II-161 -II-164 - II-161 -II-164},
	abstract = {Digital fingerprinting is an emerging tool to protect multimedia content from unauthorized distribution by embedding a unique fingerprint into each user{\textquoteright}s copy. Although several fingerprinting schemes have been proposed in related work, disproportional effort has been targeted towards identifying effective collusion attacks on fingerprinting schemes. Recent introduction of the gradient attack has refined the definition of an optimal attack and demonstrated strong effect on direct-sequence, uniformly distributed, and Gaussian spread spectrum fingerprints when applied to synthetic signals. In this paper, we apply the gradient attack on an existing well-engineered video fingerprinting scheme, refine the attack procedure, and demonstrate that the gradient attack is effective on Laplace fingerprints. Finally, we explore an improvement on fingerprint design to thwart the gradient attack. Results suggest that Laplace fingerprint should be avoided. However, we show that a signal mixed of Laplace and Gaussian fingerprints may serve as a design strategy to disable the gradient attack and force pirates into averaging as a form of adversary collusion.},
	keywords = {attack;multimedia, attacks;digital, content, data;video, distribution;fingerprint, effort;gradient, fingerprinted, fingerprinting;disproportional, fingerprints;colluding, fingerprints;Laplace, Gaussian, identification;multimedia, of, processing;, protection;unauthorized, signal, spectrum, spread, systems;security, video;collusion},
	doi = {10.1109/ICASSP.2007.366197},
	author = {He,Shan and Kirovski,D. 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}
}
