Forensic analysis of nonlinear collusion attacks for multimedia fingerprinting

TitleForensic analysis of nonlinear collusion attacks for multimedia fingerprinting
Publication TypeJournal Articles
Year of Publication2005
AuthorsZhao HV, M. Wu, Wang ZJ, Liu KJR
JournalImage Processing, IEEE Transactions on
Pagination646 - 661
Date Published2005/05//
ISBN Number1057-7149
KeywordsAutomated;Product Labeling;Reproducibility of Results;Sensitivity and Specificity;Signal Processing, bounded Gaussian-like fingerprint;detection statistics;digital fingerprinting;forensic analysis;independent Gaussian fingerprint;multimedia content;multimedia fingerprinting;nonlinear collusion attack;perceptual distortion;perceptual quality;preprocessing, Computer-Assisted;, Computer-Assisted;Models, Statistical;Nonlinear Dynamics;Patents as Topic;Pattern Recognition

Digital fingerprinting is a technology for tracing the distribution of multimedia content and protecting them from unauthorized redistribution. Unique identification information is embedded into each distributed copy of multimedia signal and serves as a digital fingerprint. Collusion attack is a cost-effective attack against digital fingerprinting, where colluders combine several copies with the same content but different fingerprints to remove or attenuate the original fingerprints. In this paper, we investigate the average collusion attack and several basic nonlinear collusions on independent Gaussian fingerprints, and study their effectiveness and the impact on the perceptual quality. With unbounded Gaussian fingerprints, perceivable distortion may exist in the fingerprinted copies as well as the copies after the collusion attacks. In order to remove this perceptual distortion, we introduce bounded Gaussian-like fingerprints and study their performance under collusion attacks. We also study several commonly used detection statistics and analyze their performance under collusion attacks. We further propose a preprocessing technique of the extracted fingerprints specifically for collusion scenarios to improve the detection performance.