Forensic analysis of nonlinear collusion attacks for multimedia fingerprinting

TitleForensic analysis of nonlinear collusion attacks for multimedia fingerprinting
Publication TypeJournal Article
Year of Publication2005
AuthorsZhao, H. V., M. Wu, Z. J. Wang, and K. J. R. Liu
JournalIEEE Transactions on Image Processing
Volume14
Pagination646–661
ISSN1057-7149
Abstract

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.

URLhttp://dx.doi.org/10.1109/TIP.2005.846035
DOI10.1109/TIP.2005.846035

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