%0 Journal Article
%J Image Processing, IEEE Transactions on
%D 2005
%T Anti-collusion forensics of multimedia fingerprinting using orthogonal modulation
%A Wang,Z.J.
%A M. Wu
%A Zhao,H.V.
%A Trappe,W.
%A Liu,K. J.R
%K Automated;Product Labeling;Signal Processing
%K Computer-Assisted;
%K Computer-Assisted;Multimedia;Patents as Topic;Pattern Recognition
%K Gaussian distribution;anticollusion forensic;colluder identification;collusion resistance;digital fingerprinting;false probability;likelihood-based approach;multimedia fingerprinting;orthogonal modulation;spread spectrum embedding;Gaussian distribution;fi
%X Digital fingerprinting is a method for protecting digital data in which fingerprints that are embedded in multimedia are capable of identifying unauthorized use of digital content. A powerful attack that can be employed to reduce this tracing capability is collusion, where several users combine their copies of the same content to attenuate/remove the original fingerprints. In this paper, we study the collusion resistance of a fingerprinting system employing Gaussian distributed fingerprints and orthogonal modulation. We introduce the maximum detector and the thresholding detector for colluder identification. We then analyze the collusion resistance of a system to the averaging collusion attack for the performance criteria represented by the probability of a false negative and the probability of a false positive. Lower and upper bounds for the maximum number of colluders K_{max} are derived. We then show that the detectors are robust to different collusion attacks. We further study different sets of performance criteria, and our results indicate that attacks based on a few dozen independent copies can confound such a fingerprinting system. We also propose a likelihood-based approach to estimate the number of colluders. Finally, we demonstrate the performance for detecting colluders through experiments using real images.
%B Image Processing, IEEE Transactions on
%V 14
%P 804 - 821
%8 2005/06//
%@ 1057-7149
%G eng
%N 6
%R 10.1109/TIP.2005.847284