Performance of detection statistics under collusion attacks on independent multimedia fingerprints

Digital fingerprinting is a technology for tracing the distribution of multimedia content and protecting them from unauthorized redistribution. Collusion attack is a cost effective attack against digital fingerprinting where several copies with the same content but different fingerprints are combined to remove the original fingerprints. In this paper, we consider average attack and several nonlinear collusion attacks on independent Gaussian based fingerprints, and study the detection performance of several commonly used detection statistics in the literature under collusion attacks. Observing that these detection statistics are not specifically designed for collusion scenarios and do not take into account the characteristics of the newly generated fingerprints under collusion attacks, we propose pre-processing techniques to improve the detection performance of the detection statistics under collusion attacks.