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.

Abstract-Multimedia security systems involve many users with different objectives and users influence each other's performance. To have a better understanding of multimedia security systems and offer stronger protection of multimedia, behavior forensics formulate the dynamics among users and investigate how they interact with and respond to each other. This paper analyzes the behavior forensics in multimedia fingerprinting and formulates the dynamics among attackers during multi-user collusion. In particular, this paper focuses on how colluders achieve the fair play of collusion and guarantee that all attackers share the same risk (i.e., the probability of being detected). We first analyze how to distribute the risk evenly among colluders when they receive fingerprinted copies of scalable resolutions due to network and device heterogeneity. We show that generating a colluded copy of higher resolution puts more severe constraints on achieving fairness. We then analyze the effectiveness of fair collusion. Our results indicate that the attackers take a larger risk of being captured when the colluded copy has higher resolution, and they have to take this tradeoff into consideration during collusion. Finally, we analyze the collusion resistance of the scalable fingerprinting systems in various scenarios with different system requirements, and evaluate the maximum number of colluders that the fingerprinting systems can withstand.

In digital fingerprinting and multimedia forensic systems, it is possible that multiple adversaries mount attacks collectively and effectively to undermine the forensic system's traitor tracing capability. During this collusion attack, an important issue that the adversaries need to address is the fairness of attack and ensuring that all colluders share the same risk of being caught. This paper studies the dynamics among attackers in enforcing the fairness of collusion and investigates the problem of traitors within traitors, in which some selfish colluders wish to minimize their own risk while still profiting from collusion. We explore the strategies that these selfish colluders can use to further lower their probability of being detected and analyze their performance. We show that by processing their fingerprinted copies before multi-user collusion, the selfish colluders can further reduce their risk at the cost of quality degradation of their fingerprinted copies.

During multi-user collusion attacks against digital fingerprinting, an important issue that colluders have to address is to distribute the risk evenly among all colluders and achieve fairness of the attack. Although they might agree so, some selfish colluders may break their agreement and process their fingerprinted copies before collusion in order to further reduce their own risk. To protect their own interest, other colluders have to detect these selfish colluders and exclude them from multi-user collusion. This paper studies this problem of traitors within traitors. We propose an autonomous selfish colluder detection and identification algorithm, in which colluders help each other detect selfish behavior. We show that the proposed algorithm can correctly identify all selfish colluders without falsely accusing any others, even when a small group of selfish colluders collaborate with each other to change the detection results.