Autonomous Identification of Selfish Colluders in Traitor-within-Traitor Behavior Forensics
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.