Fair collusion attacks on scalable video fingerprinting systems

Digital fingerprinting inserts identification information in the content to track the usage of digital data and protect content security. To trace traitors for multimedia over heterogeneous networks, this paper studies scalable multimedia fingerprinting systems in which users receive fingerprinted multimedia of different quality. We investigate the cost-effective multi-user collusion on fingerprinting systems and focus on fair collusion attacks in which colluders share the same risk of being captured. In this paper, we examine the fairness constraints on collusion when attackers receive copies of different quality and analyze the performance of scalable fingerprinting systems under fair collusion attacks.