Anti-collusion forensics of multimedia fingerprinting using orthogonal modulation

Abstract

Abstract-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 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.

Behavior modeling and forensics for multimedia social networks

“…The gradient attack was proposed in [24], which uses the combination of several basic nonlinear collusion attacks in [23]. The work in [25] evaluated the collusion resistance of multimedia fingerprints as a function of system parameters, including fingerprint length, total number of users, and system requirements.…”

Multi-User Collusion Behavior Forensics: Game Theoretic Formulation of Fairness Dynamics

“…In this paper, we first generate independent vector from Gaussian distribution N (0, σ 2 w ), and then apply Gram-Schmidt orthogonalization to generate orthogonal fingerprints for different users. Multi-user Collusion In this paper, we only consider averaging based collusion because nonlinear collusion can be modelled as averaging collusion with additive noise [7], and all collusion attacks have similar performance with colluded copies of the same quality.…”

Selfish Colluder Detection and Identification in Traitors within Traitors

“…Recent investigation in [4] showed that, under the constraints that the colluded copies under different collusions have the same perceptual quality, the performance of nonlinear collusion attacks is similar to that of the averaging attack. Thus, we only consider the averaging based collusion attacks in this paper.…”

“…During collusion, an important issue is the fairness of the attack, i.e., whether all colluders have the same probability of being detected. Most prior work assumed that all colluders keep their agreement to share the risk during collusion and focused on the analysis of collusion strategies and effectiveness [1][2][3][4].…”