A Novel Collusion Attack Strategy for Digital Fingerprinting

Digital fingerprinting is a technology which aims to embed unique marks with traceability in order to identify users who use their multimedia content for unintended purpose. A cost-efficient attack against digital fingerprinting, known as collusion attack, involves a group of users who combine their fingerprinted content for the purpose of attenuating or removing the fingerprints. In this paper, we analyze and simulate the effect of Gaussian noise with different energies added in the noise-free forgery on both the detection performance of correlation-based detector and the perceptual quality of the attacked content. Based upon the analysis and the principal of informed watermark embedding, we propose a novel collusion attack strategy, self-adaptive noise optimization (SANO) collusion attack. The experimental results, under the assumption that orthogonal fingerprints are used, show that the proposed collusion attack performs more effectively than the most of existed collusion attacks. Less than three pieces of fingerprinted content can sufficiently interrupt orthogonal fingerprints which accommodate many thousands of users. Meanwhile, high fidelity of the attacked content is retained after the proposed collusion attack.