A collusion attack optimization strategy for digital fingerprinting

Abstract

Collusion attack is a cost-efficient attack for digital fingerprinting. In this article, we propose a novel collusion attack strategy, Iterative Optimization Collusion Attack (IOCA) , which is based upon the gradient attack and the principle of informed watermark embedding. We evaluate the performance of the proposed collusion attack strategy in defeating four typical fingerprinting schemes under a well-constructed evaluation framework. The simulation results show that the proposed strategy performs more effectively than the gradient attack, and adopting no more than three fingerprinted copies can sufficiently collapse examined fingerprinting schemes. Meanwhile, the content resulted from the proposed attack still preserves high perceptual quality.