Scalable and Robust Fingerprinting Scheme Using Statistically Secure Extension of Anti-collusion Code

Abstract

Fingerprinting schemes use digital watermarks to determine originators of unauthorized/pirated copies. Multiple users may collude and collectively escape identification by creating an average or median of their individually watermarked copies. Previous fingerprint code design including ACC (anti-collusion code) cannot support large number of users, and we present a practical solution, which defines scalability over existing codebook generation scheme. To increase the robustness over average and median attack, we design a scalable ACC scheme using a Gaussian distributed random variable. We experiment with our scheme using human visual system based watermarking scheme, and the simulation results with standard test images show good collusion detection performance over average and median collusion attacks.