Scalable and Statistically Secure Fingerprinting Scheme for Mobile Game Content Distribution

Digital fingerprinting is a technique to prevent customers from redistributing multimedia contents illegally. Main attack for fingerprinting is the collusion attack, where multiple users collude by creating a mix (e.g. average or median) of their individual fingerprinted copies, and escape identification. Previous research such as ACC (anti-collusion code) cannot support large number of users, and also vulnerable to LCCA (linear combination collusion attack). We present a practical SACC (scalable ACC) scheme to generate codebooks for supporting large number of users; and angular decoding scheme to be robust on LCCA. We implemented the SACC codebook using a Gaussian distributed random variable for robustness, and experimented with commercial mobile game video sequences. Experimental results show that we can achieve good collusion detection performance over average and median attacks. For LCCA collusion attack on SACC, our angular decoding scheme identifies the correct colluder set under various WNR (watermark to noise ratio).