A Secure Fingerprinted Multimedia Distribution Using Social Network Analysis

Collusion attack is a very effective attack for digital fingerprinting system. In order to remove or attenuate the fingerprint information hidden in fingerprinted content, a number of users produce a new colluded copy through their own fingerprinted copies. In this paper, we address a novel collusion-resisting desynchronization fingerprinting approach using social network analysis. The novelty of this paper is that collusion attack occurred in a multimedia social network community with high probability. Different from all existing works, with desynchronization model constructed upon social network, the original image is desynchronized to get many similar copies which are different from each other, and then they are assigned and distributed to subscribers according to social network analysis. Theory analysis shows that the presented desynchronization distribution method has significantly better performance than those existing distribution schemes. The experimental results also show that the average colluded images even with only two desynchronized copies have poor visual quality. And the visual quality of colluded images does not improve apparently with the increase of the number of copies.