Recursive chaotic desynchronized fingerprint for large scale distribution using social network analysis

Average collusion attack is a very effective attack for digital fingerprinting system. Moreover, the commercial value of the colluded content is often time-sensitive. The more profit the colluder will make from it when the colluded copy is distributed earlier. This paper presents a new collusion-resilience approach with recursive chaotic desynchronization and social network. It has processed chaotic transformations due to random image grid based on chaos. The experimental results show that collusion even with only two copies results in degradation of the image metric, even if those traitors try to resynchronization using image registration technology. However, it will take expensive computational cost to do that, and the visual quality is degraded expensively with the increase of the number of fingerprinted copies. © 2016 SERSC.