Benchmarking of scoring functions for bias-based fingerprinting code

Abstract

The study of universal detector for fingerprinting code is strongly dependent on the design of scoring function. The best detector is known as the MAP detector that calculates an optimal correlation score, but the number of colluders and their collusion strategy are inevitable. Although there are some scoring functions under some collusion strategies and asymptotic analyses, their numerical evaluation has not been done. In this study, their performance is evaluated for some typical collusion strategies using a discretized bias-based binary fingerprinting code. We also propose a simple but efficient scoring function based on a heuristic observation.