WORST CASE ATTACKS AGAINST BINARY PROBABILISTIC TRAITOR TRACING CODES

摘要

This article deals with traitor tracing which is also known as active fingerprinting, content serialization, or user forensics. We study the impact of worst case attacks on the well-known Tardos binary probabilistic traitor tracing code, and especially its optimum setups recently advised by Amiri and Tardos, and by Huang and Moulin. This paper assesses that these optimum setups are robust in the sense that a discrepancy between the foreseen numbers of colluders and its actual value doesn't spoil the achievable rate of a joint decoder. On the other hand, this discrepancy might have a dramatic impact on a simple decoder. Since the complexity of the today's joint decoder is prohibitive, this paper mitigates the impact of the optimum setups in current realizable schemes.