This paper proposes a major shift in the decoding of probabilistic Tardos traitor tracing code. The goal of the decoder is to accuse colluders but it ignores how they have been mixing their copies in order to forge the pirated content. As originally proposed by Tardos, so far proposed decoders are agnostic and their performances are stable with respect to this unknown collusion attack. However, this stability automatically leads to non-optimality from a detection theory perspective. This is the reason why this paper proposes to estimate the collusion attack in order to approximate the optimal matched decoder. This is done iteratively thanks to the application of the well-known Expectation-Maximization algorithm. We have dropped the stability: the power of our decoding algorithm deeply depends on the collusion attack. Some attacks are worse than others. However, even for the worst collusion channel, our decoder performs better than the original Tardos decoding.

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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.