Adversarial Source Identification Game with Corrupted Training
This work addresses security in source identification for scenarios with adversarial data corruption, though it appears incremental as it builds on existing game-theoretic frameworks.
The paper tackles the problem of identifying the source of a test sequence when an attacker can corrupt the training data, deriving the unique rationalizable equilibrium in an asymptotic regime and analyzing the best achievable performance for the defender under exponential error constraints. It quantifies the ultimate distinguishability of sources based on distortion and corruption levels.
We study a variant of the source identification game with training data in which part of the training data is corrupted by an attacker. In the addressed scenario, the defender aims at deciding whether a test sequence has been drawn according to a discrete memoryless source $X \sim P_X$, whose statistics are known to him through the observation of a training sequence generated by $X$. In order to undermine the correct decision under the alternative hypothesis that the test sequence has not been drawn from $X$, the attacker can modify a sequence produced by a source $Y \sim P_Y$ up to a certain distortion, and corrupt the training sequence either by adding some fake samples or by replacing some samples with fake ones. We derive the unique rationalizable equilibrium of the two versions of the game in the asymptotic regime and by assuming that the defender bases its decision by relying only on the first order statistics of the test and the training sequences. By mimicking Stein's lemma, we derive the best achievable performance for the defender when the first type error probability is required to tend to zero exponentially fast with an arbitrarily small, yet positive, error exponent. We then use such a result to analyze the ultimate distinguishability of any two sources as a function of the allowed distortion and the fraction of corrupted samples injected into the training sequence.