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We analyze the distinguishability of two sources in a Neyman–Pearson setup when an attacker is allowed to modify the output of one of the two sources subject to an additive distortion constraint. By casting the problem in a game-theoretic framework and by exploiting the parallelism between the attacker’s goal and optimal transport theory, we introduce the concept of security margin defined as the...
We study a new variant of the source identification game with training data in which part of the training data is corrupted by an adversary. In such a scenario, the defender wants to decide whether a test sequence ξn has been drawn from the same source which generated a training sequence tN, part of which has been corrupted by the adversary. By adopting a game theoretical formulation, we derive the...
We analyze the distinguishability of two sources under adversarial conditions, when the error exponents of type I and type II error probabilities are allowed to take an arbitrarily small, yet positive, values. By exploiting the parallelism between the attacker's goal and optimal transport theory, we introduce the concept of Security Margin defined as the maximum average per-sample distortion introduced...
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