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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...
In this work, we present a decision fusion strategy for image forensics. We define a framework that exploits information provided by available forensic tools to yield a global judgment about the authenticity of an image. Sources of information are modeled and fused using Dempster–Shafer Theory of Evidence, since this theory allows us to handle uncertain answers from tools and lack of knowledge about...
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