Adversary Attack

Universal Distributional Decision-Based Black-Box Adversarial Attack with Reinforcement Learning
The vulnerability of the high-performance machine learning models implies a security risk in applications with real-world consequences. In this work, we propose a pixel-wise decision-based attack algorithm that finds a distribution of adversarial perturbation through a reinforcement learning algorithm.
黄逸然, Yexu Zhou, Michael Hefenbrock, Likun Fang, Till Riedel, Michael Beigl
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Universal Distributional Decision-Based Black-Box Adversarial Attack with Reinforcement Learning