Provable Defenses against Spatially Transformed Adversarial Inputs: Impossibility and Possibility Results

ICLR 2019 Xinyang ZhangYifan HuangChanh NguyenShouling JiTing Wang

One intriguing property of neural networks is their inherent vulnerability to adversarial inputs, which are maliciously crafted samples to trigger target networks to misbehave. The state-of-the-art attacks generate adversarial inputs using either pixel perturbation or spatial transformation... (read more)

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