Detecting Adversarial Patch Attacks through Global-local Consistency

Recent works have well-demonstrated the threat of adversarial patch attacks to real-world vision media systems. By arbitrarily modifying pixels within a small restricted area in the image, adversarial patches can mislead neural-network-based image classifiers. In this paper, we propose a simple but very effective approach to detect adversarial patches based on an interesting observation called global-local consistency. We verify this insight and propose to use Random-Local-Ensemble (RLE) strategy to further enhance it in the detection. The proposed method is trivial to implement and can be applied to protect any image classification models. Experiments on two popular datasets show that our algorithm can accurately detect the adversarial patches while maintaining high clean accuracy. Moreover, unlike the prior detection approaches which can be easily broken by adaptive attacks, our method is proved to have high robustness when facing adaptive attacks.