Turning Your Strength against You: Detecting and Mitigating Robust and Universal Adversarial Patch Attacks. (arXiv:2108.05075v3 [cs.CR] UPDATED)

Adversarial patch attacks that inject arbitrary distortions within a bounded
region of an image, can trigger misclassification in deep neural networks
(DNNs). These attacks are robust (i.e., physically realizable) and universally
malicious, and hence represent a severe security threat to real-world DNN-based
systems.

This work proposes Jujutsu, a two-stage technique to detect and mitigate
robust and universal adversarial patch attacks. We first observe that patch
attacks often yield large influence on the prediction output in order to
dominate the prediction …

adversarial attacks patch strength