Architecture-Agnostic Iterative Black-Box Certified Defense Against Adversarial Patches

Abstract

The adversarial patch attack aims to fool image classifiers within a bounded, contiguous region of arbitrary changes. To address this problem in a trustworthy way, the certified patch defense methods are proposed. However, the state-of-the-art certified defenses inevitably needed to access the size of the adversarial patch, which is unreasonable and impractical in real-world attack scenarios. To improve the feasibility of the architecture-agnostic certified defense in a black-box setting, we propose a novel two-stage Iterative Black-box Certified Defense method, termed IBCD. In the first stage, it estimates the patch size in a search-based manner by evaluating the size relationship between the patch and mask with pixel masking. In the second stage, the accuracy results are calculated by the existing white-box certified defense methods with the estimated patch size. The experiments conducted on two popular model architectures and two datasets verify the effectiveness and efficiency of IBCD.