Improving Adversarial Robustness via Decoupled Visual Representation Masking

Deep neural networks are proven to be vulnerable to finely designed adversarial examples, and adversarial defense algorithms draw more and more attention nowadays. Pre-processing based defense is a major strategy, as well as learning robust feature representation, has been proven an effective way to boost generalization. However, existing defense works lack considering different depth-level visual features in the training process. In this paper, we first highlight two novel properties of robust features from the feature distribution perspective: 1) Diversity (robust features within the same class should maintain appropriate variety). 2) Discriminability (robust features from different classes should be sufficiently separated). We find that state-of-the-art defense methods aim to address both of these mentioned issues well. It motivates us to increase intra-class variance and decrease inter-class discrepancy simultaneously in adversarial training. Specifically, we propose a simple but effective defense based on decoupled visual representation masking. The designed Decoupled Visual Feature Masking (DFM) block can adaptively disentangle visual discriminative features and non-visual features with diverse mask strategies, while the suitable discarding information can disrupt adversarial noise to improve robustness. Our work provides a generic and easy-to-plugin block unit for any former adversarial training algorithm to achieve better protection integrally. Extensive experimental results prove that the proposed method can achieve superior performance compared with state-of-the-art defense approaches. The code is publicly available at https://github.com/chenboluo/Adversarial-defense.