  Deep neural networks are proven to be vulnerable to fine-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) \textbf{Diversity}. The robust feature of intra-class samples
can maintain appropriate diversity; 2) \textbf{Discriminability}. The robust
feature of inter-class samples should ensure adequate separation. 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 the
proposed method can achieve superior performance compared with state-of-the-art
defense approaches. The code is publicly available at
\href{https://github.com/chenboluo/Adversarial-defense}{https://github.com/chenboluo/Adversarial-defense}.
