The study on improving the robustness of deep neural networks against adversarial examples grows rapidly in recent years. Among them, adversarial training is the most promising one, based on which, a lot of improvements have been developed, such as adding regularizations or leveraging unlabeled data. However, these improvements seem to come from isolated perspectives, so that we are curious about if there is something in common behind them. In this paper, we investigate the surface geometry of several well-recognized adversarial training variants, and reveal that their adversarial loss landscape is closely related to the adversarially robust generalization, i.e., the flatter the adversarial loss landscape, the smaller the adversarially robust generalization gap. Based on this finding, we then propose a simple yet effective module, Adversarial Weight Perturbation (AWP), to directly regularize the flatness of the adversarial loss landscape in the adversarial training framework. Extensive experiments demonstrate that AWP indeed owns flatter landscape and can be easily incorporated into various adversarial training variants to enhance their adversarial robustness further.

本文研究了通过对抗性样本训练神经网络的鲁棒性问题，提出了通过实现加权损失对抗扰动机制进行对抗性训练的方案，从而解决了加权损失在以往对抗性训练中的不足。

对抗性权重扰动有助于稳健泛化