Recent Advances in Adversarial Training for Adversarial Robustness

Adversarial training is one of the most effective approaches defending against adversarial examples for deep learning models. Unlike other defense strategies, adversarial training aims to promote the robustness of models intrinsically. During the last few years, adversarial training has been studied and discussed from various aspects. A variety of improvements and developments of adversarial training are proposed, but neglected in existing surveys. In this survey, we systematically review the recent progress on adversarial training with a novel taxonomy for the first time. Then we discuss the generalization problems in adversarial training from three perspectives. Finally, we highlight the challenges which are not fully solved and present potential future directions.