Abstract: Adversarial examples for fooling deep learning models have been studied for
several years and are still a hot topic. Adversarial training also receives
enormous attention because of its effectiveness in defending adversarial
examples. However, adversarial training is not perfect, many questions of which
remain to solve. During the last few years, researchers in this community have
studied and discussed adversarial training from various aspects. Many new
theories and understandings of adversarial training have been proposed. In this
survey, we systematically review the recent progress on adversarial training
for the first time, categorized by different improvements. 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.