Recent Advances in Adversarial Training for Adversarial Robustness
Abstract
Adversarial training is one of the most effective approaches for deep learning models to defend against adversarial examples. Unlike other defense strategies, adversarial training aims to enhance the robustness of models intrinsically. During the past few years, adversarial training has been studied and discussed from various aspects, which deserves a comprehensive review. For the first time in this survey, we systematically review the recent progress on adversarial training for adversarial robustness with a novel taxonomy. Then we discuss the generalization problems in adversarial training from three perspectives and highlight the challenges which are not fully tackled. Finally, we present potential future directions.
Cite
Text
Bai et al. "Recent Advances in Adversarial Training for Adversarial Robustness." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/591Markdown
[Bai et al. "Recent Advances in Adversarial Training for Adversarial Robustness." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/bai2021ijcai-recent/) doi:10.24963/IJCAI.2021/591BibTeX
@inproceedings{bai2021ijcai-recent,
title = {{Recent Advances in Adversarial Training for Adversarial Robustness}},
author = {Bai, Tao and Luo, Jinqi and Zhao, Jun and Wen, Bihan and Wang, Qian},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2021},
pages = {4312-4321},
doi = {10.24963/IJCAI.2021/591},
url = {https://mlanthology.org/ijcai/2021/bai2021ijcai-recent/}
}