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Fast Adversarial Training (FAT) not only improves the model robustness but also reduces the training cost of standard adversarial training.
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Y. Dong, F. Liao, T. Pang, H. Su, J. Zhu, X. Hu, and J. Li, “Boosting adversarial attacks with momentum,” in 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018 . Computer Vision Foundation / IEEE Computer Society, 2018, pp. 9185–9193. [Online]. Available: http://openaccess.thecvf.com/content\_cvpr\_2018/html/Dong\_Boosting\_Adversarial\_Attacks\_CVPR\_2018\_paper.html
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2018
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Y. Wang, X. Ma, J. Bailey, J. Yi, B. Zhou, and Q. Gu, “On the convergence and robustness of adversarial training,” vol. 97, pp. 6586–6595, 2019. [Online]. Available: http://proceedings.mlr.press/v97/wang19i.html
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A. Shafahi, M. Najibi, A. Ghiasi, Z. Xu, J. P. Dickerson, C. Studer, L. S. Davis, G. Taylor, and T. Goldstein, “Adversarial training for free!” pp. 3353–3364, 2019. [Online]. Available: https://proceedings.neurips.cc/paper/2019/hash/7503cfacd12053d309b6bed5c89de212-Abstract.html
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H. Kim, W. Lee, and J. Lee, “Understanding catastrophic overfitting in single-step adversarial training,” in Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021 . AAAI Press, 2021, pp. 8119–8127. [Online]. Available: https://ojs.aaai.org/index.php/AAAI/article/view/16989
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2020
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X. Jia, X. Wei, X. Cao, and X. Han, “Adv-watermark: A novel watermark perturbation for adversarial examples,” in MM ’20: The 28th ACM International Conference on Multimedia, Virtual Event / Seattle, WA, USA, October 12-16, 2020 , C. W. Chen, R. Cucchiara, X. Hua, G. Qi, E. Ricci, Z. Zhang, and R. Zimmermann, Eds. ACM, 2020, pp. 1579–1587. [Online]. Available: https://doi.org/10.1145/3394171.3413976
2020
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R. Duan, X. Ma, Y. Wang, J. Bailey, A. K. Qin, and Y. Yang, “Adversarial camouflage: Hiding physical-world attacks with natural styles,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020 . Computer Vision Foundation / IEEE, 2020, pp. 997–1005. [Online]. Available: https://openaccess.thecvf.com/content\_CVPR\_2020/html/Duan\_Adversarial\_Camouflage\_Hiding\_Physical-World\_Attacks\_With\_Natural\_Styles\_CVPR\_2020\_paper.html
2020
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Y. Wang, D. Zou, J. Yi, J. Bailey, X. Ma, and Q. Gu, “Improving adversarial robustness requires revisiting misclassified examples,” in 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net, 2020. [Online]. Available: https://openreview.net/forum?id=rklOg6EFwS
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J. Zhang, X. Xu, B. Han, G. Niu, L. Cui, M. Sugiyama, and M. S. Kankanhalli, “Attacks which do not kill training make adversarial learning stronger,” in Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event , ser. Proceedings of Machine Learning Research, vol. 119. PMLR, 2020, pp. 11 278–11 287. [Online]. Available: http://proceedings.mlr.press/v119/zhang20z.html
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E. Wong, L. Rice, and J. Z. Kolter, “Fast is better than free: Revisiting adversarial training,” 2020. [Online]. Available: https://openreview.net/forum?id=BJx040EFvH
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H. Zheng, Z. Zhang, J. Gu, H. Lee, and A. Prakash, “Efficient adversarial training with transferable adversarial examples,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020 . Computer Vision Foundation / IEEE, 2020, pp. 1178–1187. [Online]. Available: https://openaccess.thecvf.com/content\_CVPR\_2020/html/Zheng\_Efficient\_Adversarial\_Training\_With\_Transferable\_Adversarial\_Examples\_CVPR\_2020\_paper.html
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M. Andriushchenko and N. Flammarion, “Understanding and improving fast adversarial training,” 2020. [Online]. Available: https://proceedings.neurips.cc/paper/2020/hash/b8ce47761ed7b3b6f48b583350b7f9e4-Abstract.html
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2021
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