Must-read papers on Adversarial training for neural network models. The paper list is mantained by Shiwen Ni.
This is a paper list about Adversarial training for neural network models. Note that the recommended papers are those that I have read and found to be good.
⭐️ This list is constantly being updated!
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Explaining and Harnessing Adversarial Examples, ICLR 2015.
Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy [pdf], [code], (FGSM).
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Adversarial Training Methods for Semi-Supervised Text Classification, ICLR 2017.
Takeru Miyato, Andrew M. Dai, Ian Goodfellow [pdf], [code], (FGM).
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Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples, ICML 2018.
Anish Athalye, Nicholas Carlini, David Wagner [pdf], [code], (PGD).
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Adversarial Training for Free!, NeurIPS 2019.
Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein [pdf], [code], (FreeAT).
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You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle, NeurIPS 2019.
Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong [pdf], [code], (YOPO).
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FreeLB: Enhanced Adversarial Training for Natural Language Understanding, ICLR 2020.
Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, Jingjing Liu [pdf], [code], (FreeLB).
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DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks, ArXiv 2021.
Shiwen Ni, Jiawen Li, Hung-Yu Kao [pdf], [code], (DropAttack).
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What Doesn't Kill You Makes You Robust(er): Adversarial Training against Poisons and Backdoors
Jonas Geiping, Liam Fowl, Gowthami Somepalli, Micah Goldblum, Michael Moeller, Tom Goldstein, 2021.
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Attacks Which Do Not Kill Training Make Adversarial Learning Stronger
Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan Kankanhalli, ICML 2020.
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On the Convergence and Robustness of Adversarial Training
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu, ICML 2019.
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Curriculum Adversarial Training
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu, IJCAI 2018.
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Rademacher Complexity for Adversarially Robust Generalization
Dong Yin, Ramchandran Kannan, Peter Bartlett, ICML 2019.
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Deep Defense: Training DNNs with Improved Adversarial Robustness
Ziang Yan, Yiwen Guo, Changshui Zhang, NeurIPS 2018.
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Single-Step Adversarial Training With Dropout Scheduling
B. S. Vivek; R. Venkatesh Babu, CVPR 2020.
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Adversarial Training and Provable Defenses: Bridging the Gap
Mislav Balunovic, Martin Vechev, ICLR 2020.
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Adversarial Examples: Attacks and Defenses for Deep Learning
Xiaoyong Yuan; Pan He; Qile Zhu; Xiaolin Li, TNNLS 2019.
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Reliably fast adversarial training via latent adversarial perturbation
Geon Yeong Park, Sang Wan Lee, ICLR 2021.
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Rumor Detection on Social Media with Hierarchical Adversarial Training
Shiwen Ni, Jiawen Li, Hung-Yu Kao, 2022.