nishiwen1214/AT_Papers

Must-read papers on Adversarial training for neural networks！

Adversarial training papers

Must-read papers on Adversarial training for neural network models. The paper list is mantained by Shiwen Ni.

Contents

• Adversarial training papers
  • Contents
  • Introduction
  • Papers
    • Recommended Papers
    • General Paper

Introduction

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！

Papers

Recommended Papers

1. Explaining and Harnessing Adversarial Examples, ICLR 2015.

   Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy [pdf], [code], (FGSM).

2. Adversarial Training Methods for Semi-Supervised Text Classification, ICLR 2017.

   Takeru Miyato, Andrew M. Dai, Ian Goodfellow [pdf], [code], (FGM).

3. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples, ICML 2018.

   Anish Athalye, Nicholas Carlini, David Wagner [pdf], [code], (PGD).

4. 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).

5. 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).

6. 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).

7. 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).

General Paper

1. 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.

2. 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.

3. On the Convergence and Robustness of Adversarial Training

   Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu, ICML 2019.

4. Curriculum Adversarial Training

   Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu, IJCAI 2018.

5. Rademacher Complexity for Adversarially Robust Generalization

   Dong Yin, Ramchandran Kannan, Peter Bartlett, ICML 2019.

6. Deep Defense: Training DNNs with Improved Adversarial Robustness

   Ziang Yan, Yiwen Guo, Changshui Zhang, NeurIPS 2018.

7. Single-Step Adversarial Training With Dropout Scheduling

   B. S. Vivek; R. Venkatesh Babu, CVPR 2020.

8. Adversarial Training and Provable Defenses: Bridging the Gap

   Mislav Balunovic, Martin Vechev, ICLR 2020.

9. Adversarial Examples: Attacks and Defenses for Deep Learning

   Xiaoyong Yuan; Pan He; Qile Zhu; Xiaolin Li, TNNLS 2019.

10. Reliably fast adversarial training via latent adversarial perturbation

    Geon Yeong Park, Sang Wan Lee, ICLR 2021.

11. Rumor Detection on Social Media with Hierarchical Adversarial Training

    Shiwen Ni, Jiawen Li, Hung-Yu Kao, 2022.