Attacks Which Do Not Kill Training Make Adversarial Learning Stronger

26 February 2020

Bo Han

Papers citing "Attacks Which Do Not Kill Training Make Adversarial Learning Stronger"

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Title
Revisiting the Relationship between Adversarial and Clean Training: Why Clean Training Can Make Adversarial Training Better MingWei Zhou Xiaobing Pei AAML 138 0 0 30 Mar 2025
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training Tejaswini Medi Steffen Jung M. Keuper AAML 36 3 0 30 Oct 2024
Adversarial Robustification via Text-to-Image Diffusion Models Daewon Choi Jongheon Jeong Huiwon Jang Jinwoo Shin DiffM 39 1 0 26 Jul 2024
Mitigating Low-Frequency Bias: Feature Recalibration and Frequency Attention Regularization for Adversarial Robustness Kejia Zhang Juanjuan Weng Yuanzheng Cai Zhiming Luo Shaozi Li AAML 59 0 0 04 Jul 2024
MEAT: Median-Ensemble Adversarial Training for Improving Robustness and Generalization Zhaozhe Hu Jia-Li Yin Bin Chen Luojun Lin Bo-Hao Chen Ximeng Liu AAML 28 0 0 20 Jun 2024
Harmonizing Feature Maps: A Graph Convolutional Approach for Enhancing Adversarial Robustness Kejia Zhang Juanjuan Weng Junwei Wu Guoqing Yang Shaozi Li Zhiming Luo AAML 38 1 0 17 Jun 2024
ADAPT to Robustify Prompt Tuning Vision Transformers Masih Eskandar Tooba Imtiaz Zifeng Wang Jennifer Dy VPVLM VLM AAML 36 0 0 19 Mar 2024
Robust Overfitting Does Matter: Test-Time Adversarial Purification With FGSM Linyu Tang Lei Zhang AAML 27 3 0 18 Mar 2024
Towards Adversarially Robust Dataset Distillation by Curvature Regularization Eric Xue Yijiang Li Haoyang Liu Yifan Shen Haohan Wang Haohan Wang DD 61 8 0 15 Mar 2024
Rethinking Invariance Regularization in Adversarial Training to Improve Robustness-Accuracy Trade-off Futa Waseda Ching-Chun Chang Isao Echizen AAML 29 0 0 22 Feb 2024
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective Yue Xing Xiaofeng Lin Qifan Song Yi Tian Xu Belinda Zeng Guang Cheng SSL 15 0 0 26 Jan 2024
Conserve-Update-Revise to Cure Generalization and Robustness Trade-off in Adversarial Training Shruthi Gowda Bahram Zonooz Elahe Arani AAML 28 2 0 26 Jan 2024
Robust Mixture-of-Expert Training for Convolutional Neural Networks Yihua Zhang Ruisi Cai Tianlong Chen Guanhua Zhang Huan Zhang Pin-Yu Chen Shiyu Chang Zhangyang Wang Sijia Liu MoE AAML OOD 32 16 0 19 Aug 2023
Doubly Robust Instance-Reweighted Adversarial Training Daouda Sow Sen-Fon Lin Zhangyang Wang Yitao Liang AAML OOD 33 2 0 01 Aug 2023
Group-based Robustness: A General Framework for Customized Robustness in the Real World Weiran Lin Keane Lucas Neo Eyal Lujo Bauer Michael K. Reiter Mahmood Sharif OOD AAML 22 1 0 29 Jun 2023
Group Orthogonalization Regularization For Vision Models Adaptation and Robustness Yoav Kurtz Noga Bar Raja Giryes 24 0 0 16 Jun 2023
How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses Joana Cabral Costa Tiago Roxo Hugo Manuel Proença Pedro R. M. Inácio AAML 34 49 0 18 May 2023
Exploiting Frequency Spectrum of Adversarial Images for General Robustness Chun Yang Tan K. Kawamoto Hiroshi Kera AAML OOD 26 1 0 15 May 2023
Beyond Empirical Risk Minimization: Local Structure Preserving Regularization for Improving Adversarial Robustness Wei Wei Jiahuan Zhou Yingying Wu AAML 13 0 0 29 Mar 2023
CAT:Collaborative Adversarial Training Xingbin Liu Huafeng Kuang Xianming Lin Yongjian Wu Rongrong Ji AAML 17 4 0 27 Mar 2023
PIAT: Parameter Interpolation based Adversarial Training for Image Classification Kun He Xin Liu Yichen Yang Zhou Qin Weigao Wen Hui Xue J. Hopcroft AAML 22 0 0 24 Mar 2023
Generalist: Decoupling Natural and Robust Generalization Hongjun Wang Yisen Wang OOD AAML 46 14 0 24 Mar 2023
An Extended Study of Human-like Behavior under Adversarial Training Paul Gavrikov J. Keuper M. Keuper AAML 28 9 0 22 Mar 2023
Randomized Adversarial Training via Taylor Expansion Gao Jin Xinping Yi Dengyu Wu Ronghui Mu Xiaowei Huang AAML 36 34 0 19 Mar 2023
Better Diffusion Models Further Improve Adversarial Training Zekai Wang Tianyu Pang Chao Du Min-Bin Lin Weiwei Liu Shuicheng Yan DiffM 16 207 0 09 Feb 2023
Beckman Defense A. V. Subramanyam OOD AAML 32 0 0 04 Jan 2023
Confidence-aware Training of Smoothed Classifiers for Certified Robustness Jongheon Jeong Seojin Kim Jinwoo Shin AAML 19 7 0 18 Dec 2022
Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning Ethan Rathbun Kaleel Mahmood Sohaib Ahmad Caiwen Ding Marten van Dijk AAML 19 4 0 26 Nov 2022
Towards Robust Dataset Learning Yihan Wu Xinda Li Florian Kerschbaum Heng Huang Hongyang R. Zhang DD OOD 41 10 0 19 Nov 2022
Learning Sample Reweighting for Accuracy and Adversarial Robustness Chester Holtz Tsui-Wei Weng Gal Mishne OOD 24 4 0 20 Oct 2022
Scaling Adversarial Training to Large Perturbation Bounds Sravanti Addepalli Samyak Jain Gaurang Sriramanan R. Venkatesh Babu AAML 25 22 0 18 Oct 2022
Robust Models are less Over-Confident Julia Grabinski Paul Gavrikov J. Keuper M. Keuper AAML 28 24 0 12 Oct 2022
A2: Efficient Automated Attacker for Boosting Adversarial Training Zhuoer Xu Guanghui Zhu Changhua Meng Shiwen Cui ZhenZhe Ying Weiqiang Wang GU Ming Yihua Huang AAML 34 13 0 07 Oct 2022
Strength-Adaptive Adversarial Training Chaojian Yu Dawei Zhou Li Shen Jun Yu Bo Han Mingming Gong Nannan Wang Tongliang Liu OOD 17 2 0 04 Oct 2022
Improving Robust Fairness via Balance Adversarial Training Chunyu Sun Chenye Xu Chengyuan Yao Siyuan Liang Yichao Wu Ding Liang XiangLong Liu Aishan Liu 23 11 0 15 Sep 2022
Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples Nuo Xu Kaleel Mahmood Haowen Fang Ethan Rathbun Caiwen Ding Wujie Wen AAML 27 12 0 07 Sep 2022
Two Heads are Better than One: Robust Learning Meets Multi-branch Models Dong Huang Qi Bu Yuhao Qing Haowen Pi Sen Wang Heming Cui OOD AAML 22 0 0 17 Aug 2022
Removing Batch Normalization Boosts Adversarial Training Haotao Wang Aston Zhang Shuai Zheng Xingjian Shi Mu Li Zhangyang Wang 32 41 0 04 Jul 2022
Analysis and Extensions of Adversarial Training for Video Classification K. A. Kinfu René Vidal AAML 25 13 0 16 Jun 2022
Building Robust Ensembles via Margin Boosting Dinghuai Zhang Hongyang R. Zhang Aaron Courville Yoshua Bengio Pradeep Ravikumar A. Suggala AAML UQCV 35 15 0 07 Jun 2022
Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile Dong Chen Lingfei Wu Siliang Tang Xiao Yun Bo Long Yueting Zhuang VLM NoLa 23 9 0 04 Jun 2022
Superclass Adversarial Attack Soichiro Kumano Hiroshi Kera T. Yamasaki AAML 30 1 0 29 May 2022
CE-based white-box adversarial attacks will not work using super-fitting Youhuan Yang Lei Sun Leyu Dai Song Guo Xiuqing Mao Xiaoqin Wang Bayi Xu AAML 24 0 0 04 May 2022
Universum-inspired Supervised Contrastive Learning Aiyang Han Chuanxing Geng Songcan Chen SSL 21 3 0 22 Apr 2022
Adversarial Robustness through the Lens of Convolutional Filters Paul Gavrikov J. Keuper 30 15 0 05 Apr 2022
A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies Zhuang Qian Kaizhu Huang Qiufeng Wang Xu-Yao Zhang OOD AAML ObjD 47 71 0 26 Mar 2022
Self-Ensemble Adversarial Training for Improved Robustness Hongjun Wang Yisen Wang OOD AAML 11 48 0 18 Mar 2022
LAS-AT: Adversarial Training with Learnable Attack Strategy Xiaojun Jia Yong Zhang Baoyuan Wu Ke Ma Jue Wang Xiaochun Cao AAML 41 131 0 13 Mar 2022
A Unified Wasserstein Distributional Robustness Framework for Adversarial Training Tu Bui Trung Le Quan Hung Tran He Zhao Dinh Q. Phung AAML OOD 31 42 0 27 Feb 2022
On the Effectiveness of Adversarial Training against Backdoor Attacks Yinghua Gao Dongxian Wu Jingfeng Zhang Guanhao Gan Shutao Xia Gang Niu Masashi Sugiyama AAML 32 22 0 22 Feb 2022