Fast and Reliable Evaluation of Adversarial Robustness with
Minimum-Margin Attack

Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack

International Conference on Machine Learning (ICML), 2022

15 June 2022

Bo Han

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Papers citing "Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack"

8 / 8 papers shown

Title
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains Jiawen Zhang Zhenwei Zhang Shun Zheng Xumeng Wen Jia Li Jiang Bian AI4TS AAML 335 1 0 26 May 2025
One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy Jiacheng Zhang Benjamin I. P. Rubinstein Jing Zhang Yifan Zhang 331 2 0 04 Mar 2025
Segment-Anything Models Achieve Zero-shot Robustness in Autonomous Driving Jun Yan Pengyu Wang Danni Wang Weiquan Huang Daniel Watzenig Huilin Yin AAML VLM 169 6 0 19 Aug 2024
Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training Jiacheng Zhang Yifan Zhang Dawei Zhou Jingfeng Zhang Tongliang Liu AAML 182 7 0 02 Jun 2024
OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift Lin Li Yifei Wang Chawin Sitawarin Michael W. Spratling 232 11 0 19 Oct 2023
Cooperation or Competition: Avoiding Player Domination for Multi-Target Robustness via Adaptive BudgetsComputer Vision and Pattern Recognition (CVPR), 2023 Yimu Wang Dinghuai Zhang Yihan Wu Heng Huang Hongyang R. Zhang AAML 133 1 0 27 Jun 2023
Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation ScoreInternational Conference on Machine Learning (ICML), 2023 Shuhai Zhang Yifan Zhang Jiahao Yang Yifan Yang Changsheng Li Bo Han Zhuliang Yu DiffM AAML 126 33 0 25 May 2023
Towards Lightweight Black-Box Attacks against Deep Neural Networks Chenghao Sun Yonggang Zhang Chaoqun Wan Qizhou Wang Ya Li Tongliang Liu Bo Han Xinmei Tian AAML MLAU 204 5 0 29 Sep 2022