v1v2 (latest)

Understanding and Improving Fast Adversarial Training

6 July 2020

Maksym Andriushchenko

Nicolas Flammarion

AAML

ArXiv (abs)PDF HTML Github (95★)

Papers citing "Understanding and Improving Fast Adversarial Training"

50 / 193 papers shown

Title
SecurityNet: Assessing Machine Learning Vulnerabilities on Public Models Boyang Zhang Zheng Li Ziqing Yang Xinlei He Michael Backes Mario Fritz Yang Zhang 100 6 0 19 Oct 2023
IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks Yue Cao Tianlin Li Xiaofeng Cao Ivor Tsang Yang Liu Qing Guo AAML 66 2 0 18 Oct 2023
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting Runqi Lin Chaojian Yu Bo Han Tongliang Liu 78 9 0 13 Oct 2023
Generating Less Certain Adversarial Examples Improves Robust Generalization Minxing Zhang Michael Backes Xiao Zhang AAML 143 1 0 06 Oct 2023
Splitting the Difference on Adversarial Training Matan Levi A. Kontorovich 89 4 0 03 Oct 2023
Improving Machine Learning Robustness via Adversarial Training Long Dang T. Hapuarachchi Kaiqi Xiong Jing Lin OOD AAML 58 2 0 22 Sep 2023
Robust and Efficient Interference Neural Networks for Defending Against Adversarial Attacks in ImageNet Yunuo Xiong Shujuan Liu H. Xiong AAML 39 0 0 03 Sep 2023
Fast Adversarial Training with Smooth Convergence Mengnan Zhao Lulu Zhang Yuqiu Kong Baocai Yin AAML 51 8 0 24 Aug 2023
Revisiting and Exploring Efficient Fast Adversarial Training via LAW: Lipschitz Regularization and Auto Weight Averaging Xiaojun Jia YueFeng Chen Xiaofeng Mao Ranjie Duan Jindong Gu Rong Zhang H. Xue Xiaochun Cao AAML 62 11 0 22 Aug 2023
Adversarial Collaborative Filtering for Free Huiyuan Chen Xiaoting Li Vivian Lai Chin-Chia Michael Yeh Yujie Fan Yan Zheng Mahashweta Das Hao Yang AAML 56 6 0 20 Aug 2023
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 91 19 0 19 Aug 2023
SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection João Vitorino Isabel Praça Eva Maia AAML 61 26 0 13 Aug 2023
On the Interplay of Convolutional Padding and Adversarial Robustness Paul Gavrikov J. Keuper AAML 90 3 0 12 Aug 2023
An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learning Yihua Zhang Prashant Khanduri Ioannis C. Tsaknakis Yuguang Yao Min-Fong Hong Sijia Liu AI4CE 115 31 0 01 Aug 2023
Doubly Robust Instance-Reweighted Adversarial Training Daouda Sow Sen-Fon Lin Zhangyang Wang Yitao Liang AAML OOD 100 2 0 01 Aug 2023
Neural Polarizer: A Lightweight and Effective Backdoor Defense via Purifying Poisoned Features Mingli Zhu Shaokui Wei H. Zha Baoyuan Wu AAML 84 38 0 29 Jun 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 82 1 0 29 Jun 2023
DSRM: Boost Textual Adversarial Training with Distribution Shift Risk Minimization Songyang Gao Shihan Dou Yan Liu Xiao Wang Qi Zhang Zhongyu Wei Jin Ma Yingchun Shan OOD 62 4 0 27 Jun 2023
A Spectral Perspective towards Understanding and Improving Adversarial Robustness Binxiao Huang Rui Lin Chaofan Tao Ngai Wong AAML 73 0 0 25 Jun 2023
Revisiting and Advancing Adversarial Training Through A Simple Baseline Hong Liu AAML 59 0 0 13 Jun 2023
AROID: Improving Adversarial Robustness through Online Instance-wise Data Augmentation Lin Li Jianing Qiu Michael W. Spratling AAML 56 4 0 12 Jun 2023
AdvFunMatch: When Consistent Teaching Meets Adversarial Robustness Ziuhi Wu Haichang Gao Bingqian Zhou Ping Wang AAML 63 0 0 24 May 2023
$Releasing Inequality Phenomenon in $\ell_{\infty}$-norm Adversarial Training via Input Gradient Distillation$ Releasing Inequality Phenomenon in $\ell_{\infty}$ -norm Adversarial Training via Input Gradient Distillation Junxi Chen Junhao Dong Xiaohua Xie Jianhuang Lai AAML 63 0 0 16 May 2023
Exploiting Frequency Spectrum of Adversarial Images for General Robustness Chun Yang Tan K. Kawamoto Hiroshi Kera AAML OOD 61 1 0 15 May 2023
Efficient Search of Comprehensively Robust Neural Architectures via Multi-fidelity Evaluation Jialiang Sun Wen Yao Tingsong Jiang Xiaoqian Chen AAML 75 10 0 12 May 2023
Cross-Entropy Loss Functions: Theoretical Analysis and Applications Anqi Mao M. Mohri Yutao Zhong AAML 123 328 0 14 Apr 2023
Hyper-parameter Tuning for Adversarially Robust Models Pedro Mendes Paolo Romano David Garlan AAML 44 2 0 05 Apr 2023
Improving Fast Adversarial Training with Prior-Guided Knowledge Xiaojun Jia Yong Zhang Xingxing Wei Baoyuan Wu Ke Ma Jue Wang Xiaochun Cao AAML 98 32 0 01 Apr 2023
Adversarial Attack and Defense for Medical Image Analysis: Methods and Applications Junhao Dong Junxi Chen Xiaohua Xie Jianhuang Lai Hechang Chen AAML MedIm 129 19 0 24 Mar 2023
Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing Lin Li Michael W. Spratling AAML 130 4 0 24 Mar 2023
PRISE: Demystifying Deep Lucas-Kanade with Strongly Star-Convex Constraints for Multimodel Image Alignment Yiqing Zhang Xinming Huang Ziming Zhang 71 4 0 21 Mar 2023
Improving the Robustness of Deep Convolutional Neural Networks Through Feature Learning Jin Ding Jie-Chao Zhao Yongyang Sun Ping Tan Ji-en Ma You-tong Fang AAML 83 1 0 11 Mar 2023
Less is More: Data Pruning for Faster Adversarial Training Yize Li Pu Zhao Xinyu Lin B. Kailkhura Ryan Goldh AAML 115 11 0 23 Feb 2023
Investigating Catastrophic Overfitting in Fast Adversarial Training: A Self-fitting Perspective Zhengbao He Tao Li Sizhe Chen Xiaolin Huang AAML 85 4 0 23 Feb 2023
Regret-Based Defense in Adversarial Reinforcement Learning Roman Belaire Pradeep Varakantham Thanh Nguyen David Lo AAML 38 3 0 14 Feb 2023
Better Diffusion Models Further Improve Adversarial Training Zekai Wang Tianyu Pang Chao Du Min Lin Weiwei Liu Shuicheng Yan DiffM 106 228 0 09 Feb 2023
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection Xilie Xu Jingfeng Zhang Feng Liu Masashi Sugiyama Mohan S. Kankanhalli AAML 104 17 0 08 Feb 2023
Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification João Vitorino Isabel Praça Eva Maia AAML 102 28 0 30 Jan 2023
Data Augmentation Alone Can Improve Adversarial Training Lin Li Michael W. Spratling 66 55 0 24 Jan 2023
RobArch: Designing Robust Architectures against Adversarial Attacks Sheng-Hsuan Peng Weilin Xu Cory Cornelius Kevin Wenliang Li Rahul Duggal Duen Horng Chau Jason Martin AAML 57 6 0 08 Jan 2023
Explainability and Robustness of Deep Visual Classification Models Jindong Gu AAML 96 2 0 03 Jan 2023
Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks Nikolaos Antoniou Efthymios Georgiou Alexandros Potamianos AAML 71 5 0 15 Dec 2022
REAP: A Large-Scale Realistic Adversarial Patch Benchmark Nabeel Hingun Chawin Sitawarin Jerry Li David Wagner AAML 97 15 0 12 Dec 2022
DISCO: Adversarial Defense with Local Implicit Functions Chih-Hui Ho Nuno Vasconcelos AAML 130 39 0 11 Dec 2022
Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and Regularization Lin Li Michael W. Spratling AAML 92 35 0 09 Dec 2022
Advancing Deep Metric Learning Through Multiple Batch Norms And Multi-Targeted Adversarial Examples Inderjeet Singh Kazuya Kakizaki Toshinori Araki AAML OOD 75 0 0 29 Nov 2022
Towards More Robust Interpretation via Local Gradient Alignment Sunghwan Joo Seokhyeon Jeong Juyeon Heo Adrian Weller Taesup Moon FAtt 81 6 0 29 Nov 2022
DeepTaster: Adversarial Perturbation-Based Fingerprinting to Identify Proprietary Dataset Use in Deep Neural Networks Seonhye Park A. Abuadbba Shuo Wang Kristen Moore Yansong Gao Hyoungshick Kim Surya Nepal AAML 51 2 0 24 Nov 2022
Efficient Adversarial Training with Robust Early-Bird Tickets Zhiheng Xi Rui Zheng Tao Gui Qi Zhang Xuanjing Huang AAML 84 9 0 14 Nov 2022
The Enemy of My Enemy is My Friend: Exploring Inverse Adversaries for Improving Adversarial Training Junhao Dong Seyed-Mohsen Moosavi-Dezfooli Jianhuang Lai Xiaohua Xie AAML 112 29 0 01 Nov 2022