Understanding and Improving Fast Adversarial Training

6 July 2020

Papers citing "Understanding and Improving Fast Adversarial Training"

50 / 61 papers shown

Title
Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness Olukorede Fakorede Modeste Atsague Jin Tian AAML 37 0 0 31 Dec 2024
On Using Certified Training towards Empirical Robustness Alessandro De Palma Serge Durand Zakaria Chihani François Terrier Caterina Urban OOD AAML 33 1 0 02 Oct 2024
On the Robustness of Kolmogorov-Arnold Networks: An Adversarial Perspective Tal Alter Raz Lapid Moshe Sipper AAML 56 6 0 25 Aug 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
One Noise to Rule Them All: Multi-View Adversarial Attacks with Universal Perturbation Mehmet Ergezer Phat Duong Christian Green Tommy Nguyen Abdurrahman Zeybey AAML 26 2 0 02 Apr 2024
Robust Overfitting Does Matter: Test-Time Adversarial Purification With FGSM Linyu Tang Lei Zhang AAML 29 3 0 18 Mar 2024
Catastrophic Overfitting: A Potential Blessing in Disguise Mengnan Zhao Lihe Zhang Yuqiu Kong Baocai Yin AAML 41 1 0 28 Feb 2024
Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion Yichi Zhang Zhuo Chen Lei Liang Hua-zeng Chen Wen Zhang 51 4 0 22 Feb 2024
SecurityNet: Assessing Machine Learning Vulnerabilities on Public Models Boyang Zhang Zheng Li Ziqing Yang Xinlei He Michael Backes Mario Fritz Yang Zhang 26 4 0 19 Oct 2023
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting Runqi Lin Chaojian Yu Bo Han Tongliang Liu 22 7 0 13 Oct 2023
Generating Less Certain Adversarial Examples Improves Robust Generalization Minxing Zhang Michael Backes Xiao Zhang AAML 40 1 0 06 Oct 2023
Improving Machine Learning Robustness via Adversarial Training Long Dang T. Hapuarachchi Kaiqi Xiong Jing Lin OOD AAML 30 2 0 22 Sep 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 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
$Releasing Inequality Phenomena in $L_{\infty}$-Adversarial Training via Input Gradient Distillation$ Releasing Inequality Phenomena in $L_{\infty}$ -Adversarial Training via Input Gradient Distillation Junxi Chen Junhao Dong Xiaohua Xie AAML 16 0 0 16 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
PRISE: Demystifying Deep Lucas-Kanade with Strongly Star-Convex Constraints for Multimodel Image Alignment Yiqing Zhang Xinming Huang Ziming Zhang 34 4 0 21 Mar 2023
Better Diffusion Models Further Improve Adversarial Training Zekai Wang Tianyu Pang Chao Du Min-Bin Lin Weiwei Liu Shuicheng Yan DiffM 18 207 0 09 Feb 2023
Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification João Vitorino Isabel Praça Eva Maia AAML 29 28 0 30 Jan 2023
Data Augmentation Alone Can Improve Adversarial Training Lin Li Michael W. Spratling 16 50 0 24 Jan 2023
Explainability and Robustness of Deep Visual Classification Models Jindong Gu AAML 39 2 0 03 Jan 2023
Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks Nikolaos Antoniou Efthymios Georgiou Alexandros Potamianos AAML 27 5 0 15 Dec 2022
REAP: A Large-Scale Realistic Adversarial Patch Benchmark Nabeel Hingun Chawin Sitawarin Jerry Li David A. Wagner AAML 29 14 0 12 Dec 2022
Towards More Robust Interpretation via Local Gradient Alignment Sunghwan Joo Seokhyeon Jeong Juyeon Heo Adrian Weller Taesup Moon FAtt 25 5 0 29 Nov 2022
Robust Models are less Over-Confident Julia Grabinski Paul Gavrikov J. Keuper M. Keuper AAML 28 24 0 12 Oct 2022
Bag of Tricks for FGSM Adversarial Training Zichao Li Li Liu Zeyu Wang Yuyin Zhou Cihang Xie AAML 23 6 0 06 Sep 2022
Adversarial Vulnerability of Temporal Feature Networks for Object Detection Svetlana Pavlitskaya Nikolai Polley Michael Weber J. Marius Zöllner AAML 14 2 0 23 Aug 2022
Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance Bahjat Kawar Roy Ganz Michael Elad DiffM 21 38 0 18 Aug 2022
Towards Efficient Adversarial Training on Vision Transformers Boxi Wu Jindong Gu Zhifeng Li Deng Cai Xiaofei He Wei Liu ViT AAML 35 37 0 21 Jul 2022
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale Gaoyuan Zhang Songtao Lu Yihua Zhang Xiangyi Chen Pin-Yu Chen Quanfu Fan Lee Martie L. Horesh Min-Fong Hong Sijia Liu OOD 24 12 0 13 Jun 2022
How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations? Alvin Chan Yew-Soon Ong Clement Tan AAML 22 13 0 09 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
Fast AdvProp Jieru Mei Yucheng Han Yutong Bai Yixiao Zhang Yingwei Li Xianhang Li Alan Yuille Cihang Xie AAML 24 8 0 21 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 49 71 0 26 Mar 2022
Why adversarial training can hurt robust accuracy Jacob Clarysse Julia Hörrmann Fanny Yang AAML 13 18 0 03 Mar 2022
Semi-Implicit Hybrid Gradient Methods with Application to Adversarial Robustness Beomsu Kim Junghoon Seo AAML 20 0 0 21 Feb 2022
The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining Murat Kuzlu Ferhat Ozgur Catak Umit Cali Evren Çatak Ozgur Guler AAML 24 9 0 16 Feb 2022
Random Walks for Adversarial Meshes Amir Belder Gal Yefet Ran Ben Izhak A. Tal AAML 25 2 0 15 Feb 2022
Layer-wise Regularized Adversarial Training using Layers Sustainability Analysis (LSA) framework Mohammad Khalooei M. Homayounpour M. Amirmazlaghani AAML 17 3 0 05 Feb 2022
Scale-Invariant Adversarial Attack for Evaluating and Enhancing Adversarial Defenses Mengting Xu Tao Zhang Zhongnian Li Daoqiang Zhang AAML 30 1 0 29 Jan 2022
On the Convergence and Robustness of Adversarial Training Yisen Wang Xingjun Ma James Bailey Jinfeng Yi Bowen Zhou Quanquan Gu AAML 192 345 0 15 Dec 2021
Subspace Adversarial Training Tao Li Yingwen Wu Sizhe Chen Kun Fang Xiaolin Huang AAML OOD 36 56 0 24 Nov 2021
Robust and Accurate Object Detection via Self-Knowledge Distillation Weipeng Xu Pengzhi Chu Renhao Xie Xiongziyan Xiao Hongcheng Huang AAML ObjD 19 4 0 14 Nov 2021
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks Maksym Yatsura J. H. Metzen Matthias Hein OOD 24 14 0 02 Nov 2021
BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining Weizhe Hua Yichi Zhang Chuan Guo Zhiru Zhang G. E. Suh OOD 39 15 0 29 Sep 2021
SoK: Machine Learning Governance Varun Chandrasekaran Hengrui Jia Anvith Thudi Adelin Travers Mohammad Yaghini Nicolas Papernot 30 16 0 20 Sep 2021
Advances in adversarial attacks and defenses in computer vision: A survey Naveed Akhtar Ajmal Saeed Mian Navid Kardan M. Shah AAML 26 235 0 01 Aug 2021
RobustNav: Towards Benchmarking Robustness in Embodied Navigation Prithvijit Chattopadhyay Judy Hoffman Roozbeh Mottaghi Aniruddha Kembhavi 18 55 0 08 Jun 2021