On the Convergence and Robustness of Adversarial Training

15 December 2021

Quanquan Gu

Papers citing "On the Convergence and Robustness of Adversarial Training"

50 / 211 papers shown

Title
Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks Jia-Wei Liu Yaochu Jin AAML OOD 18 36 0 16 Jan 2021
Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks Yige Li Lingjuan Lyu Nodens Koren X. Lyu Bo-wen Li Xingjun Ma AAML FedML 11 428 0 15 Jan 2021
Unlearnable Examples: Making Personal Data Unexploitable Hanxun Huang Xingjun Ma S. Erfani James Bailey Yisen Wang MIACV 156 190 0 13 Jan 2021
DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning Olakunle Ibitoye M. O. Shafiq Ashraf Matrawy FedML 28 18 0 08 Jan 2021
Understanding and Increasing Efficiency of Frank-Wolfe Adversarial Training Theodoros Tsiligkaridis Jay Roberts AAML 22 11 0 22 Dec 2020
Adversarially Robust Estimate and Risk Analysis in Linear Regression Yue Xing Ruizhi Zhang Guang Cheng AAML 28 27 0 18 Dec 2020
Characterizing the Evasion Attackability of Multi-label Classifiers Zhuo Yang Yufei Han Xiangliang Zhang AAML 16 10 0 17 Dec 2020
Amata: An Annealing Mechanism for Adversarial Training Acceleration Nanyang Ye Qianxiao Li Xiao-Yun Zhou Zhanxing Zhu AAML 32 15 0 15 Dec 2020
Mitigating the Impact of Adversarial Attacks in Very Deep Networks Mohammed Hassanin Ibrahim Radwan Nour Moustafa M. Tahtali Neeraj Kumar AAML 18 5 0 08 Dec 2020
Privacy and Robustness in Federated Learning: Attacks and Defenses Lingjuan Lyu Han Yu Xingjun Ma Chen Chen Lichao Sun Jun Zhao Qiang Yang Philip S. Yu FedML 183 355 0 07 Dec 2020
Bridging the Performance Gap between FGSM and PGD Adversarial Training Tianjin Huang Vlado Menkovski Yulong Pei Mykola Pechenizkiy AAML 14 17 0 07 Nov 2020
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks Ruize Gao Feng Liu Jingfeng Zhang Bo Han Tongliang Liu Gang Niu Masashi Sugiyama AAML 19 51 0 22 Oct 2020
Towards Understanding the Dynamics of the First-Order Adversaries Zhun Deng Hangfeng He Jiaoyang Huang Weijie J. Su AAML 25 11 0 20 Oct 2020
Robust Neural Networks inspired by Strong Stability Preserving Runge-Kutta methods Byungjoo Kim Bryce Chudomelka Jinyoung Park Jaewoo Kang Youngjoon Hong Hyunwoo J. Kim AAML 12 6 0 20 Oct 2020
A Unified Approach to Interpreting and Boosting Adversarial Transferability Xin Wang Jie Ren Shuyu Lin Xiangming Zhu Yisen Wang Quanshi Zhang AAML 29 94 0 08 Oct 2020
Geometry-aware Instance-reweighted Adversarial Training Jingfeng Zhang Jianing Zhu Gang Niu Bo Han Masashi Sugiyama Mohan Kankanhalli AAML 47 269 0 05 Oct 2020
Do Wider Neural Networks Really Help Adversarial Robustness? Boxi Wu Jinghui Chen Deng Cai Xiaofei He Quanquan Gu AAML 14 95 0 03 Oct 2020
Efficient Robust Training via Backward Smoothing Jinghui Chen Yu Cheng Zhe Gan Quanquan Gu Jingjing Liu AAML 24 40 0 03 Oct 2020
Bag of Tricks for Adversarial Training Tianyu Pang Xiao Yang Yinpeng Dong Hang Su Jun Zhu AAML 25 261 0 01 Oct 2020
Improving Query Efficiency of Black-box Adversarial Attack Yang Bai Yuyuan Zeng Yong Jiang Yisen Wang Shutao Xia Weiwei Guo AAML MLAU 37 52 0 24 Sep 2020
Detection of Iterative Adversarial Attacks via Counter Attack Matthias Rottmann Kira Maag Mathis Peyron N. Krejić Hanno Gottschalk AAML 6 4 0 23 Sep 2020
Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning Yao Zhou Jun Wu Haixun Wang Jingrui He AAML FedML 36 26 0 18 Sep 2020
Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks Nilaksh Das Haekyu Park Zijie J. Wang Fred Hohman Robert Firstman Emily Rogers Duen Horng Chau AAML 23 26 0 05 Sep 2020
An Integrated Approach to Produce Robust Models with High Efficiency Zhijian Li Bao Wang Jack Xin MQ AAML 20 3 0 31 Aug 2020
On the Generalization Properties of Adversarial Training Yue Xing Qifan Song Guang Cheng AAML 25 32 0 15 Aug 2020
Adversarial Training and Provable Robustness: A Tale of Two Objectives Jiameng Fan Wenchao Li AAML 23 20 0 13 Aug 2020
Understanding and Improving Fast Adversarial Training Maksym Andriushchenko Nicolas Flammarion AAML 26 284 0 06 Jul 2020
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks Yunfei Liu Xingjun Ma James Bailey Feng Lu AAML 22 504 0 05 Jul 2020
Diverse Knowledge Distillation (DKD): A Solution for Improving The Robustness of Ensemble Models Against Adversarial Attacks Ali Mirzaeian Jana Kosecka Houman Homayoun Tinoosh Mohsening Avesta Sasan FedML AAML 25 3 0 26 Jun 2020
Smooth Adversarial Training Cihang Xie Mingxing Tan Boqing Gong Alan Yuille Quoc V. Le OOD 30 152 0 25 Jun 2020
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness Xingjun Ma Linxi Jiang Hanxun Huang Zejia Weng James Bailey Yu-Gang Jiang AAML 28 10 0 24 Jun 2020
RayS: A Ray Searching Method for Hard-label Adversarial Attack Jinghui Chen Quanquan Gu AAML 10 137 0 23 Jun 2020
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them Chen Liu Mathieu Salzmann Tao R. Lin Ryota Tomioka Sabine Süsstrunk AAML 24 81 0 15 Jun 2020
Towards Understanding Fast Adversarial Training Bai Li Shiqi Wang Suman Jana Lawrence Carin AAML 32 50 0 04 Jun 2020
Adversarial Classification via Distributional Robustness with Wasserstein Ambiguity Nam Ho-Nguyen Stephen J. Wright OOD 50 16 0 28 May 2020
Feature Purification: How Adversarial Training Performs Robust Deep Learning Zeyuan Allen-Zhu Yuanzhi Li MLT AAML 37 147 0 20 May 2020
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks Linhai Ma Liang Liang AAML 26 18 0 19 May 2020
Robust Deep Learning as Optimal Control: Insights and Convergence Guarantees Jacob H. Seidman Mahyar Fazlyab V. Preciado George J. Pappas AAML 16 15 0 01 May 2020
Adversarial Weight Perturbation Helps Robust Generalization Dongxian Wu Shutao Xia Yisen Wang OOD AAML 14 17 0 13 Apr 2020
SAT: Improving Adversarial Training via Curriculum-Based Loss Smoothing Chawin Sitawarin S. Chakraborty David Wagner AAML 25 37 0 18 Mar 2020
Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles Ranjie Duan Xingjun Ma Yisen Wang James Bailey •. A. K. Qin Yun Yang AAML 167 224 0 08 Mar 2020
Sparsity Meets Robustness: Channel Pruning for the Feynman-Kac Formalism Principled Robust Deep Neural Nets Thu Dinh Bao Wang Andrea L. Bertozzi Stanley J. Osher AAML 14 16 0 02 Mar 2020
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models Xiao Zhang Jinghui Chen Quanquan Gu David Evans 26 17 0 01 Mar 2020
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger Jingfeng Zhang Xilie Xu Bo Han Gang Niu Li-zhen Cui Masashi Sugiyama Mohan S. Kankanhalli AAML 33 397 0 26 Feb 2020
GANs May Have No Nash Equilibria Farzan Farnia Asuman Ozdaglar GAN 28 43 0 21 Feb 2020
Boosting Adversarial Training with Hypersphere Embedding Tianyu Pang Xiao Yang Yinpeng Dong Kun Xu Jun Zhu Hang Su AAML 33 154 0 20 Feb 2020
CAT: Customized Adversarial Training for Improved Robustness Minhao Cheng Qi Lei Pin-Yu Chen Inderjit Dhillon Cho-Jui Hsieh OOD AAML 27 114 0 17 Feb 2020
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets Dongxian Wu Yisen Wang Shutao Xia James Bailey Xingjun Ma AAML SILM 19 310 0 14 Feb 2020
Improving the affordability of robustness training for DNNs Sidharth Gupta Parijat Dube Ashish Verma AAML 27 15 0 11 Feb 2020
Walking on the Edge: Fast, Low-Distortion Adversarial Examples Hanwei Zhang Yannis Avrithis Teddy Furon Laurent Amsaleg AAML 17 45 0 04 Dec 2019