Defenses in Adversarial Machine Learning: A Survey

13 December 2023

Hongrui Chen

Papers citing "Defenses in Adversarial Machine Learning: A Survey"

50 / 101 papers shown

Title
Do Wider Neural Networks Really Help Adversarial Robustness? Boxi Wu Jinghui Chen Deng Cai Xiaofei He Quanquan Gu AAML 73 95 0 03 Oct 2020
Adversarially Robust Neural Architectures Minjing Dong Yanxi Li Yunhe Wang Chang Xu AAML OOD 82 49 0 02 Sep 2020
One-pixel Signature: Characterizing CNN Models for Backdoor Detection Shanjiaoyang Huang Weiqi Peng Zhiwei Jia Zhuowen Tu 47 64 0 18 Aug 2020
Anti-Bandit Neural Architecture Search for Model Defense Hanlin Chen Baochang Zhang Shenjun Xue Xuan Gong Hong Liu Rongrong Ji David Doermann AAML 45 35 0 03 Aug 2020
Removing Backdoor-Based Watermarks in Neural Networks with Limited Data Xuankai Liu Fengting Li Bihan Wen Qi Li AAML 63 61 0 02 Aug 2020
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning Hongyi Wang Kartik K. Sreenivasan Shashank Rajput Harit Vishwakarma Saurabh Agarwal Jy-yong Sohn Kangwook Lee Dimitris Papailiopoulos FedML 89 611 0 09 Jul 2020
Smooth Adversarial Training Cihang Xie Mingxing Tan Boqing Gong Alan Yuille Quoc V. Le OOD 80 154 0 25 Jun 2020
A Self-supervised Approach for Adversarial Robustness Muzammal Naseer Salman Khan Munawar Hayat Fahad Shahbaz Khan Fatih Porikli AAML 87 260 0 08 Jun 2020
BERT Loses Patience: Fast and Robust Inference with Early Exit Wangchunshu Zhou Canwen Xu Tao Ge Julian McAuley Ke Xu Furu Wei 56 343 0 07 Jun 2020
Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models Mitch Hill Jonathan Mitchell Song-Chun Zhu AAML 72 71 0 27 May 2020
Neural Network Laundering: Removing Black-Box Backdoor Watermarks from Deep Neural Networks William Aiken Hyoungshick Kim Simon S. Woo 40 64 0 22 Apr 2020
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization Saehyung Lee Hyungyu Lee Sungroh Yoon AAML 234 117 0 05 Mar 2020
Overfitting in adversarially robust deep learning Leslie Rice Eric Wong Zico Kolter 121 809 0 26 Feb 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 58 404 0 26 Feb 2020
Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference Ting-Kuei Hu Tianlong Chen Haotao Wang Zhangyang Wang OOD AAML 3DH 81 84 0 24 Feb 2020
Adversarial Distributional Training for Robust Deep Learning Yinpeng Dong Zhijie Deng Tianyu Pang Hang Su Jun Zhu OOD 79 123 0 14 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 96 314 0 14 Feb 2020
A Simple Framework for Contrastive Learning of Visual Representations Ting-Li Chen Simon Kornblith Mohammad Norouzi Geoffrey E. Hinton SSL 390 18,897 0 13 Feb 2020
Learning to Detect Malicious Clients for Robust Federated Learning Suyi Li Yong Cheng Wei Wang Yang Liu Tianjian Chen AAML FedML 112 226 0 01 Feb 2020
When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks Minghao Guo Yuzhe Yang Rui Xu Ziwei Liu Dahua Lin AAML OOD 98 158 0 25 Nov 2019
Can You Really Backdoor Federated Learning? Ziteng Sun Peter Kairouz A. Suresh H. B. McMahan FedML 85 579 0 18 Nov 2019
Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy Min Du R. Jia Basel Alomair AAML 80 177 0 16 Nov 2019
A New Defense Against Adversarial Images: Turning a Weakness into a Strength Tao Yu Shengyuan Hu Chuan Guo Wei-Lun Chao Kilian Q. Weinberger AAML 116 103 0 16 Oct 2019
Defending Neural Backdoors via Generative Distribution Modeling Ximing Qiao Yukun Yang H. Li AAML 62 183 0 10 Oct 2019
Adversarial Robustness Against the Union of Multiple Perturbation Models Pratyush Maini Eric Wong J. Zico Kolter OOD AAML 47 151 0 09 Sep 2019
Stateful Detection of Black-Box Adversarial Attacks Steven Chen Nicholas Carlini D. Wagner AAML MLAU 62 126 0 12 Jul 2019
ML-LOO: Detecting Adversarial Examples with Feature Attribution Puyudi Yang Jianbo Chen Cho-Jui Hsieh Jane-ling Wang Michael I. Jordan AAML 68 101 0 08 Jun 2019
Unlabeled Data Improves Adversarial Robustness Y. Carmon Aditi Raghunathan Ludwig Schmidt Percy Liang John C. Duchi 130 754 0 31 May 2019
Are Labels Required for Improving Adversarial Robustness? J. Uesato Jean-Baptiste Alayrac Po-Sen Huang Robert Stanforth Alhussein Fawzi Pushmeet Kohli AAML 85 335 0 31 May 2019
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle Dinghuai Zhang Tianyuan Zhang Yiping Lu Zhanxing Zhu Bin Dong AAML 124 362 0 02 May 2019
Adversarial Training and Robustness for Multiple Perturbations Florian Tramèr Dan Boneh AAML SILM 91 380 0 30 Apr 2019
Adversarial Training for Free! Ali Shafahi Mahyar Najibi Amin Ghiasi Zheng Xu John P. Dickerson Christoph Studer L. Davis Gavin Taylor Tom Goldstein AAML 136 1,253 0 29 Apr 2019
Image Super-Resolution as a Defense Against Adversarial Attacks Aamir Mustafa Salman H. Khan Munawar Hayat Jianbing Shen Ling Shao AAML SupR 80 176 0 07 Jan 2019
ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples Xiaojun Jia Xingxing Wei Xiaochun Cao H. Foroosh AAML 121 270 0 30 Nov 2018
Defense Against Adversarial Attacks with Saak Transform Sibo Song Yueru Chen Ngai-Man Cheung C.-C. Jay Kuo 60 24 0 06 Aug 2018
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models Pouya Samangouei Maya Kabkab Rama Chellappa AAML GAN 86 1,179 0 17 May 2018
Curriculum Adversarial Training Qi-Zhi Cai Min Du Chang-rui Liu Basel Alomair AAML 80 165 0 13 May 2018
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples Zihao Liu Qi Liu Tao Liu Nuo Xu Xue Lin Yanzhi Wang Wujie Wen AAML MQ 69 263 0 14 Mar 2018
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning Nicolas Papernot Patrick McDaniel OOD AAML 154 508 0 13 Mar 2018
Divide, Denoise, and Defend against Adversarial Attacks Seyed-Mohsen Moosavi-Dezfooli A. Shrivastava Oncel Tuzel AAML 55 45 0 19 Feb 2018
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality Xingjun Ma Yue Liu Yisen Wang S. Erfani S. Wijewickrema Grant Schoenebeck Basel Alomair Michael E. Houle James Bailey AAML 114 742 0 08 Jan 2018
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser Fangzhou Liao Ming Liang Yinpeng Dong Tianyu Pang Xiaolin Hu Jun Zhu 87 888 0 08 Dec 2017
Countering Adversarial Images using Input Transformations Chuan Guo Mayank Rana Moustapha Cissé Laurens van der Maaten AAML 128 1,406 0 31 Oct 2017
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain Tianyu Gu Brendan Dolan-Gavitt S. Garg SILM 130 1,782 0 22 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 319 12,151 0 19 Jun 2017
Multimodal Machine Learning: A Survey and Taxonomy T. Baltrušaitis Chaitanya Ahuja Louis-Philippe Morency 119 2,939 0 26 May 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 177 2,729 0 19 May 2017
Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression Nilaksh Das Madhuri Shanbhogue Shang-Tse Chen Fred Hohman Li-Wei Chen Michael E. Kounavis Duen Horng Chau AAML 72 307 0 08 May 2017
Detecting Adversarial Samples from Artifacts Reuben Feinman Ryan R. Curtin S. Shintre Andrew B. Gardner AAML 105 894 0 01 Mar 2017
On the (Statistical) Detection of Adversarial Examples Kathrin Grosse Praveen Manoharan Nicolas Papernot Michael Backes Patrick McDaniel AAML 86 714 0 21 Feb 2017