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Carefully Blending Adversarial Training, Purification, and Aggregation Improves Adversarial Robustness

20 February 2025

Papers citing "Carefully Blending Adversarial Training, Purification, and Aggregation Improves Adversarial Robustness"

50 / 53 papers shown

Title
Robust Principles: Architectural Design Principles for Adversarially Robust CNNsBritish Machine Vision Conference (BMVC), 2023 Sheng-Hsuan Peng Weilin Xu Cory Cornelius Matthew Hull Kevin Wenliang Li Rahul Duggal Mansi Phute Jason Martin Duen Horng Chau AAML 201 56 0 30 Aug 2023
Robust Classification via a Single Diffusion ModelInternational Conference on Machine Learning (ICML), 2023 Huanran Chen Yinpeng Dong Zhengyi Wang Xiaohu Yang Chen-Dong Duan Hang Su Jun Zhu 266 77 0 24 May 2023
Decoupled Kullback-Leibler Divergence LossNeural Information Processing Systems (NeurIPS), 2023 Jiequan Cui Zhuotao Tian Zhisheng Zhong Xiaojuan Qi Bei Yu Hanwang Zhang 201 66 0 23 May 2023
Robust Evaluation of Diffusion-Based Adversarial PurificationIEEE International Conference on Computer Vision (ICCV), 2023 M. Lee Dongwoo Kim 303 80 0 16 Mar 2023
Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat ModelsNeural Information Processing Systems (NeurIPS), 2023 Naman D. Singh Francesco Croce Matthias Hein OOD 286 91 0 03 Mar 2023
Better Diffusion Models Further Improve Adversarial TrainingInternational Conference on Machine Learning (ICML), 2023 Zekai Wang Tianyu Pang Chao Du Min Lin Weiwei Liu Shuicheng Yan DiffM 359 275 0 09 Feb 2023
Adversarial Purification with the Manifold HypothesisAAAI Conference on Artificial Intelligence (AAAI), 2022 Zhaoyuan Yang Zhiwei Xu Jing Zhang Leonid Sigal Peter Tu AAML 310 8 0 26 Oct 2022
Diffusion Models for Adversarial PurificationInternational Conference on Machine Learning (ICML), 2022 Weili Nie Brandon Guo Yujia Huang Chaowei Xiao Arash Vahdat Anima Anandkumar WIGM 430 572 0 16 May 2022
LAS-AT: Adversarial Training with Learnable Attack StrategyComputer Vision and Pattern Recognition (CVPR), 2022 Yang Liu Yong Zhang Baoyuan Wu Ke Ma Jue Wang Xiaochun Cao AAML 128 169 0 13 Mar 2022
Data Augmentation Can Improve RobustnessNeural Information Processing Systems (NeurIPS), 2021 Sylvestre-Alvise Rebuffi Sven Gowal D. A. Calian Florian Stimberg Olivia Wiles Timothy A. Mann AAML 182 353 0 09 Nov 2021
MEMO: Test Time Robustness via Adaptation and Augmentation Marvin Zhang Sergey Levine Chelsea Finn OOD TTA 460 435 0 18 Oct 2021
Improving Robustness using Generated Data Sven Gowal Sylvestre-Alvise Rebuffi Olivia Wiles Florian Stimberg D. A. Calian Timothy A. Mann 261 341 0 18 Oct 2021
Adversarial purification with Score-based generative modelsInternational Conference on Machine Learning (ICML), 2021 Jongmin Yoon Sung Ju Hwang Juho Lee DiffM 170 175 0 11 Jun 2021
A Variational Perspective on Diffusion-Based Generative Models and Score MatchingNeural Information Processing Systems (NeurIPS), 2021 Chin-Wei Huang Jae Hyun Lim Aaron Courville DiffM 236 226 0 05 Jun 2021
Online Adversarial Purification based on Self-SupervisionInternational Conference on Learning Representations (ICLR), 2021 Changhao Shi Chester Holtz Zhengchao Wan AAML 149 61 0 23 Jan 2021
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples Sven Gowal Chongli Qin J. Uesato Timothy A. Mann Pushmeet Kohli AAML 379 356 0 07 Oct 2020
Perceptual Adversarial Robustness: Defense Against Unseen Threat ModelsInternational Conference on Learning Representations (ICLR), 2020 Cassidy Laidlaw Sahil Singla Soheil Feizi AAML OOD 339 207 0 22 Jun 2020
Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based ModelsInternational Conference on Learning Representations (ICLR), 2020 Mitch Hill Jonathan Mitchell Song-Chun Zhu AAML 175 83 0 27 May 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacksInternational Conference on Machine Learning (ICML), 2020 Francesco Croce Matthias Hein AAML 584 2,136 0 03 Mar 2020
On Adaptive Attacks to Adversarial Example DefensesNeural Information Processing Systems (NeurIPS), 2020 Florian Tramèr Nicholas Carlini Wieland Brendel Aleksander Madry AAML 530 899 0 19 Feb 2020
Decision-Making with Auto-Encoding Variational BayesNeural Information Processing Systems (NeurIPS), 2020 Romain Lopez Pierre Boyeau Nir Yosef Michael I. Jordan Jeffrey Regier BDL 1.3K 19,430 0 17 Feb 2020
Robustness of Bayesian Neural Networks to Gradient-Based AttacksNeural Information Processing Systems (NeurIPS), 2020 Ginevra Carbone Matthew Wicker Luca Laurenti A. Patané Luca Bortolussi G. Sanguinetti AAML 201 82 0 11 Feb 2020
PyTorch: An Imperative Style, High-Performance Deep Learning LibraryNeural Information Processing Systems (NeurIPS), 2019 Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 932 47,917 0 03 Dec 2019
Square Attack: a query-efficient black-box adversarial attack via random searchEuropean Conference on Computer Vision (ECCV), 2019 Maksym Andriushchenko Francesco Croce Nicolas Flammarion Matthias Hein AAML 656 1,145 0 29 Nov 2019
Imperceptible Adversarial Attacks on Tabular Data Vincent Ballet X. Renard Jonathan Aigrain Thibault Laugel P. Frossard Marcin Detyniecki 240 82 0 08 Nov 2019
On the Variance of the Adaptive Learning Rate and BeyondInternational Conference on Learning Representations (ICLR), 2019 Liyuan Liu Haoming Jiang Pengcheng He Weizhu Chen Xiaodong Liu Jianfeng Gao Jiawei Han ODL 529 2,098 0 08 Aug 2019
Lookahead Optimizer: k steps forward, 1 step backNeural Information Processing Systems (NeurIPS), 2019 Michael Ruogu Zhang James Lucas Geoffrey E. Hinton Jimmy Ba ODL 381 795 0 19 Jul 2019
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary AttackInternational Conference on Machine Learning (ICML), 2019 Francesco Croce Matthias Hein AAML 401 553 0 03 Jul 2019
Adversarial Examples Are Not Bugs, They Are FeaturesNeural Information Processing Systems (NeurIPS), 2019 Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 549 1,989 0 06 May 2019
Adversarial Training and Robustness for Multiple PerturbationsNeural Information Processing Systems (NeurIPS), 2019 Florian Tramèr Dan Boneh AAML SILM 389 408 0 30 Apr 2019
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition Yao Qin Nicholas Carlini Ian Goodfellow G. Cottrell Colin Raffel AAML 207 412 0 22 Mar 2019
PuVAE: A Variational Autoencoder to Purify Adversarial ExamplesIEEE Access (IEEE Access), 2019 Uiwon Hwang Jaewoo Park Hyemi Jang Sungroh Yoon N. Cho AAML 135 90 0 02 Mar 2019
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch G. Ding Luyu Wang Xiaomeng Jin 153 195 0 20 Feb 2019
Theoretically Principled Trade-off between Robustness and Accuracy Hongyang R. Zhang Yaodong Yu Jiantao Jiao Eric Xing L. Ghaoui Sai Li 626 2,818 0 24 Jan 2019
Intrinsic Geometric Vulnerability of High-Dimensional Artificial Intelligence Luca Bortolussi G. Sanguinetti AAML 152 4 0 08 Nov 2018
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models Pouya Samangouei Maya Kabkab Rama Chellappa AAML GAN 464 1,223 0 17 May 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 1.1K 3,353 0 01 Feb 2018
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning Battista Biggio Fabio Roli AAML 285 1,510 0 08 Dec 2017
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser Fangzhou Liao Ming Liang Yinpeng Dong Tianyu Pang Xiaolin Hu Jun Zhu 376 979 0 08 Dec 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 658 9,877 0 25 Aug 2017
Evasion Attacks against Machine Learning at Test Time Battista Biggio Igino Corona Davide Maiorca B. Nelson Nedim Srndic Pavel Laskov Giorgio Giacinto Fabio Roli AAML 432 2,243 0 21 Aug 2017
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets P. Chrabaszcz I. Loshchilov Katharina Eggensperger SSeg OOD 478 710 0 27 Jul 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 1.1K 13,530 0 19 Jun 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 358 2,918 0 19 May 2017
Parseval Networks: Improving Robustness to Adversarial Examples Moustapha Cissé Piotr Bojanowski Edouard Grave Yann N. Dauphin Nicolas Usunier AAML 380 841 0 28 Apr 2017
Adversarial examples in the physical worldInternational Conference on Learning Representations (ICLR), 2016 Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 1.3K 6,378 0 08 Jul 2016
Attribute2Image: Conditional Image Generation from Visual Attributes Xinchen Yan Jimei Yang Kihyuk Sohn Honglak Lee DRL GAN 264 790 0 02 Dec 2015
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 614 5,212 0 14 Nov 2015
Cyclical Learning Rates for Training Neural NetworksIEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2015 L. Smith ODL 636 2,756 0 03 Jun 2015
Automatic differentiation in machine learning: a survey A. G. Baydin Barak A. Pearlmutter Alexey Radul J. Siskind PINN AI4CE ODL 430 3,208 0 20 Feb 2015