FACM: Intermediate Layer Still Retain Effective Features against Adversarial Examples

2 June 2022

Papers citing "FACM: Intermediate Layer Still Retain Effective Features against Adversarial Examples"

39 / 39 papers shown

Title
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking Chang-Shu Liu Yinpeng Dong Wenzhao Xiang Xiaohu Yang Hang Su Junyi Zhu YueFeng Chen Yuan He H. Xue Shibao Zheng OOD VLM AAML 72 79 0 28 Feb 2023
PRIME: A few primitives can boost robustness to common corruptions Apostolos Modas Rahul Rade Guillermo Ortiz-Jiménez Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 48 44 0 27 Dec 2021
Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks Qiyu Kang Yang Song Qinxu Ding Wee Peng Tay AAML 56 93 0 25 Oct 2021
Improving Robustness using Generated Data Sven Gowal Sylvestre-Alvise Rebuffi Olivia Wiles Florian Stimberg D. A. Calian Timothy A. Mann 68 300 0 18 Oct 2021
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks Hanxun Huang Yisen Wang S. Erfani Quanquan Gu James Bailey Xingjun Ma AAML TPM 86 102 0 07 Oct 2021
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? Vikash Sehwag Saeed Mahloujifar Tinashe Handina Sihui Dai Chong Xiang M. Chiang Prateek Mittal OOD 76 130 0 19 Apr 2021
Improving Adversarial Robustness via Channel-wise Activation Suppressing Yang Bai Yuyuan Zeng Yong Jiang Shutao Xia Xingjun Ma Yisen Wang AAML 64 131 0 11 Mar 2021
Fixing Data Augmentation to Improve Adversarial Robustness Sylvestre-Alvise Rebuffi Sven Gowal D. A. Calian Florian Stimberg Olivia Wiles Timothy A. Mann AAML 79 272 0 02 Mar 2021
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 316 702 0 19 Oct 2020
To be Robust or to be Fair: Towards Fairness in Adversarial Training Han Xu Xiaorui Liu Yaxin Li Anil K. Jain Jiliang Tang 43 180 0 13 Oct 2020
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples Sven Gowal Chongli Qin J. Uesato Timothy A. Mann Pushmeet Kohli AAML 49 331 0 07 Oct 2020
Bag of Tricks for Adversarial Training Tianyu Pang Xiao Yang Yinpeng Dong Hang Su Jun Zhu AAML 66 269 0 01 Oct 2020
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization Dan Hendrycks Steven Basart Norman Mu Saurav Kadavath Frank Wang ... Samyak Parajuli Mike Guo D. Song Jacob Steinhardt Justin Gilmer OOD 328 1,734 0 29 Jun 2020
Adversarial Robustness on In- and Out-Distribution Improves Explainability Maximilian Augustin Alexander Meinke Matthias Hein OOD 154 102 0 20 Mar 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks Francesco Croce Matthias Hein AAML 213 1,842 0 03 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 56 404 0 26 Feb 2020
Adversarial Detection and Correction by Matching Prediction Distributions G. Vacanti A. V. Looveren AAML 97 16 0 21 Feb 2020
Boosting Adversarial Training with Hypersphere Embedding Tianyu Pang Xiao Yang Yinpeng Dong Kun Xu Jun Zhu Hang Su AAML 68 156 0 20 Feb 2020
Fast is better than free: Revisiting adversarial training Eric Wong Leslie Rice J. Zico Kolter AAML OOD 138 1,177 0 12 Jan 2020
Efficient Adversarial Training with Transferable Adversarial Examples Haizhong Zheng Ziqi Zhang Juncheng Gu Honglak Lee A. Prakash AAML 53 108 0 27 Dec 2019
Square Attack: a query-efficient black-box adversarial attack via random search Maksym Andriushchenko Francesco Croce Nicolas Flammarion Matthias Hein AAML 81 987 0 29 Nov 2019
RandAugment: Practical automated data augmentation with a reduced search space E. D. Cubuk Barret Zoph Jonathon Shlens Quoc V. Le MQ 221 3,485 0 30 Sep 2019
Defending Against Adversarial Attacks Using Random Forests Yifan Ding Liqiang Wang Huan Zhang Jinfeng Yi Deliang Fan Boqing Gong AAML 49 14 0 16 Jun 2019
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle Dinghuai Zhang Tianyuan Zhang Yiping Lu Zhanxing Zhu Bin Dong AAML 96 361 0 02 May 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 141 2,038 0 08 Feb 2019
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models Pouya Samangouei Maya Kabkab Rama Chellappa AAML GAN 82 1,177 0 17 May 2018
Stochastic Activation Pruning for Robust Adversarial Defense Guneet Singh Dhillon Kamyar Azizzadenesheli Zachary Chase Lipton Jeremy Bernstein Jean Kossaifi Aran Khanna Anima Anandkumar AAML 73 547 0 05 Mar 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 216 3,185 0 01 Feb 2018
Towards Robust Neural Networks via Random Self-ensemble Xuanqing Liu Minhao Cheng Huan Zhang Cho-Jui Hsieh FedML AAML 88 420 0 02 Dec 2017
Mitigating Adversarial Effects Through Randomization Cihang Xie Jianyu Wang Zhishuai Zhang Zhou Ren Alan Yuille AAML 113 1,058 0 06 Nov 2017
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 276 9,760 0 25 Oct 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 301 12,063 0 19 Jun 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 177 2,725 0 19 May 2017
Delving into Transferable Adversarial Examples and Black-box Attacks Yanpei Liu Xinyun Chen Chang-rui Liu D. Song AAML 140 1,737 0 08 Nov 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 258 8,550 0 16 Aug 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 334 7,984 0 23 May 2016
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 148 4,895 0 14 Nov 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 271 19,049 0 20 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 268 14,912 1 21 Dec 2013