Robust Transferable Feature Extractors: Learning to Defend Pre-Trained
Networks Against White Box Adversaries

Robust Transferable Feature Extractors: Learning to Defend Pre-Trained Networks Against White Box Adversaries

14 September 2022

Papers citing "Robust Transferable Feature Extractors: Learning to Defend Pre-Trained Networks Against White Box Adversaries"

18 / 18 papers shown

Title
Defense against Adversarial Attacks on Hybrid Speech Recognition using Joint Adversarial Fine-tuning with Denoiser Sonal Joshi Saurabh Kataria Yiwen Shao Piotr Żelasko Jesus Villalba Sanjeev Khudanpur Najim Dehak AAML 33 4 0 08 Apr 2022
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training Dawei Zhou N. Wang Xinbo Gao Bo Han Jun Yu Xiaoyu Wang Tongliang Liu AAML 56 4 0 10 Jun 2021
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 312 702 0 19 Oct 2020
Do Adversarially Robust ImageNet Models Transfer Better? Hadi Salman Andrew Ilyas Logan Engstrom Ashish Kapoor Aleksander Madry 62 424 0 16 Jul 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 439 42,393 0 03 Dec 2019
Adversarial Training Can Hurt Generalization Aditi Raghunathan Sang Michael Xie Fanny Yang John C. Duchi Percy Liang 82 242 0 14 Jun 2019
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 89 1,837 0 06 May 2019
On Evaluating Adversarial Robustness Nicholas Carlini Anish Athalye Nicolas Papernot Wieland Brendel Jonas Rauber Dimitris Tsipras Ian Goodfellow Aleksander Madry Alexey Kurakin ELM AAML 81 901 0 18 Feb 2019
Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models D. Su Huan Zhang Hongge Chen Jinfeng Yi Pin-Yu Chen Yupeng Gao VLM 112 391 0 05 Aug 2018
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 96 1,778 0 30 May 2018
On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses Anish Athalye Nicholas Carlini AAML 50 170 0 10 Apr 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 212 3,184 0 01 Feb 2018
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser Fangzhou Liao Ming Liang Yinpeng Dong Tianyu Pang Xiaolin Hu Jun Zhu 83 886 0 08 Dec 2017
Countering Adversarial Images using Input Transformations Chuan Guo Mayank Rana Moustapha Cissé Laurens van der Maaten AAML 107 1,404 0 31 Oct 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 294 12,060 0 19 Jun 2017
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size F. Iandola Song Han Matthew W. Moskewicz Khalid Ashraf W. Dally Kurt Keutzer 139 7,477 0 24 Feb 2016
U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger Philipp Fischer Thomas Brox SSeg 3DV 1.8K 77,099 0 18 May 2015
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.6K 100,330 0 04 Sep 2014

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