Robust Local Features for Improving the Generalization of Adversarial Training

23 September 2019

Papers citing "Robust Local Features for Improving the Generalization of Adversarial Training"

24 / 24 papers shown

Title
Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks Jiadong Lin Chuanbiao Song Kun He Liwei Wang John E. Hopcroft AAML 68 569 0 17 Aug 2019
Adversarially Robust Generalization Just Requires More Unlabeled Data Runtian Zhai Tianle Cai Di He Chen Dan Kun He John E. Hopcroft Liwei Wang 66 156 0 03 Jun 2019
Interpreting Adversarially Trained Convolutional Neural Networks Tianyuan Zhang Zhanxing Zhu AAML GAN FAtt 97 161 0 23 May 2019
A Direct Approach to Robust Deep Learning Using Adversarial Networks Huaxia Wang Chun-Nam Yu GAN AAML OOD 71 77 0 23 May 2019
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 89 1,839 0 06 May 2019
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks Yandong Li Lijun Li Liqiang Wang Tong Zhang Boqing Gong AAML 54 245 0 01 May 2019
On the Sensitivity of Adversarial Robustness to Input Data Distributions G. Ding Kry Yik-Chau Lui Xiaomeng Jin Luyu Wang Ruitong Huang OOD 52 60 0 22 Feb 2019
Theoretically Principled Trade-off between Robustness and Accuracy Hongyang R. Zhang Yaodong Yu Jiantao Jiao Eric Xing L. Ghaoui Michael I. Jordan 132 2,549 0 24 Jan 2019
The Limitations of Adversarial Training and the Blind-Spot Attack Huan Zhang Hongge Chen Zhao Song Duane S. Boning Inderjit S. Dhillon Cho-Jui Hsieh AAML 53 145 0 15 Jan 2019
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness Robert Geirhos Patricia Rubisch Claudio Michaelis Matthias Bethge Felix Wichmann Wieland Brendel 100 2,668 0 29 Nov 2018
Improving the Generalization of Adversarial Training with Domain Adaptation Chuanbiao Song Kun He Liwei Wang John E. Hopcroft AAML OOD 94 131 0 01 Oct 2018
Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Kunal Talwar Aleksander Madry OOD AAML 134 790 0 30 Apr 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
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks J. Uesato Brendan O'Donoghue Aaron van den Oord Pushmeet Kohli AAML 150 604 0 15 Feb 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 219 3,185 0 01 Feb 2018
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 304 12,063 0 19 Jun 2017
SmoothGrad: removing noise by adding noise D. Smilkov Nikhil Thorat Been Kim F. Viégas Martin Wattenberg FAtt ODL 201 2,221 0 12 Jun 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 121 1,857 0 20 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 261 8,552 0 16 Aug 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 337 7,984 0 23 May 2016
Practical Black-Box Attacks against Machine Learning Nicolas Papernot Patrick McDaniel Ian Goodfellow S. Jha Z. Berkay Celik A. Swami MLAU AAML 75 3,677 0 08 Feb 2016
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 277 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 270 14,918 1 21 Dec 2013