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Exploring Adversarial Examples and Adversarial Robustness of Convolutional Neural Networks by Mutual Information

12 July 2022

Papers citing "Exploring Adversarial Examples and Adversarial Robustness of Convolutional Neural Networks by Mutual Information"

26 / 26 papers shown

Title
A Closer Look at the Adversarial Robustness of Information Bottleneck Models I. Korshunova David Stutz Alexander A. Alemi Olivia Wiles Sven Gowal 44 3 0 12 Jul 2021
FreeLB: Enhanced Adversarial Training for Natural Language Understanding Chen Zhu Yu Cheng Zhe Gan S. Sun Tom Goldstein Jingjing Liu AAML 292 443 0 25 Sep 2019
Interpreting Adversarially Trained Convolutional Neural Networks Tianyuan Zhang Zhanxing Zhu AAML GAN FAtt 109 161 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 97 1,845 0 06 May 2019
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle Dinghuai Zhang Tianyuan Zhang Yiping Lu Zhanxing Zhu Bin Dong AAML 126 362 0 02 May 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 139 1,253 0 29 Apr 2019
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet Wieland Brendel Matthias Bethge SSL FAtt 114 561 0 20 Mar 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 143 2,676 0 29 Nov 2018
A Comprehensive Survey of Deep Learning for Image Captioning Md Zakir Hossain Ferdous Sohel M. Shiratuddin Hamid Laga VLM 3DV 117 777 0 06 Oct 2018
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 112 1,785 0 30 May 2018
Learning Representations for Neural Network-Based Classification Using the Information Bottleneck Principle Rana Ali Amjad Bernhard C. Geiger 113 197 0 27 Feb 2018
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 163 2,160 0 21 Aug 2017
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models Pin-Yu Chen Huan Zhang Yash Sharma Jinfeng Yi Cho-Jui Hsieh AAML 106 1,887 0 14 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 321 12,151 0 19 Jun 2017
Estimating Mixture Entropy with Pairwise Distances Artemy Kolchinsky Brendan D. Tracey OT 54 130 0 08 Jun 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 185 2,731 0 19 May 2017
Neural Style Transfer: A Review Yongcheng Jing Yezhou Yang Zunlei Feng Jingwen Ye Yizhou Yu Xiuming Zhang 128 740 0 11 May 2017
Delving into Transferable Adversarial Examples and Black-box Attacks Yanpei Liu Xinyun Chen Chang-rui Liu Basel Alomair AAML 147 1,741 0 08 Nov 2016
Universal adversarial perturbations Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi Omar Fawzi P. Frossard AAML 164 2,534 0 26 Oct 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 282 8,593 0 16 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 551 5,914 0 08 Jul 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 362 8,005 0 23 May 2016
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 160 4,910 0 14 Nov 2015
Deep Learning and the Information Bottleneck Principle Naftali Tishby Noga Zaslavsky DRL 220 1,595 0 09 Mar 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 282 19,145 0 20 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 293 14,978 1 21 Dec 2013