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Is current research on adversarial robustness addressing the right problem?

31 July 2022

Papers citing "Is current research on adversarial robustness addressing the right problem?"

22 / 22 papers shown

Title
MLP-Mixer: An all-MLP Architecture for Vision Ilya O. Tolstikhin N. Houlsby Alexander Kolesnikov Lucas Beyer Xiaohua Zhai ... Andreas Steiner Daniel Keysers Jakob Uszkoreit Mario Lucic Alexey Dosovitskiy 441 2,689 0 04 May 2021
Contemplating real-world object classification Ali Borji OOD 30 9 0 08 Mar 2021
Noise or Signal: The Role of Image Backgrounds in Object Recognition Kai Y. Xiao Logan Engstrom Andrew Ilyas Aleksander Madry 148 387 0 17 Jun 2020
Adversarial examples are useful too! Ali Borji SILM AAML 37 1 0 13 May 2020
Shortcut Learning in Deep Neural Networks Robert Geirhos J. Jacobsen Claudio Michaelis R. Zemel Wieland Brendel Matthias Bethge Felix Wichmann 221 2,061 0 16 Apr 2020
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 95 1,845 0 06 May 2019
Do ImageNet Classifiers Generalize to ImageNet? Benjamin Recht Rebecca Roelofs Ludwig Schmidt Vaishaal Shankar OOD SSeg VLM 124 1,728 0 13 Feb 2019
Disentangling Adversarial Robustness and Generalization David Stutz Matthias Hein Bernt Schiele AAML OOD 272 284 0 03 Dec 2018
Humans can decipher adversarial images Zhenglong Zhou C. Firestone AAML 49 122 0 11 Sep 2018
Generalisation in humans and deep neural networks Robert Geirhos Carlos R. Medina Temme Jonas Rauber Heiko H. Schutt Matthias Bethge Felix Wichmann OOD 109 610 0 27 Aug 2018
Why do deep convolutional networks generalize so poorly to small image transformations? Aharon Azulay Yair Weiss 86 562 0 30 May 2018
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 110 1,784 0 30 May 2018
On the Suitability of $L_p$ -norms for Creating and Preventing Adversarial Examples Mahmood Sharif Lujo Bauer Michael K. Reiter AAML 130 138 0 27 Feb 2018
Adversarial Patch Tom B. Brown Dandelion Mané Aurko Roy Martín Abadi Justin Gilmer AAML 91 1,097 0 27 Dec 2017
Dynamic Routing Between Capsules S. Sabour Nicholas Frosst Geoffrey E. Hinton 195 4,606 0 26 Oct 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 319 12,151 0 19 Jun 2017
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 282 8,587 0 16 Aug 2016
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 154 4,905 0 14 Nov 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 282 19,129 0 20 Dec 2014
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images Anh Totti Nguyen J. Yosinski Jeff Clune AAML 174 3,275 0 05 Dec 2014
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition C. Cadieu Ha Hong Daniel L. K. Yamins Nicolas Pinto Diego Ardila E. Solomon N. Majaj J. DiCarlo 96 787 0 12 Jun 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 291 14,968 1 21 Dec 2013