Certifiably Adversarially Robust Detection of Out-of-Distribution Data

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Certifiably Adversarially Robust Detection of Out-of-Distribution Data

16 July 2020

Julian Bitterwolf

Alexander Meinke

Matthias Hein

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Papers citing "Certifiably Adversarially Robust Detection of Out-of-Distribution Data"

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Title
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks Francesco Croce Matthias Hein AAML 227 1,858 0 03 Mar 2020
Towards neural networks that provably know when they don't know Alexander Meinke Matthias Hein OODD 77 141 0 26 Sep 2019
Sparse and Imperceivable Adversarial Attacks Francesco Croce Matthias Hein AAML 97 199 0 11 Sep 2019
Towards Stable and Efficient Training of Verifiably Robust Neural Networks Huan Zhang Hongge Chen Chaowei Xiao Sven Gowal Robert Stanforth Yue Liu Duane S. Boning Cho-Jui Hsieh AAML 80 349 0 14 Jun 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 158 2,051 0 08 Feb 2019
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem Matthias Hein Maksym Andriushchenko Julian Bitterwolf OODD 170 559 0 13 Dec 2018
Deep Anomaly Detection with Outlier Exposure Dan Hendrycks Mantas Mazeika Thomas G. Dietterich OODD 183 1,487 0 11 Dec 2018
Generalisation in humans and deep neural networks Robert Geirhos Carlos R. Medina Temme Jonas Rauber Heiko H. Schutt Matthias Bethge Felix Wichmann OOD 107 609 0 27 Aug 2018
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks Kimin Lee Kibok Lee Honglak Lee Jinwoo Shin OODD 192 2,062 0 10 Jul 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 243 3,194 0 01 Feb 2018
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples Kimin Lee Honglak Lee Kibok Lee Jinwoo Shin OODD 120 882 0 26 Nov 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 285 8,920 0 25 Aug 2017
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 160 2,159 0 21 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 315 12,131 0 19 Jun 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 131 1,864 0 20 May 2017
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks Dan Hendrycks Kevin Gimpel UQCV 168 3,472 0 07 Oct 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 268 8,583 0 16 Aug 2016
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images Anh Totti Nguyen J. Yosinski Jeff Clune AAML 171 3,274 0 05 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 282 14,963 1 21 Dec 2013