Unity is strength: Improving the Detection of Adversarial Examples with Ensemble Approaches

24 November 2021

Papers citing "Unity is strength: Improving the Detection of Adversarial Examples with Ensemble Approaches"

21 / 21 papers shown

Title
Adversarial Detection by Approximation of Ensemble Boundary T. Windeatt AAML 109 0 0 18 Nov 2022
Why is the Mahalanobis Distance Effective for Anomaly Detection? Ryo Kamoi Kei Kobayashi OODD 154 58 0 01 Mar 2020
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel Aleksander Madry AAML 274 834 0 19 Feb 2020
Deep Neural Rejection against Adversarial Examples Angelo Sotgiu Ambra Demontis Marco Melis Battista Biggio Giorgio Fumera Xiaoyi Feng Fabio Roli AAML 54 69 0 01 Oct 2019
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition Yao Qin Nicholas Carlini Ian Goodfellow G. Cottrell Colin Raffel AAML 69 379 0 22 Mar 2019
BOHB: Robust and Efficient Hyperparameter Optimization at Scale Stefan Falkner Aaron Klein Frank Hutter BDL 203 1,096 0 04 Jul 2018
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning Nicolas Papernot Patrick McDaniel OOD AAML 149 508 0 13 Mar 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
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 121 1,857 0 20 May 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 198 3,873 0 10 Apr 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 188 5,989 0 04 Mar 2017
Detecting Adversarial Samples from Artifacts Reuben Feinman Ryan R. Curtin S. Shintre Andrew B. Gardner AAML 93 893 0 01 Mar 2017
On the (Statistical) Detection of Adversarial Examples Kathrin Grosse Praveen Manoharan Nicolas Papernot Michael Backes Patrick McDaniel AAML 76 713 0 21 Feb 2017
On Detecting Adversarial Perturbations J. H. Metzen Tim Genewein Volker Fischer Bastian Bischoff AAML 61 950 0 14 Feb 2017
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 261 8,552 0 16 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 540 5,897 0 08 Jul 2016
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 151 4,895 0 14 Nov 2015
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 248 4,667 0 21 Dec 2014
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
Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek Hugo Larochelle Ryan P. Adams 353 7,936 0 13 Jun 2012