$n$ -ML: Mitigating Adversarial Examples via Ensembles of Topologically Manipulated Classifiers

19 December 2019

Papers citing "$n$-ML: Mitigating Adversarial Examples via Ensembles of Topologically Manipulated Classifiers"

31 / 31 papers shown

Title
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Tianlin Li Siyue Wang Pin-Yu Chen Yanzhi Wang Brian Kulis Xue Lin S. Chin AAML 54 42 0 20 Aug 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 119 1,242 0 29 Apr 2019
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition Yao Qin Nicholas Carlini Ian Goodfellow G. Cottrell Colin Raffel AAML 66 379 0 22 Mar 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 122 2,028 0 08 Feb 2019
Improving Adversarial Robustness of Ensembles with Diversity Training Sanjay Kariyappa Moinuddin K. Qureshi AAML FedML 40 134 0 28 Jan 2019
Improving Adversarial Robustness via Promoting Ensemble Diversity Tianyu Pang Kun Xu Chao Du Ning Chen Jun Zhu AAML 60 437 0 25 Jan 2019
A Multiversion Programming Inspired Approach to Detecting Audio Adversarial Examples Qiang Zeng Jianhai Su Chenglong Fu Golam Kayas Lannan Luo AAML 37 46 0 26 Dec 2018
Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks T. Brunner Frederik Diehl Michael Truong-Le Alois Knoll MLAU AAML 57 116 0 24 Dec 2018
Feature Denoising for Improving Adversarial Robustness Cihang Xie Yuxin Wu Laurens van der Maaten Alan Yuille Kaiming He 102 908 0 09 Dec 2018
Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding Lea Schonherr Katharina Kohls Steffen Zeiler Thorsten Holz D. Kolossa AAML 61 288 0 16 Aug 2018
Robustifying Models Against Adversarial Attacks by Langevin Dynamics Vignesh Srinivasan Arturo Marbán K. Müller Wojciech Samek Shinichi Nakajima AAML 57 9 0 30 May 2018
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models Pouya Samangouei Maya Kabkab Rama Chellappa AAML GAN 82 1,176 0 17 May 2018
Improving Transferability of Adversarial Examples with Input Diversity Cihang Xie Zhishuai Zhang Yuyin Zhou Song Bai Jianyu Wang Zhou Ren Alan Yuille AAML 91 1,116 0 19 Mar 2018
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning Nicolas Papernot Patrick McDaniel OOD AAML 125 506 0 13 Mar 2018
On the Suitability of $L_p$ -norms for Creating and Preventing Adversarial Examples Mahmood Sharif Lujo Bauer Michael K. Reiter AAML 125 138 0 27 Feb 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 180 3,180 0 01 Feb 2018
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser Fangzhou Liao Ming Liang Yinpeng Dong Tianyu Pang Xiaolin Hu Jun Zhu 81 883 0 08 Dec 2017
Towards Robust Neural Networks via Random Self-ensemble Xuanqing Liu Minhao Cheng Huan Zhang Cho-Jui Hsieh FedML AAML 88 418 0 02 Dec 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 113 2,145 0 21 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 255 12,029 0 19 Jun 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 115 1,854 0 20 May 2017
Adversarial Transformation Networks: Learning to Generate Adversarial Examples S. Baluja Ian S. Fischer GAN 75 284 0 28 Mar 2017
Detecting Adversarial Samples from Artifacts Reuben Feinman Ryan R. Curtin S. Shintre Andrew B. Gardner AAML 90 892 0 01 Mar 2017
On the (Statistical) Detection of Adversarial Examples Kathrin Grosse Praveen Manoharan Nicolas Papernot Michael Backes Patrick McDaniel AAML 73 712 0 21 Feb 2017
On Detecting Adversarial Perturbations J. H. Metzen Tim Genewein Volker Fischer Bastian Bischoff AAML 59 949 0 14 Feb 2017
Delving into Transferable Adversarial Examples and Black-box Attacks Yanpei Liu Xinyun Chen Chang-rui Liu D. Song AAML 133 1,731 0 08 Nov 2016
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 458 3,137 0 04 Nov 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 212 8,533 0 16 Aug 2016
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 214 4,665 0 21 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 227 14,893 1 21 Dec 2013
ADADELTA: An Adaptive Learning Rate Method Matthew D. Zeiler ODL 132 6,623 0 22 Dec 2012

nnn-ML: Mitigating Adversarial Examples via Ensembles of Topologically Manipulated Classifiers

Papers citing "$n$-ML: Mitigating Adversarial Examples via Ensembles of Topologically Manipulated Classifiers"

$n$ -ML: Mitigating Adversarial Examples via Ensembles of Topologically Manipulated Classifiers