Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output Codes

4 March 2023

Papers citing "Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output Codes"

17 / 17 papers shown

Title
Recent Advances in Adversarial Training for Adversarial Robustness Tao Bai Jinqi Luo Jun Zhao Bihan Wen Qian Wang AAML 128 493 0 02 Feb 2021
Integer Programming-based Error-Correcting Output Code Design for Robust Classification Samarth Gupta Saurabh Amin 21 4 0 30 Oct 2020
Batch Normalization Increases Adversarial Vulnerability and Decreases Adversarial Transferability: A Non-Robust Feature Perspective Philipp Benz Chaoning Zhang In So Kweon AAML 51 41 0 07 Oct 2020
Deep Convolutional Neural Network Ensembles using ECOC Sara Atito Ali Ahmed Cemre Zor Berrin Yanikoglu Muhammad Awais J. Kittler 20 6 0 07 Sep 2020
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks Sanchari Sen Balaraman Ravindran A. Raghunathan FedML AAML 47 63 0 21 Apr 2020
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel Aleksander Madry AAML 277 834 0 19 Feb 2020
Error-Correcting Output Codes with Ensemble Diversity for Robust Learning in Neural Networks Yang Song Qiyu Kang Wee Peng Tay AAML 48 21 0 30 Nov 2019
On Evaluating Adversarial Robustness Nicholas Carlini Anish Athalye Nicolas Papernot Wieland Brendel Jonas Rauber Dimitris Tsipras Ian Goodfellow Aleksander Madry Alexey Kurakin ELM AAML 89 901 0 18 Feb 2019
Improving Adversarial Robustness via Promoting Ensemble Diversity Tianyu Pang Kun Xu Chao Du Ning Chen Jun Zhu AAML 78 439 0 25 Jan 2019
Theoretically Principled Trade-off between Robustness and Accuracy Hongyang R. Zhang Yaodong Yu Jiantao Jiao Eric Xing L. Ghaoui Michael I. Jordan 140 2,551 0 24 Jan 2019
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 230 3,186 0 01 Feb 2018
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 310 12,069 0 19 Jun 2017
Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong Warren He James Wei Xinyun Chen Nicholas Carlini Basel Alomair AAML 80 242 0 15 Jun 2017
Robustness to Adversarial Examples through an Ensemble of Specialists Mahdieh Abbasi Christian Gagné AAML 79 109 0 22 Feb 2017
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 266 8,555 0 16 Aug 2016
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 277 19,066 0 20 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 277 14,927 1 21 Dec 2013