Delving into the pixels of adversarial samples

21 June 2021

Papers citing "Delving into the pixels of adversarial samples"

19 / 19 papers shown

Title
Backpropagating Linearly Improves Transferability of Adversarial Examples Yiwen Guo Qizhang Li Hao Chen FedML AAML 75 116 0 07 Dec 2020
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability Nathan Inkawhich Kevin J. Liang Binghui Wang Matthew J. Inkawhich Lawrence Carin Yiran Chen AAML 62 90 0 29 Apr 2020
Transferable Perturbations of Deep Feature Distributions Nathan Inkawhich Kevin J. Liang Lawrence Carin Yiran Chen AAML 63 87 0 27 Apr 2020
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel Aleksander Madry AAML 285 839 0 19 Feb 2020
Enhancing Adversarial Example Transferability with an Intermediate Level Attack Qian Huang Isay Katsman Horace He Zeqi Gu Serge J. Belongie Ser-Nam Lim SILM AAML 94 246 0 23 Jul 2019
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
On Evaluating Adversarial Robustness Nicholas Carlini Anish Athalye Nicolas Papernot Wieland Brendel Jonas Rauber Dimitris Tsipras Ian Goodfellow Aleksander Madry Alexey Kurakin ELM AAML 109 905 0 18 Feb 2019
Adversarial Robustness Toolbox v1.0.0 Maria-Irina Nicolae M. Sinn Minh-Ngoc Tran Beat Buesser Ambrish Rawat ... Nathalie Baracaldo Bryant Chen Heiko Ludwig Ian Molloy Ben Edwards AAML VLM 91 460 0 03 Jul 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 249 3,195 0 01 Feb 2018
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models Pin-Yu Chen Huan Zhang Yash Sharma Jinfeng Yi Cho-Jui Hsieh AAML 106 1,887 0 14 Aug 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
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 131 1,867 0 20 May 2017
The Space of Transferable Adversarial Examples Florian Tramèr Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML SILM 102 558 0 11 Apr 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 477 3,148 0 04 Nov 2016
Xception: Deep Learning with Depthwise Separable Convolutions François Chollet MDE BDL PINN 1.5K 14,618 0 07 Oct 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 282 8,587 0 16 Aug 2016
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning Christian Szegedy Sergey Ioffe Vincent Vanhoucke Alexander A. Alemi 382 14,268 0 23 Feb 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
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