Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks

24 December 2018

Papers citing "Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks"

24 / 24 papers shown

Title
Distributed Black-box Attack: Do Not Overestimate Black-box Attacks Han-Ching Wu Sareh Rowlands Johan Wahlstrom MLAU AAML 69 0 0 28 Oct 2022
Logit Pairing Methods Can Fool Gradient-Based Attacks Marius Mosbach Maksym Andriushchenko T. A. Trost Matthias Hein Dietrich Klakow AAML 66 82 0 29 Oct 2018
Low Frequency Adversarial Perturbation Chuan Guo Jared S. Frank Kilian Q. Weinberger AAML 61 166 0 24 Sep 2018
Adversarial Vision Challenge Wieland Brendel Jonas Rauber Alexey Kurakin Nicolas Papernot Behar Veliqi M. Salathé Sharada Mohanty Matthias Bethge AAML 56 58 0 06 Aug 2018
Evaluating and Understanding the Robustness of Adversarial Logit Pairing Logan Engstrom Andrew Ilyas Anish Athalye AAML 55 141 0 26 Jul 2018
Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors Andrew Ilyas Logan Engstrom Aleksander Madry MLAU AAML 85 374 0 20 Jul 2018
Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach Minhao Cheng Thong Le Pin-Yu Chen Jinfeng Yi Huan Zhang Cho-Jui Hsieh AAML 94 346 0 12 Jul 2018
AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks Chun-Chen Tu Pai-Shun Ting Pin-Yu Chen Sijia Liu Huan Zhang Jinfeng Yi Cho-Jui Hsieh Shin-Ming Cheng MLAU AAML 73 396 0 30 May 2018
Black-box Adversarial Attacks with Limited Queries and Information Andrew Ilyas Logan Engstrom Anish Athalye Jessy Lin MLAU AAML 151 1,194 0 23 Apr 2018
Adversarial Attacks and Defences Competition Alexey Kurakin Ian Goodfellow Samy Bengio Yinpeng Dong Fangzhou Liao ... Junjiajia Long Yerkebulan Berdibekov Takuya Akiba Seiya Tokui Motoki Abe AAML SILM 86 320 0 31 Mar 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 171 3,171 0 01 Feb 2018
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models Wieland Brendel Jonas Rauber Matthias Bethge AAML 65 1,335 0 12 Dec 2017
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser Fangzhou Liao Ming Liang Yinpeng Dong Tianyu Pang Xiaolin Hu Jun Zhu 81 879 0 08 Dec 2017
Mitigating Adversarial Effects Through Randomization Cihang Xie Jianyu Wang Zhishuai Zhang Zhou Ren Alan Yuille AAML 88 1,050 0 06 Nov 2017
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 67 1,864 0 14 Aug 2017
Synthesizing Robust Adversarial Examples Anish Athalye Logan Engstrom Ilya Sutskever Kevin Kwok AAML 44 66 0 24 Jul 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 241 11,962 0 19 Jun 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 174 2,712 0 19 May 2017
The Space of Transferable Adversarial Examples Florian Tramèr Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML SILM 73 555 0 11 Apr 2017
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 183 8,513 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 322 14,196 0 23 Feb 2016
Rethinking the Inception Architecture for Computer Vision Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jonathon Shlens Z. Wojna 3DV BDL 577 27,231 0 02 Dec 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 201 18,922 0 20 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 204 14,831 1 21 Dec 2013