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We study the most practical problem setup for evaluating adversarial robustness of a machine learning system with limited access: the hard-label black-box attack setting for generating adversarial examples, where limited model queries are allowed and only the decision is provided to a queried data input.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
Earlier work this paper cites.
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Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Torchvision the machine-vision package of torch
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Earlier work this paper cites.
Randomized smoothing for stochastic optimization
John C Duchi, Peter L Bartlett, and Martin J Wainwright · 2012
Earlier work this paper cites.
Explaining and harnessing adversarial examples
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Wieland Brendel, Jonas Rauber, and Matthias Bethge · 2017
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Yurii Nesterov and Vladimir Spokoiny · 2017
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Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
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Adversarial attacks on stochastic bandits
Kwang-Sung Jun, Lihong Li, Yuzhe Ma, and Jerry Zhu · 2018
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Zeroth-order stochastic variance reduction for nonconvex optimization
Sijia Liu, Bhavya Kailkhura, Pin-Yu Chen, Paishun Ting, Shiyu Chang, and Lisa Amini · 2018
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There are no bit parts for sign bits in black-box attacks
Abdullah Al-Dujaili and Una-May O’Reilly · 2019
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Genattack: Practical black-box attacks with gradient-free optimization
Moustafa Alzantot, Yash Sharma, Supriyo Chakraborty, Huan Zhang, Cho-Jui Hsieh, and Mani B Srivastava · 2019
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Hopskipjumpattack: A query-efficient decision-based attack
Jianbo Chen, Michael I. Jordan, and Martin J. Wainwright · 2019
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Guessing smart: Biased sampling for efficient black-box adversarial attacks
Thomas Brunner, Frederik Diehl, Michael Truong Le, and Alois Knoll · 2018
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Black-box adversarial attacks with limited queries and information
Andrew Ilyas, Logan Engstrom, Anish Athalye, and Jessy Lin
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Prior convictions: Black-box adversarial attacks with bandits and priors
Andrew Ilyas, Logan Engstrom, and Aleksander Madry
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Cifar-10 (canadian institute for advanced research)
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton
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Query-efficient hard-label black-box attack: An optimization-based approach
Minhao Cheng, Thong Le, Pin-Yu Chen, Huan Zhang, JinFeng Yi, and Cho-Jui Hsieh · 2019
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signSGD via zeroth-order oracle
Sijia Liu, Pin-Yu Chen, Xiangyi Chen, and Mingyi Hong · 2019
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Autozoom: Autoencoder-based zeroth order optimization method for attacking black-box neural networks
Chun-Chen Tu, Paishun Ting, Pin-Yu Chen, Sijia Liu, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, and Shin-Ming Cheng · 2019
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The limitations of adversarial training and the blind-spot attack
Huan Zhang, Hongge Chen, Zhao Song, Duane Boning, Inderjit S Dhillon, and Cho-Jui Hsieh · 2019
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