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We study the problem of attacking a machine learning model in the hard-label black-box setting, where no model information is revealed except that the attacker can make queries to probe the corresponding hard-label decisions.
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Alex Krizhevsky · 2009
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Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Yurii Nesterov · 2011
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Intriguing properties of neural networks
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Stochastic first-and zeroth-order methods for nonconvex stochastic programming
Saeed Ghadimi and Guanghui Lan · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Pierre Baldi, Peter Sadowski, and Daniel Whiteson · 2014
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Seyed Mohsen Moosavi Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Wieland Brendel, Jonas Rauber, and Matthias Bethge · 2017
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Random gradient-free minimization of convex functions
Yurii Nesterov and Vladimir Spokoiny · 2017
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Towards evaluating the robustness of neural networks
Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
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Exploring the space of black-box attacks on deep neural networks
Arjun Nitin Bhagoji, Warren He, Bo Li, and Dawn Song · 2017
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Attacking visual language grounding with adversarial examples: A case study on neural image captioning
Hongge Chen, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, and Cho-Jui Hsieh · 2018
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Seq2sick: Evaluating the robustness of sequence-to-sequence models with adversarial examples
Minhao Cheng, Jinfeng Yi, Huan Zhang, Pin-Yu Chen, and Cho-Jui Hsieh · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
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, and Cho-Jui Hsieh · 2017
Cited alongside, same era.
Query-efficient black-box adversarial examples
Andrew Ilyas, Logan Engstrom, Anish Athalye, and Jessy Lin · 2017
Cited alongside, same era.
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard
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Ead: elastic-net attacks to deep neural networks via adversarial examples
Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, and Cho-Jui Hsieh · 2018
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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, and Shin-Ming Cheng · 2018
Closest in time.