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Despite remarkable success in practice, modern machine learning models have been found to be susceptible to adversarial attacks that make human-imperceptible perturbations to the data, but result in serious and potentially dangerous prediction errors.
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Dimitrios Diochnos, Saeed Mahloujifar, and Mohammad Mahmoody · 2018
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Alhussein Fawzi, Hamza Fawzi, and Omar Fawzi · 2018
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Jernej Kos, Ian Fischer, and Dawn Song · 2018
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Saeed Mahloujifar, Dimitrios I Diochnos, and Mohammad Mahmoody · 2019
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Omar Montasser, Steve Hanneke, and Nathan Srebro · 2019
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Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Sok: Security and privacy in machine learning
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael P Wellman · 2018
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Adversarially robust generalization requires more data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Madry · 2018
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Are adversarial examples inevitable?
Ali Shafahi, W Ronny Huang, Christoph Studer, Soheil Feizi, and Tom Goldstein · 2018
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Understanding adversarial training: Increasing local stability of supervised models through robust optimization
Uri Shaham, Yutaro Yamada, and Sahand Negahban · 2018
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Towards fast computation of certified robustness for relu networks
Lily Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Luca Daniel, Duane Boning, and Inderjit Dhillon · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
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Amir Najafi, Shin-ichi Maeda, Masanori Koyama, and Takeru Miyato · 2019
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Adversarial robustness may be at odds with simplicity
Preetum Nakkiran · 2019
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Adversarial training can hurt generalization
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C Duchi, and Percy Liang · 2019
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Disentangling adversarial robustness and generalization
David Stutz, Matthias Hein, and Bernt Schiele · 2019
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Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2019
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Rademacher complexity for adversarially robust generalization
Dong Yin, Ramchandran Kannan, and Peter Bartlett · 2019
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Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 2019
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Lower bounds for adversarially robust pac learning
Dimitrios I Diochnos, Saeed Mahloujifar, and Mohammad Mahmoody · 2020
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Adversarially robust learning could leverage computational hardness
Sanjam Garg, Somesh Jha, Saeed Mahloujifar, and Mahmoody Mohammad · 2020
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Understanding and mitigating the tradeoff between robustness and accuracy
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John Duchi, and Percy Liang · 2020
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Confidence-calibrated adversarial training: Generalizing to unseen attacks
David Stutz, Matthias Hein, and Bernt Schiele · 2020
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Attacks which do not kill training make adversarial learning stronger
Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, and Mohan Kankanhalli · 2020
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