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Adversarial training has been actively studied in recent computer vision research to improve the robustness of models.
The mnist database of handwritten digits
Yann LeCun · 1998
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Large-scale machine learning with stochastic gradient descent
Léon Bottou · 2010
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Nedim Srndic, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
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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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Mitigating adversarial effects through randomization
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille · 2017
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On the effectiveness of interval bound propagation for training verifiably robust models
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy Mann, and Pushmeet Kohli · 2018
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Harini Kannan, Alexey Kurakin, and Ian Goodfellow · 2018
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Defense against adversarial attacks using high-level representation guided denoiser
Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Xiaolin Hu, and Jun Zhu · 2018
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Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
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Fast and effective robustness certification
Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, and Martin Vechev · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 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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Scaling provable adversarial defenses
Eric Wong, Frank Schmidt, Jan Hendrik Metzen, and J Zico Kolter · 2018
Cited alongside, same era.
Training for faster adversarial robustness verification via inducing relu stability
Kai Y Xiao, Vincent Tjeng, Nur Muhammad Shafiullah, and Aleksander Madry · 2018
Cited alongside, same era.
Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C Duchi, and Percy S Liang · 2019
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
Jeremy M Cohen, Elan Rosenfeld, and J Zico Kolter · 2019
Cited alongside, same era.
Provable robustness of relu networks via maximization of linear regions
Francesco Croce, Maksym Andriushchenko, and Matthias Hein · 2019
Cited alongside, same era.
Adversarial examples are not bugs, they are features
Dataset meta-learning from kernel ridge-regression
Timothy Nguyen, Zhourong Chen, and Jaehoon Lee · 2020
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Secdd: Efficient and secure method for remotely training neural networks
Ilia Sucholutsky and Matthias Schonlau · 2020
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On adaptive attacks to adversarial example defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel, and Aleksander Madry · 2020
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Adversarial weight perturbation helps robust generalization
Dongxian Wu, Shu-Tao Xia, and Yisen Wang · 2020
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Automatic perturbation analysis for scalable certified robustness and beyond
Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, and Cho-Jui Hsieh · 2020
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Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
Cited alongside, same era.
Certified robustness to adversarial examples with differential privacy
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2019
Cited alongside, same era.
Certified adversarial robustness with additive noise
Bai Li, Changyou Chen, Wenlin Wang, and Lawrence Carin · 2019
Cited alongside, same era.
Adversarial defense by restricting the hidden space of deep neural networks
Aamir Mustafa, Salman Khan, Munawar Hayat, Roland Goecke, Jianbing Shen, and Ling Shao · 2019
Cited alongside, same era.
Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2019
Cited alongside, same era.
Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 2019
Cited alongside, same era.
Adversarial training and provable defenses: Bridging the gap
Mislav Balunović and Martin Vechev · 2020
Cited alongside, same era.
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Macer: Attack-free and scalable robust training via maximizing certified radius
Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, and Liwei Wang · 2020
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Black-box certification with randomized smoothing: A functional optimization based framework
Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, and Qiang Liu · 2020
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Attacks which do not kill training make adversarial learning stronger
Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Li zhen Cui, Masashi Sugiyama, and Mohan S. Kankanhalli · 2020
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Distilled one-shot federated learning
Yanlin Zhou, George Pu, Xiyao Ma, Xiaolin Li, and Dapeng Wu · 2020
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Dataset distillation with infinitely wide convolutional networks
Timothy Nguyen, Roman Novak, Lechao Xiao, and Jaehoon Lee · 2021
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Soft-label dataset distillation and text dataset distillation
Ilia Sucholutsky and Matthias Schonlau · 2021
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Towards certifying l-infinity robustness using neural networks with l-inf-dist neurons
Bohang Zhang, Tianle Cai, Zhou Lu, Di He, and Liwei Wang · 2021
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Dataset condensation with differentiable siamese augmentation
Bo Zhao and Hakan Bilen · 2021
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Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2021
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Dataset distillation by matching training trajectories
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu · 2022
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