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Recent studies indicate that current adversarial attack methods are flawed and easy to fail when encountering some deliberately designed defense.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergery Ioffe, Jon Shlens, and Wojna Zbigniew · 2016
Earlier work this paper cites.
Regularizing neural networks by penalizing confident output distributions, 2017
Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, and Geoffrey Hinton · 2017
Earlier work this paper cites.
Towards better decoding and language model integration in sequence to sequence models
Jan Chorowski and Navdeep Jaitly · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Earlier work this paper cites.
Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018
Earlier work this paper cites.
Logit pairing methods can fool gradient-based attacks
Marius Mosbach, Maksym Andriushchenko, Thomas Trost, Matthias Hein, and Dietrich Klakow · 2018
Cited alongside, same era.
Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
Cited alongside, same era.
Gpipe: Efficient training of giant neural networks using pipeline parallelism
Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V Le, Yonghui Wu, et al · 2019
Cited alongside, same era.
When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton · 2019
Cited alongside, same era.
Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks
Francesco Croce, Jonas Rauber, and Matthias Hein · 2019
Cited alongside, same era.
Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C Duchi, and Percy S Liang · 2019
Later among the works it cites.
Robustness to adversarial perturbations in learning from incomplete data
Amir Najafi, Shin-ichi Maeda, Masanori Koyama, and Takeru Miyato · 2019
Later among the works it cites.
Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 2019
Later among the works it cites.
An alternative surrogate loss for pgd-based adversarial testing
Sven Gowal, Jonathan Uesato, Chongli Qin, Po-Sen Huang, Timothy Mann, and Pushmeet Kohli · 2019
Later among the works it cites.
Diversity can be transferred: Output diversification for white- and black-box attacks
Yusuke Tashiro, Yang Song, and Stefano Ermon · 2020
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Preetum Nakkiran · 2019
Cited alongside, same era.
Are labels required for improving adversarial robustness?
Jean-Baptiste Alayrac, Jonathan Uesato, Po-Sen Huang, Alhussein Fawzi, Robert Stanforth, and Pushmeet Kohli · 2019
Cited alongside, same era.
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce and Matthias Hein · 2020
Closest in time.
Imbalanced gradients: A new cause of overestimated adversarial robustness
Linxi Jiang, Xingjun Ma, Zejia Weng, James Bailey, and Yu-Gang Jiang · 2020
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