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State-of-the-art adversarial attacks on neural networks use expensive iterative methods and numerous random restarts from different initial points.
“Imagenet: A large-scale hierarchical image database,”
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei, · 2009
Earlier work this paper cites.
“Intriguing properties of neural networks,”
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus, · 2013
Earlier work this paper cites.
“Towards deep neural network architectures robust to adversarial examples,”
Shixiang Gu and Luca Rigazio, · 2014
Earlier work this paper cites.
“Explaining and harnessing adversarial examples,”
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy, · 2014
Earlier work this paper cites.
“Adam: A method for stochastic optimization,”
Diederik P Kingma and Jimmy Ba, · 2014
Earlier work this paper cites.
Sergey Zagoruyko and Nikos Komodakis, · 2016
Earlier work this paper cites.
“Certifying some distributional robustness with principled adversarial training,”
Aman Sinha, Hongseok Namkoong, and John Duchi, · 2017
Earlier work this paper cites.
“Universal adversarial perturbations,”
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard, · 2017
Cited alongside, same era.
“Synthesizing robust adversarial examples,”
Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok, · 2017
Cited alongside, same era.
“Decision-based adversarial attacks: Reliable attacks against black-box machine learning models,”
Wieland Brendel, Jonas Rauber, and Matthias Bethge, · 2017
Cited alongside, same era.
“Darts: Deceiving autonomous cars with toxic signs,”
Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Mung Chiang, and Prateek Mittal, · 2018
Cited alongside, same era.
“Adversarial attacks against medical deep learning systems,”
“signsgd: Compressed optimisation for non-convex problems,”
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, and Anima Anandkumar, · 2018
Later among the works it cites.
“Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients,”
Andrew Slavin Ross and Finale Doshi-Velez, · 2018
Later among the works it cites.
“Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,”
Anish Athalye, Nicholas Carlini, and David Wagner, · 2018
Later among the works it cites.
“Distributionally adversarial attack,”
Tianhang Zheng, Changyou Chen, and Kui Ren, · 2019
Closest in time.
“Adversarial robustness through local linearization,”
Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli, et al., · 2019
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Samuel G Finlayson, Hyung Won Chung, Isaac S Kohane, and Andrew L Beam, · 2018
Cited alongside, same era.
“Towards deep learning models resistant to adversarial attacks,”
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu, · 2018
Cited alongside, same era.
Anish Athalye, Nicholas Carlini, and David Wagner, · 2018
Cited alongside, same era.
Closest in time.
“cifar10 challenge,” https://github.com/MadryLab/cifar10_challenge , 2019
Aleksander Madry, · 2019
Closest in time.