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MagNet and "Efficient Defenses..." were recently proposed as a defense to adversarial examples.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
Earlier work this paper cites.
Delving into transferable adversarial examples and black-box attacks
Y. Liu, X. Chen, C. Liu, and D. Song · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2017
Cited alongside, same era.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio
Cited in the paper.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy
Cited in the paper.
MagNet: a two-pronged defense against adversarial examples
D. Meng and H. Chen · 2017
Closest in time.
APE-GAN: Adversarial Perturbation Elimination with GAN
S. Shen, G. Jin, K. Gao, and Y. Zhang · 2017
Closest in time.
Efficient defenses against adversarial attacks
V. Zantedeschi, M.-I. Nicolae, and A. Rawat · 2017
Closest in time.
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