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The ability to deploy neural networks in real-world, safety-critical systems is severely limited by the presence of adversarial examples: slightly perturbed inputs that are misclassified by the network.
An abstraction-refinement approach to verification of artificial neural networks
L. Pulina and A. Tacchella · 2010
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Challenging SMT solvers to verify neural networks
L. Pulina and A. Tacchella · 2012
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
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Learning with a strong adversary
R. Huang, B. Xu, D. Schuurmans, and C. Szepesvári · 2015
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Deepfool: a simple and accurate method to fool deep neural networks
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2015
Earlier work this paper cites.
End to end learning for self-driving cars, 2016
M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba · 2016
Earlier work this paper cites.
Safety verification of deep neural networks, 2016
X. Huang, M. Kwiatkowska, S. Wang, and M. Wu · 2016
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Policy compression for aircraft collision avoidance systems
K. Julian, J. Lopez, J. Brush, M. Owen, and M. Kochenderfer · 2016
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Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
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Adversarial diversity and hard positive generation
A. Rozsa, E. M. Rudd, and T. E. Boult · 2016
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Improving the robustness of deep neural networks via stability training
S. Zheng, Y. Song, T. Leung, and I. Goodfellow · 2016
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Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
On detecting adversarial perturbations
J. Hendrik Metzen, T. Genewein, V. Fischer, and B. Bischoff · 2017
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Early methods for detecting adversarial images
D. Hendrycks and K. Gimpel · 2017
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Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
A. Athalye, N. Carlini, and D. Wagner · 2018
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Ensemble adversarial training: Attacks and defenses
F. Tramèr, A. Kurakin, N. Papernot, I. Goodfellow, D. Boneh, and P. McDaniel · 2018
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Cited alongside, same era.
Formal verification of piece-wise linear feed-forward neural networks
R. Ehlers · 2017
Cited alongside, same era.
Towards Proving the Adversarial Robustness of Deep Neural Networks
G. Katz, C. Barrett, D. Dill, K. Julian, and M. Kochenderfer
Cited in the paper.
Reluplex: An efficient SMT solver for verifying deep neural networks
G. Katz, C. Barrett, D. Dill, K. Julian, and M. Kochenderfer
Cited in the paper.
Reluplex, 2017c
G. Katz, C. Barrett, D. Dill, K. Julian, and M. Kochenderfer
Cited in the paper.
Spatially transformed adversarial examples
C. Xiao, J.-Y. Zhu, B. Li, W. He, M. Liu, and D. Song · 2018
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Generating natural adversarial examples
S. S. Zhengli Zhao, Dheeru Dua · 2018
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