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This paper addresses the problem of formally verifying desirable properties of neural networks, i.e., obtaining provable guarantees that neural networks satisfy specifications relating their inputs and outputs (robustness to bounded norm adversarial perturbations, for example).
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B. Polyak · 2003
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Convex optimization , volume 1
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
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I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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Deep Learning
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V. Tjeng and R. Tedrake · 2017
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Y. Wang, S. Jha, and K. Chaudhuri · 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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Adversarial vulnerability for any classifier
A. Fawzi, H. Fawzi, and O. Fawzi · 2018
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Adversarial spheres
J. Gilmer, L. Metz, F. Faghri, S. S. Schoenholz, M. Raghu, M. Wattenberg, and I. Goodfellow · 2018
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R. Ehlers · 2017
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Formal guarantees on the robustness of a classifier against adversarial manipulation
M. Hein and M. Andriushchenko · 2017
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Safety verification of deep neural networks
X. Huang, M. Kwiatkowska, S. Wang, and M. Wu · 2017
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Reluplex: An efficient SMT solver for verifying deep neural networks
G. Katz, C. Barrett, D. L. Dill, K. Julian, and M. J. Kochenderfer · 2017
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Adversarial examples are not easily detected: Bypassing ten detection methods
N. Carlini and D. Wagner
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Provable defenses against adversarial examples via the convex outer adversarial polytope
J. Z. Kolter and E. Wong · 2018
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Towards deep learning models resistant to adversarial attacks
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Certified defenses against adversarial examples
A. Raghunathan, J. Steinhardt, and P. Liang · 2018
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Adversarial risk and the dangers of evaluating against weak attacks
J. Uesato, B. O’Donoghue, A. van den Oord, and P. Kohli · 2018
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