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Despite their impressive performance on diverse tasks, neural networks fail catastrophically in the presence of adversarial inputs---imperceptibly but adversarially perturbed versions of natural inputs.
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J. Löfberg · 2004
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Introduction to the non-asymptotic analysis of random matrices
R. Vershynin · 2010
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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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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Hidden voice commands
N. Carlini, P. Mishra, T. Vaidya, Y. Zhang, M. Sherr, C. Shields, D. Wagner, and W. Zhou · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami · 2016
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Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
M. Sharif, S. Bhagavatula, L. Bauer, and M. K. Reiter · 2016
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A. A. Ahmadi and A. Majumdar · 2017
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Synthesizing robust adversarial examples
A. Athalye and I. Sutskever · 2017
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T. B. Brown, D. Mané, A. Roy, M. Abadi, and J. Gilmer · 2017
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Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
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Ground-truth adversarial examples
N. Carlini, G. Katz, C. Barrett, and D. L. Dill · 2017
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Formal verification of piece-wise linear feed-forward neural networks
R. Ehlers · 2017
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Robust physical-world attacks on machine learning models
I. Evtimov, K. Eykholt, E. Fernandes, T. Kohno, B. Li, A. Prakash, A. Rahmati, and D. Song · 2017
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Formal guarantees on the robustness of a classifier against adversarial manipulation
M. Hein and M. Andriushchenko · 2017
No need to worry about adversarial examples in object detection in autonomous vehicles
J. Lu, H. Sibai, E. Fabry, and D. Forsyth · 2017
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Verifying neural networks with mixed integer programming
V. Tjeng and R. Tedrake · 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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Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2018
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Certified defenses against adversarial examples
A. Raghunathan, J. Steinhardt, and P. Liang · 2018
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Adversarial attacks on neural network policies
S. Huang, N. Papernot, I. Goodfellow, Y. Duan, and P. Abbeel · 2017
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Adversarial examples for evaluating reading comprehension systems
R. Jia and P. Liang · 2017
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J. Z. Kolter and E. Wong · 2017
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Training verified learners with learned verifiers
K. Dvijotham, S. Gowal, R. Stanforth, R. Arandjelovic, B. O’Donoghue, J. Uesato, and P. Kohli
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A dual approach to scalable verification of deep networks
K. Dvijotham, R. Stanforth, S. Gowal, T. Mann, and P. Kohli
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Reluplex: An efficient SMT solver for verifying deep neural networks
G. Katz, C. Barrett, D. Dill, K. Julian, and M. Kochenderfer
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Towards fast computation of certified robustness for relu networks
T. Weng, H. Zhang, H. Chen, Z. Song, C. Hsieh, D. Boning, I. S. Dhillon, and L. Daniel · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
E. Wong and J. Z. Kolter · 2018
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Scaling provable adversarial defenses
E. Wong, F. Schmidt, J. H. Metzen, and J. Z. Kolter · 2018
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