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Finding minimum distortion of adversarial examples and thus certifying robustness in neural network classifiers for given data points is known to be a challenging problem.
2013
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” ICLR , 2015
2015
Earlier work this paper cites.
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: a simple and accurate method to fool deep neural networks,” in IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 2574–2582
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
A. Fawzi, S.-M. Moosavi-Dezfooli, and P. Frossard, “The robustness of deep networks: A geometrical perspective,” IEEE Signal Processing Magazine , vol. 34, no. 6, pp. 50–62, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in IEEE Symposium on Security and Privacy (SP) , 2017, pp. 39–57
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical black-box attacks against machine learning,” in ACM Asia Conference on Computer and Communications Security , 2017, pp. 506–519
2017
Earlier work this paper cites.
Y. Liu, X. Chen, C. Liu, and D. Song, “Delving into transferable adversarial examples and black-box attacks,” ICLR , 2017
2017
Earlier work this paper cites.
P.-Y. Chen, H. Zhang, Y. Sharma, J. Yi, and C.-J. Hsieh, “ZOO: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models,” in ACM Workshop on Artificial Intelligence and Security , 2017, pp. 15–26
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
G. Katz, C. Barrett, D. L. Dill, K. Julian, and M. J. Kochenderfer, “Reluplex: An efficient smt solver for verifying deep neural networks,” in International Conference on Computer Aided Verification . Springer, 2017, pp. 97–117
2017
Cited alongside, same era.
J. Peck, J. Roels, B. Goossens, and Y. Saeys, “Lower bounds on the robustness to adversarial perturbations,” in NIPS , 2017
2017
Cited alongside, same era.
W. Brendel, J. Rauber, and M. Bethge, “Decision-based adversarial attacks: Reliable attacks against black-box machine learning models,” ICLR , 2018
2018
Closest in time.
A. Sinha, H. Namkoong, and J. Duchi, “Certifiable distributional robustness with principled adversarial training,” ICLR , 2018
2018
Closest in time.
J. Z. Kolter and E. Wong, “Provable defenses against adversarial examples via the convex outer adversarial polytope,” ICML , 2018
2018
Closest in time.
A. Raghunathan, J. Steinhardt, and P. Liang, “Certified defenses against adversarial examples,” ICLR , 2018
2018
Closest in time.
T.-W. Weng, H. Zhang, H. Chen, Z. Song, C.-J. Hsieh, D. Boning, I. S. Dhillon, and L. Daniel, “Towards fast computation of certified robustness for relu networks,” ICML , 2018
2018
Closest in time.
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2017
Cited alongside, same era.
C.-H. Cheng, G. Nührenberg, and H. Ruess, “Maximum resilience of artificial neural networks,” in International Symposium on Automated Technology for Verification and Analysis . Springer, 2017, pp. 251–268
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
R. Ehlers, “Formal verification of piece-wise linear feed-forward neural networks,” in International Symposium on Automated Technology for Verification and Analysis . Springer, 2017, pp. 269–286
2017
Cited alongside, same era.
M. Hein and M. Andriushchenko, “Formal guarantees on the robustness of a classifier against adversarial manipulation,” in NIPS , 2017
2017
Cited alongside, same era.
X. Cao and N. Z. Gong, “Mitigating evasion attacks to deep neural networks via region-based classification,” in ACM Annual Computer Security Applications Conference , 2017, pp. 278–287
2017
Cited alongside, same era.
T.-W. Weng, H. Zhang, P.-Y. Chen, J. Yi, D. Su, Y. Gao, C.-J. Hsieh, and L. Daniel, “Evaluating the robustness of neural networks: An extreme value theory approach,” ICLR , 2018
2018
Closest in time.
T. Gehr, M. Mirman, D. Drachsler-Cohen, P. Tsankov, S. Chaudhuri, and M. Vechev, “Ai2: Safety and robustness certification of neural networks with abstract interpretation,” in IEEE Symposium on Security and Privacy (SP) , vol. 00, 2018, pp. 948–963
2018
Closest in time.
K. Dvijotham, R. Stanforth, S. Gowal, T. Mann, and P. Kohli, “A dual approach to scalable verification of deep networks,” UAI , 2018
2018
Closest in time.
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” ICLR , 2018
2018
Closest in time.
P.-Y. Chen, Y. Sharma, H. Zhang, J. Yi, and C.-J. Hsieh, “EAD: elastic-net attacks to deep neural networks via adversarial examples,” AAAI , 2018
2018
Closest in time.