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The fragility of modern machine learning models has drawn a considerable amount of attention from both academia and the public.
Tight certificates of adversarial robustness for randomly smoothed classifiers
Lee, G.-H., Yuan, Y., Chang, S., and Jaakkola, T. S. (2019) · 1906
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The mnist database of handwritten digits
LeCun, Y. (1998) · 1998
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Random smoothing might be unable to certify l ∞ l_{\infty} robustness for high-dimensional images
Blum, A., Dick, T., Manoj, N., and Zhang, H. (2020) · 2002
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Curse of dimensionality on randomized smoothing for certifiable robustness
Kumar, A., Levine, A., Goldstein, T., and Feizi, S. (2020) · 2002
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Sok: Certified robustness for deep neural networks
Li, L., Qi, X., Xie, T., and Li, B. (2020) · 2009
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Evasion attacks against machine learning at test time
Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., and Roli, F. (2013) · 2013
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. (2014) · 2014
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · 2015
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Formal guarantees on the robustness of a classifier against adversarial manipulation
Hein, M. and Andriushchenko, M. (2017) · 2017
Cited alongside, same era.
Reluplex: An efficient smt solver for verifying deep neural networks
Katz, G., Barrett, C., Dill, D. L., et al. (2017) · 2017
Cited alongside, same era.
Ai2: Safety and robustness certification of neural networks with abstract interpretation
Gehr, T., Mirman, M., Drachsler-Cohen, D., Tsankov, P., Chaudhuri, S., and Vechev, M. (2018) · 2018
Cited alongside, same era.
Certified defenses against adversarial examples
Raghunathan, A., Steinhardt, J., and Liang, P. (2018) · 2018
Cited alongside, same era.
Fast and effective robustness certification
Singh, G., Gehr, T., Mirman, M., Püschel, M., and Vechev, M. (2018) · 2018
Cited alongside, same era.
Evaluating robustness of neural networks with mixed integer programming
Efficient neural network robustness certification with general activation functions
Zhang, H., Weng, T.-W., Chen, P.-Y., Hsieh, C.-J., and Daniel, L. (2018) · 2018
Later among the works it cites.
Certified adversarial robustness via randomized smoothing
Cohen, J., Rosenfeld, E., and Kolter, Z. (2019) · 2019
Later among the works it cites.
Constructing a provably adversarially-robust classifier from a high accuracy one
Głuch, G. and Urbanke, R. (2019) · 2019
Later among the works it cites.
Certified robustness for top-k predictions against adversarial perturbations via randomized smoothing
Jia, J., Cao, X., Wang, B., and Gong, N. Z. (2019) · 2019
Later among the works it cites.
Certified robustness to adversarial examples with differential privacy
Lecuyer, M., Atlidakis, V., Geambasu, R., Hsu, D., and Jana, S. (2019) · 2019
Later among the works it cites.
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Tjeng, V., Xiao, K. Y., and Tedrake, R. (2018) · 2018
Cited alongside, same era.
Efficient formal safety analysis of neural networks
Wang, S., Pei, K., Whitehouse, J., Yang, J., and Jana, S. (2018) · 2018
Cited alongside, same era.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Wong, E. and Kolter, Z. (2018) · 2018
Cited alongside, same era.
Towards fast computation of certified robustness for relu networks
Weng, T.-W., Zhang, H., Chen, H., Song, Z., Hsieh, C.-J., Boning, D., Dhillon, I. S., and Daniel, L. (2018a)
Cited in the paper.
Evaluating the robustness of neural networks: An extreme value theory approach
Weng, T.-W., Zhang, H., Chen, P.-Y., et al. (2018b)
Cited in the paper.
Certified adversarial robustness with additive noise
Li, B., Chen, C., Wang, W., and Carin, L. (2019) · 2019
Later among the works it cites.
Provably robust deep learning via adversarially trained smoothed classifiers
Salman, H., Li, J., Razenshteyn, I., Zhang, P., Zhang, H., Bubeck, S., and Yang, G. (2019) · 2019
Later among the works it cites.
Randomized smoothing of all shapes and sizes
Yang, G., Duan, T., Hu, J. E., Salman, H., Razenshteyn, I., and Li, J. (2020) · 2020
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