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Randomized smoothing is the current state-of-the-art defense with provable robustness against $\ell_2$ adversarial attacks.
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Efficient neural network robustness certification with general activation functions
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Mitigating evasion attacks to deep neural networks via region-based classification
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Adversarial examples are not easily detected: Bypassing ten detection methods
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A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
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Formal verification of piece-wise linear feed-forward neural networks
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Deep neural networks as 0-1 mixed integer linear programs: A feasibility study
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Reluplex: An efficient smt solver for verifying deep neural networks
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Unlabeled Data Improves Adversarial Robustness
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A Framework for Robustness Certification of Smoothed Classifiers Using F-Divergences
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Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers
Lee, G.-H., Yuan, Y., Chang, S., and Jaakkola, T · 2019
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Certified Adversarial Robustness with Additive Noise
Li, B., Chen, C., Wang, W., and Carin, L · 2019
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$\ell_1$ Adversarial Robustness Certificates: a Randomized Smoothing Approach
Teng, J., Lee, G.-H., and Yuan, Y · 2019
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Evaluating robustness of neural networks with mixed integer programming
Tjeng, V., Xiao, K. Y., and Tedrake, R · 2019
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Wasserstein adversarial examples via projected sinkhorn iterations
Wong, E., Schmidt, F. R., and Kolter, J. Z · 2019
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Random smoothing might be unable to certify ℓ ∞ \ell_{\infty} robustness for high-dimensional images
Blum, A., Dick, T., Manoj, N., and Zhang, H · 2020
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Improved image wasserstein attacks and defenses
Hu, J. E., Swaminathan, A., Salman, H., and Yang, G · 2020
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Curse of dimensionality on randomized smoothing for certifiable robustness
Kumar, A., Levine, A., Goldstein, T., and Feizi, S · 2020
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Black-box smoothing: A provable defense for pretrained classifiers, 2020
Salman, H., Sun, M., Yang, G., Kapoor, A., and Kolter, J. Z · 2020
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Macer: Attack-free and scalable robust training via maximizing certified radius
Zhai, R., Dan, C., He, D., Zhang, H., Gong, B., Ravikumar, P., Hsieh, C.-J., and Wang, L · 2020
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Filling the soap bubbles: Efficient black-box adversarial certification with non-gaussian smoothing, 2020
Zhang*, D., Ye*, M., Gong*, C., Zhu, Z., and Liu, Q · 2020
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A unified framework for randomized smoothing based certified defenses, 2020
Zheng, T., Wang, D., Li, B., and Xu, J · 2020
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