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Adversarial training is the most successful empirical method for increasing the robustness of neural networks against adversarial attacks.
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Kurakin, A., Boneh, D., Tramèr, F., Goodfellow, I., Kurakin, A., Brain, G., Papernot, N., Goodfellow, I., Boneh, D., McDaniel, P.: Ensemble adversarial training: Attacks and defenses. In: International Conference on Learning Representations, ICLR (2018)
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He, Z., Rakin, A.S., Fan, D.: Parametric noise injection: Trainable randomness to improve deep neural network robustness against adversarial attack. In: Computer Vision and Pattern Recognition, CVPR. pp. 588–597 (2019)
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Tramèr, F., Boneh, D.: Adversarial training and robustness for multiple perturbations. In: Advances in Neural Information Processing Systems, NeurIPS. pp. 5858–5868 (2019)
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Wong, E., Rice, L., Kolter, J.Z.: Fast is better than free: Revisiting adversarial training. In: International Conference on Learning Representations, ICLR (2020), https://openreview.net/forum?id=BJx040EFvH
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