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Recent work has shown that it is possible to train deep neural networks that are provably robust to norm-bounded adversarial perturbations.
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Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu, “Towards deep learning models resistant to adversarial attacks,” in
2018
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2018
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2018
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Matthew Mirman, Timon Gehr, and Martin Vechev, “Differentiable abstract interpretation for provably robust neural networks,” in
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Kai Y. Xiao, Vincent Tjeng, Nur Muhammad (Mahi) Shafiullah, and Aleksander Madry, “Training for faster adversarial robustness verification via inducing reLU stability,” in
2019
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2019
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