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Found 3 papers, 1 papers with code
Improved robustness to adversarial examples using Lipschitz regularization of the loss
We augment adversarial training (AT) with worst case adversarial training (WCAT) which improves adversarial robustness by 11% over the current state-of-the-art result in the $\ell_2$ norm on CIFAR-10.
Lipschitz regularized Deep Neural Networks generalize and are adversarially robust
In this work we study input gradient regularization of deep neural networks, and demonstrate that such regularization leads to generalization proofs and improved adversarial robustness.
Anomaly detection and classification for streaming data using PDEs
We propose in this paper a fast real-time streaming version of the PDA algorithm for anomaly detection that exploits the computational advantages of PDE continuum limits.
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