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Found 3 papers, 1 papers with code
Improving Robustness via Tilted Exponential Layer: A Communication-Theoretic Perspective
State-of-the-art techniques for enhancing robustness of deep networks mostly rely on empirical risk minimization with suitable data augmentation.
Generalized Likelihood Ratio Test for Adversarially Robust Hypothesis Testing
We derive the worst-case attack for the GLRT defense, and show that its asymptotic performance (as the dimension of the data increases) approaches that of the minimax defense.
Adversarially Robust Classification based on GLRT
We evaluate the GLRT approach for the special case of binary hypothesis testing in white Gaussian noise under $\ell_{\infty}$ norm-bounded adversarial perturbations, a setting for which a minimax strategy optimizing for the worst-case attack is known.
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