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Variational Randomized Smoothing for Sample-Wise Adversarial Robustness (2407.11844v1)

Published 16 Jul 2024 in cs.LG, cs.AI, cs.CR, and stat.ML

Abstract: Randomized smoothing is a defensive technique to achieve enhanced robustness against adversarial examples which are small input perturbations that degrade the performance of neural network models. Conventional randomized smoothing adds random noise with a fixed noise level for every input sample to smooth out adversarial perturbations. This paper proposes a new variational framework that uses a per-sample noise level suitable for each input by introducing a noise level selector. Our experimental results demonstrate enhancement of empirical robustness against adversarial attacks. We also provide and analyze the certified robustness for our sample-wise smoothing method.

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