Variational Randomized Smoothing for Sample-Wise Adversarial Robustness

Ryo Hase; Ye Wang; Toshiaki Koike-Akino; Jing Liu; Kieran Parsons

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arXiv · 2407.11844

Variational Randomized Smoothing for Sample-Wise Adversarial Robustness

Ryo Hase·Ye Wang·Toshiaki Koike-Akino·Jing Liu·Kieran Parsons

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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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Ryo Hase, Ye Wang, Toshiaki Koike-Akino, Jing Liu, Kieran Parsons. 2024-07-16. Variational Randomized Smoothing for Sample-Wise Adversarial Robustness. https://arxiv.org/abs/2407.11844

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