Variational Randomized Smoothing for Sample-Wise Adversarial Robustness
<|reference_start|>Variational Randomized Smoothing for Sample-Wise Adversarial Robustness: 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.<|reference_end|>
@article{hase2024variational,
    title={Variational Randomized Smoothing for Sample-Wise Adversarial Robustness},
    author={Ryo Hase, Ye Wang, Toshiaki Koike-Akino, Jing Liu, Kieran Parsons},
    journal={arXiv preprint arXiv:2407.11844},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11844},
    primaryClass={cs.LG cs.AI cs.CR stat.ML}
}