Feb. 13, 2024, 5:42 a.m. | Devansh Bhardwaj Kshitiz Kaushik Sarthak Gupta

cs.LG updates on arXiv.org arxiv.org

Randomized smoothing has emerged as a potent certifiable defense against adversarial attacks by employing smoothing noises from specific distributions to ensure the robustness of a smoothed classifier. However, the utilization of Monte Carlo sampling in this process introduces a compute-intensive element, which constrains the practicality of randomized smoothing on a larger scale. To address this limitation, we propose a novel approach that replaces Monte Carlo sampling with the training of a surrogate neural network. Through extensive experimentation in various settings, …

adversarial adversarial attacks attacks classifier compute cs.lg defense element process robustness sampling scalable scale

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