Papers › Projected Randomized Smoothing for Certified Adversarial Robustness

Projected Randomized Smoothing for Certified Adversarial Robustness

25 Sep 2023arXiv:2309.13794archive 2025-07-28

Samuel Pfrommer, Brendon G. Anderson, Somayeh Sojoudi

Randomized smoothing is the current state-of-the-art method for producing provably robust classifiers. While randomized smoothing typically yields robust ℓ₂-ball certificates, recent research has generalized provable robustness to different norm balls as well as anisotropic regions. This work considers a classifier architecture that first projects onto a low-dimensional approximation of the data manifold and then applies a standard classifier. By performing randomized smoothing in the low-dimensional projected space, we characterize the certified region of our smoothed composite classifier back in the high-dimensional input space and prove a tractable lower bound on its volume. We show experimentally on CIFAR-10 and SVHN that classifiers without the initial projection are vulnerable to perturbations that are normal to the data manifold and yet are captured by the certified regions of our method. We compare the volume of our certified regions against various baselines and show that our method improves on the state-of-the-art by many orders of magnitude.

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adversary_success spfrommer/projected_randomized_smoothing/randsmooth/main/attack.py official repository unverified no licence file found · pointer only · d00ad75826ce855b · report
certified_accuracy_vol spfrommer/projected_randomized_smoothing/randsmooth/main/cert.py official repository unverified no licence file found · pointer only · 6e18a973719c7af2 · report

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Adversarial Robustness

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Randomized Smoothing

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