Certified Adversarial Robustness via Randomized Smoothing Certified Adversarial Robustness via Randomized Smoothing
Paper summary Cohen et al. study robustness bounds of randomized smoothing, a region-based classification scheme where the prediction is averaged over Gaussian samples around the test input. Specifically, given a test input, the predicted class is the class whose decision region has the largest overlap with a normal distribution of pre-defined variance. The intuition of this approach is that, for small perturbations, the decision regions of classes can’t vary too much. In practice, randomized smoothing is applied using samples. In the paper, Cohen et al. show that this approach conveys robustness against radii R depending on the confidence difference between the actual class and the “runner-up” class. In practice, the radii also depend on the number of samples used. Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).
Certified Adversarial Robustness via Randomized Smoothing
Jeremy M Cohen and Elan Rosenfeld and J. Zico Kolter
arXiv e-Print archive - 2019 via Local arXiv
Keywords: cs.LG, stat.ML


Summary by David Stutz 1 year ago
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