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Certification of Speaker Recognition Models to Additive Perturbations
In this paper, we pioneer applying robustness certification techniques to speaker recognition, originally developed for the image domain.
General Lipschitz: Certified Robustness Against Resolvable Semantic Transformations via Transformation-Dependent Randomized Smoothing
Randomized smoothing is the state-of-the-art approach to construct image classifiers that are provably robust against additive adversarial perturbations of bounded magnitude.
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