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- W4284671540 abstract "We study certified robustness of machine learning classifiers against adversarial perturbations. In particular, we propose the first universally approximated certified robustness (UniCR) framework, which can approximate the robustness certification of any input on any classifier against any $ell_p$ perturbations with noise generated by any continuous probability distribution. Compared with the state-of-the-art certified defenses, UniCR provides many significant benefits: (1) the first universal robustness certification framework for the above 4 'any's; (2) automatic robustness certification that avoids case-by-case analysis, (3) tightness validation of certified robustness, and (4) optimality validation of noise distributions used by randomized smoothing. We conduct extensive experiments to validate the above benefits of UniCR and the advantages of UniCR over state-of-the-art certified defenses against $ell_p$ perturbations." @default.
- W4284671540 created "2022-07-08" @default.
- W4284671540 creator A5005150832 @default.
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- W4284671540 date "2022-07-05" @default.
- W4284671540 modified "2023-09-30" @default.
- W4284671540 title "UniCR: Universally Approximated Certified Robustness via Randomized Smoothing" @default.
- W4284671540 doi "https://doi.org/10.48550/arxiv.2207.02152" @default.
- W4284671540 hasPublicationYear "2022" @default.
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