AIにより推定されたラベル
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Abstract
Randomized smoothing is the current state-of-the-art defense with provable robustness against ℓ2 adversarial attacks. Many works have devised new randomized smoothing schemes for other metrics, such as ℓ1 or ℓ∞; however, substantial effort was needed to derive such new guarantees. This begs the question: can we find a general theory for randomized smoothing? We propose a novel framework for devising and analyzing randomized smoothing schemes, and validate its effectiveness in practice. Our theoretical contributions are: (1) we show that for an appropriate notion of “optimal”, the optimal smoothing distributions for any “nice” norms have level sets given by the norm’s *Wulff Crystal*; (2) we propose two novel and complementary methods for deriving provably robust radii for any smoothing distribution; and, (3) we show fundamental limits to current randomized smoothing techniques via the theory of *Banach space cotypes*. By combining (1) and (2), we significantly improve the state-of-the-art certified accuracy in ℓ1 on standard datasets. Meanwhile, we show using (3) that with only label statistics under random input perturbations, randomized smoothing cannot achieve nontrivial certified accuracy against perturbations of ℓp-norm $\Omega(\min(1, d^{\frac{1}{p} – \frac{1}{2}}))$, when the input dimension d is large. We provide code in github.com/tonyduan/rs4a.