Randomized Smoothing
Overview
Randomized smoothing studies how adding random perturbations to inputs or representations can make model predictions more stable and, in many settings, yield certified bounds on the perturbations an attacker can make. Common approaches use Gaussian noise and Monte Carlo estimates, while newer methods adapt the noise or use structured, discrete, or semantically meaningful perturbations. Work spans image classification and extends to language and multimodal models, audio, regression, and trajectory prediction. A central tension is making certificates cheaper and better matched to real attacks without letting the guarantee depend on unrealistic noise assumptions or overstate empirical robustness.
Latest papers
25 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.