B.S. Electrical Engineering and Computer Sciences, 2020
University of California, Berkeley
Randomized Smoothing  is a defense against adversarial inputs to machine learning models. In this project, we study randomized smoothing from a statistical-learning-theoretic lens. We show that, under certain conditions, a model upon which the randomized smoothing is performed, compared to a model of the same architecture upon which randomized smoothing is not performed, yields lower natural test accuracy. Extensive experiments are provided to support our conclusions.
hrosenberg _AT_ ece.wisc.edu
 Cohen, Jeremy, Elan Rosenfeld, and Zico Kolter. “Certified Adversarial Robustness via Randomized Smoothing.” International Conference on Machine Learning. 2019.