1. Introduction
Randomized Smoothing is a technique used in machine learning to enhance the certified robustness of classifiers. The main idea is to add random noise to the input data and then smooth the classifier’s output over this noise distribution. This approach can help in obtaining stronger robustness guarantees against adversarial attacks.