Neural Network Models for Adversarial Robustness using Variational Randomized Smoothing

Abstract

Embodiments disclose a method and a system for robust transformation of input with a neural network. The method comprises processing the input data with a variational neural network (VNN) trained with ML to produce static parameters including noise level for the input data, injecting a set of random noises sampled on a probabilistic distribution according to the statistic parameters defined by the VNN to produce a set of perturbed input samples. The method comprises processing each of the set of perturbed input samples with a transformation neural network to produce a set of transformations and outputting a combination of the set of transformations as the robust transformation of the input data. Some embodiments consider training the variational neural network and transformation neural network by using adversarial examples from an attack model via alternating, explicit, and implicit gradient frameworks.

Dependencies are inferred from the claim text.