Bayesian Adversarial Spheres: Bayesian Inference and Adversarial Examples in a Noiseless Setting
Abstract
Modern deep neural network models suffer from adversarial examples, i.e. confidently misclassified points in the input space. It has been shown that Bayesian neural networks are a promising approach for detecting adversarial points, but careful analysis is problematic due to the complexity of these models. Recently Gilmer et al. (2018) introduced adversarial spheres, a toy set-up that simplifies both practical and theoretical analysis of the problem. In this work, we use the adversarial sphere set-up to understand the properties of approximate Bayesian inference methods for a linear model in a noiseless setting. We compare predictions of Bayesian and non-Bayesian methods, showcasing the advantages of the former, although revealing open challenges for deep learning applications.
Cite
@article{arxiv.1811.12335,
title = {Bayesian Adversarial Spheres: Bayesian Inference and Adversarial Examples in a Noiseless Setting},
author = {Artur Bekasov and Iain Murray},
journal= {arXiv preprint arXiv:1811.12335},
year = {2018}
}
Comments
To appear in the third workshop on Bayesian Deep Learning (NeurIPS 2018), Montreal, Canada