Towards Robust Deep Learning with Ensemble Networks and Noisy Layers

TL;DR

This paper proposes a combined ensemble and noisy layer approach to enhance the robustness of deep image classifiers against adversarial attacks while maintaining accuracy, supported by theoretical guarantees and experimental validation.

Contribution

It introduces a novel combination of mechanisms for robustness and accuracy in deep learning, with formal guarantees and empirical evidence.

Findings

The combined approach improves adversarial robustness.

It maintains high accuracy on clean data.

Experimental results validate the effectiveness of the method.

Abstract

In this paper we provide an approach for deep learning that protects against adversarial examples in image classification-type networks. The approach relies on two mechanisms:1) a mechanism that increases robustness at the expense of accuracy, and, 2) a mechanism that improves accuracy but does not always increase robustness. We show that an approach combining the two mechanisms can provide protection against adversarial examples while retaining accuracy. We formulate potential attacks on our approach with experimental results to demonstrate its effectiveness. We also provide a robustness guarantee for our approach along with an interpretation for the guarantee.