Late Breaking Results: Fortifying Neural Networks: Safeguarding Against Adversarial Attacks with Stochastic Computing

TL;DR

This paper explores the use of stochastic computing to enhance neural network security, demonstrating its effectiveness in reducing vulnerability to adversarial attacks through extensive experimental validation.

Contribution

It introduces stochastic computing as a novel approach to improve neural network robustness against adversarial attacks, providing empirical evidence of its effectiveness.

Findings

SC significantly reduces attack impact on NN outputs

Neural networks with SC show increased resilience to adversarial perturbations

The approach enhances data integrity in sensitive applications

Abstract

In neural network (NN) security, safeguarding model integrity and resilience against adversarial attacks has become paramount. This study investigates the application of stochastic computing (SC) as a novel mechanism to fortify NN models. The primary objective is to assess the efficacy of SC to mitigate the deleterious impact of attacks on NN results. Through a series of rigorous experiments and evaluations, we explore the resilience of NNs employing SC when subjected to adversarial attacks. Our findings reveal that SC introduces a robust layer of defense, significantly reducing the susceptibility of networks to attack-induced alterations in their outcomes. This research contributes novel insights into the development of more secure and reliable NN systems, essential for applications in sensitive domains where data integrity is of utmost concern.