Evaluating Ensemble Robustness Against Adversarial Attacks

TL;DR

This paper introduces a gradient-based measure to evaluate and improve the robustness of neural network ensembles against transferability of adversarial attacks, enhancing security in black-box scenarios.

Contribution

It proposes a novel gradient-based metric to assess ensemble collaboration in reducing adversarial transferability and demonstrates its use in training more robust ensembles.

Findings

The measure effectively quantifies ensemble robustness.

Training with the measure increases adversarial resistance.

Ensemble collaboration reduces transferability of attacks.

Abstract

Adversarial examples, which are slightly perturbed inputs generated with the aim of fooling a neural network, are known to transfer between models; adversaries which are effective on one model will often fool another. This concept of transferability poses grave security concerns as it leads to the possibility of attacking models in a black box setting, during which the internal parameters of the target model are unknown. In this paper, we seek to analyze and minimize the transferability of adversaries between models within an ensemble. To this end, we introduce a gradient based measure of how effectively an ensemble's constituent models collaborate to reduce the space of adversarial examples targeting the ensemble itself. Furthermore, we demonstrate that this measure can be utilized during training as to increase an ensemble's robustness to adversarial examples.