Black-box Adversarial Attacks with Bayesian Optimization

TL;DR

This paper introduces a query-efficient black-box adversarial attack method using Bayesian optimization, significantly reducing the number of queries needed compared to existing techniques, especially in low-query scenarios.

Contribution

The paper proposes a novel Bayesian optimization-based approach with effective dimension upsampling to enhance query efficiency in black-box adversarial attacks.

Findings

Achieves comparable attack success with fewer queries.

Reduces query count by up to 80% in low-query regimes.

Performs well on high-dimensional deep learning models.

Abstract

We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples using information limited to loss function evaluations of input-output pairs. We use Bayesian optimization~(BO) to specifically cater to scenarios involving low query budgets to develop query efficient adversarial attacks. We alleviate the issues surrounding BO in regards to optimizing high dimensional deep learning models by effective dimension upsampling techniques. Our proposed approach achieves performance comparable to the state of the art black-box adversarial attacks albeit with a much lower average query count. In particular, in low query budget regimes, our proposed method reduces the query count up to $80\%$ with respect to the state of the art methods.