AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows

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Authors Hadi M. Dolatabadi, Christopher Leckie, Sarah Erfani
Journal/Conference Name arXiv preprint
Paper Category
Paper Abstract Deep learning classifiers are susceptible to well-crafted, imperceptible variations of their inputs, known as adversarial attacks. In this regard, the study of powerful attack models sheds light on the sources of vulnerability in these classifiers, hopefully leading to more robust ones. In this paper, we introduce AdvFlow a novel black-box adversarial attack method on image classifiers that exploits the power of normalizing flows to model the density of adversarial examples around a given target image. We see that the proposed method generates adversaries that closely follow the clean data distribution, a property which makes their detection less likely. Also, our experimental results show competitive performance of the proposed approach with some of the existing attack methods on defended classifiers, outperforming them in both the number of queries and attack success rate. The code is available at https//github.com/hmdolatabadi/AdvFlow.
Date of publication 2020
Code Programming Language Python
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