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Poster
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows
Hadi Mohaghegh Dolatabadi · Sarah Erfani · Christopher Leckie

Tue Dec 08 09:00 PM -- 11:00 PM (PST) @ Poster Session 2 #669

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.

Author Information

Hadi Mohaghegh Dolatabadi (University of Melbourne)
Sarah Erfani (University of Melbourne)
Christopher Leckie (University of Melbourne)

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