Conference Proceedings

AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows

Hadi Mohaghegh Dolatabadi, Sarah Erfani, Christopher Leckie

Advances in neural information processing systems | Curran Associates, Inc. | Published : 2020

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 ..

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Funding Acknowledgements

This research was undertaken using the LIEF HPC-GPGPU Facility hosted at the University of Melbourne. This Facility was established with the assistance of LIEF Grant LE170100200.