When Not to Classify: Detection of Reverse Engineering Attacks on DNN Image Classifiers

Yujia Wang; David J. Miller; George Kesidis

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arXiv · 1811.02658

When Not to Classify: Detection of Reverse Engineering Attacks on DNN Image Classifiers

Yujia Wang·David J. Miller·George Kesidis

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Abstract

This paper addresses detection of a reverse engineering (RE) attack targeting a deep neural network (DNN) image classifier; by querying, RE's aim is to discover the classifier's decision rule. RE can enable test-time evasion attacks, which require knowledge of the classifier. Recently, we proposed a quite effective approach (ADA) to detect test-time evasion attacks. In this paper, we extend ADA to detect RE attacks (ADA-RE). We demonstrate our method is successful in detecting "stealthy" RE attacks before they learn enough to launch effective test-time evasion attacks.

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BibTeX RIS

Yujia Wang, David J. Miller, George Kesidis. 2018-10-31. When Not to Classify: Detection of Reverse Engineering Attacks on DNN Image Classifiers. https://arxiv.org/abs/1811.02658

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