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

arXiv: https://arxiv.org/abs/1811.02658 · HTML: https://mlsanity.com/paper/1811.02658/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2018.

## Built on by (most cited first, 1 of 1)

- [A BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers](https://mlsanity.com/paper/2105.13530/) (2021)

## Abstract (arXiv)

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.