Blacklight: Scalable Defense for Neural Networks against Query-Based Black-Box Attacks


Huiying Li, Shawn Shan, and Emily Wenger, University of Chicago; Jiayun Zhang, Fudan University; Haitao Zheng and Ben Y. Zhao, University of Chicago


Deep learning systems are known to be vulnerable to adversarial examples. In particular, query-based black-box attacks do not require knowledge of the deep learning model, but can compute adversarial examples over the network by submitting queries and inspecting returns. Recent work largely improves the efficiency of those attacks, demonstrating their practicality on today's ML-as-a-service platforms.

We propose Blacklight, a new defense against query-based black-box adversarial attacks. Blacklight is driven by a fundamental insight: to compute adversarial examples, these attacks perform iterative optimization over the network, producing queries highly similar in the input space. Thus Blacklight detects query-based black-box attacks by detecting highly similar queries, using an efficient similarity engine operating on probabilistic content fingerprints. We evaluate Blacklight against eight state-of-the-art attacks, across a variety of models and image classification tasks. Blacklight identifies them all, often after only a handful of queries. By rejecting all detected queries, Blacklight prevents any attack from completing, even when persistent attackers continue to submit queries after banned accounts or rejected queries. Blacklight is also robust against several powerful countermeasures, including an optimal black-box attack that approximates white-box attacks in efficiency. Finally, we illustrate how Blacklight generalizes to other domains like text classification.

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@inproceedings {281294,
author = {Huiying Li and Shawn Shan and Emily Wenger and Jiayun Zhang and Haitao Zheng and Ben Y. Zhao},
title = {Blacklight: Scalable Defense for Neural Networks against {Query-Based} {Black-Box} Attacks},
booktitle = {31st USENIX Security Symposium (USENIX Security 22)},
year = {2022},
isbn = {978-1-939133-31-1},
address = {Boston, MA},
pages = {2117--2134},
url = {},
publisher = {USENIX Association},
month = aug

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