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Blacklight: Scalable Defense for Neural Networks against Query-Based Black-Box Attacks. (arXiv:2006.14042v3 [cs.CR] UPDATED)
June 10, 2022, 1:20 a.m. | Huiying Li, Shawn Shan, Emily Wenger, Jiayun Zhang, Haitao Zheng, Ben Y. Zhao
cs.CR updates on arXiv.org arxiv.org
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. The fundamental insight driving our design is that, to
compute adversarial examples, these attacks …
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