Sensitive-Sample Fingerprinting of Deep Neural Networks
| Autor: | Zecheng He, Tianwei Zhang, Ruby B. Lee |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Deep learning Sample (statistics) Cloud computing 010501 environmental sciences computer.software_genre 01 natural sciences Zero (linguistics) 030507 speech-language pathology & audiology 03 medical and health sciences False positive paradox Deep neural networks Data mining Artificial intelligence 0305 other medical science business computer 0105 earth and related environmental sciences |
| Zdroj: | CVPR |
| DOI: | 10.1109/cvpr.2019.00486 |
| Popis: | Numerous cloud-based services are provided to help customers develop and deploy deep learning applications. When a customer deploys a deep learning model in the cloud and serves it to end-users, it is important to be able to verify that the deployed model has not been tampered with. In this paper, we propose a novel and practical methodology to verify the integrity of remote deep learning models, with only black-box access to the target models. Specifically, we define Sensitive-Sample fingerprints, which are a small set of human unnoticeable transformed inputs that make the model outputs sensitive to the model's parameters. Even small model changes can be clearly reflected in the model outputs. Experimental results on different types of model integrity attacks show that we proposed approach is both effective and efficient. It can detect model integrity breaches with high accuracy (>99.95%) and guaranteed zero false positives on all evaluated attacks. Meanwhile, it only requires up to 103X fewer model inferences, compared with non-sensitive samples. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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