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Integrity Fingerprinting of DNN with Double Black-box Design and Verification. (arXiv:2203.10902v2 [cs.CR] UPDATED)
March 24, 2022, 1:20 a.m. | Shuo Wang, Sharif Abuadbba, Sidharth Agarwal, Kristen Moore, Surya Nepal, Salil Kanhere
cs.CR updates on arXiv.org arxiv.org
Cloud-enabled Machine Learning as a Service (MLaaS) has shown enormous
promise to transform how deep learning models are developed and deployed.
Nonetheless, there is a potential risk associated with the use of such services
since a malicious party can modify them to achieve an adverse result.
Therefore, it is imperative for model owners, service providers, and end-users
to verify whether the deployed model has not been tampered with or not. Such
verification requires public verifiability (i.e., fingerprinting patterns are
available …
More from arxiv.org / cs.CR updates on arXiv.org
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