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Creators/Authors contains: "Rouhani, Bita Darvish"

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  1. ; ; ; ; ; ; ; ; ; (, ACM)
    Free, publicly-accessible full text available July 20, 2026
  2. ; ; ; ; (, IEEE Design & Test)
    Recent advances in model piracy have uncovered a new security hole for malicious attacks endangering the Intellectual Property (IP) of Deep Learning (DL) systems. This manuscript features our research titled “DeepAttest: An End-toEnd Attestation Framework for Deep Neural Networks” [1] that is selected for the 2021 Top Picks in hardware and embedded security. DeepAttest is the first end-to-end framework that achieves reliable and efficient IP protection of DL devices with hardware-bounded usage control. We leverage device-specific model fingerprinting and Trusted Execution Environment (TEE) to ensure that only DL models with the device-specific fingerprint can run inference on protected hardware 
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