Intellectual Property Protection of Deep-Learning Systems via Hardware/Software Co-Design

被引：1

作者：

Chen, Huili ^{[1
,5
]}

Fu, Cheng ^{[2
]}

Rouhani, Bita Darvish ^{[3
]}

Zhao, Jishen ^{[2
]}

Koushanfar, Farinaz ^{[4
]}

机构：

[1] Univ Calif San Diego, Dept Elect & Comp Engn, La Jolla, CA 92093 USA

[2] Univ Calif San Diego, Comp Sci & Engn Dept, La Jolla, CA 92093 USA

[3] Microsoft, Redmond, WA 98052 USA

[4] Univ Calif San Diego, Elect & Comp Engn, La Jolla, CA 92093 USA

[5] Univ Calif San Diego, La Jolla, CA 92093 USA

来源：

IEEE DESIGN & TEST | 2024年 / 41卷 / 02期

关键词：

Hardware; Fingerprint recognition; Security; Watermarking; IP networks; Computational modeling; Performance evaluation; Intellectual property protection; Deep learning hardware; Attestation; Digital fingerprinting;

D O I：

10.1109/MDAT.2023.3303435

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Editor's notes: This article protects deep learning models by leveraging hardware device-specific model fingerprinting and trusted execution environment. - Gang Qu, University of Maryland, USA © 2013 IEEE.

引用

页码：23 / 31

页数：9

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