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Proceedings Paper

Adding identity numbers to deep neural networks
Author(s): Xiangrui Xu; Yaqin Li; Yunlong Gao; Cao Yuan
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Paper Abstract

Amid the maturity of machine learning, deep neural networks are gradually applied in the business sector rather than be restricted in the laboratory. However, its intellectual property protection encounters a significant challenge. In this paper, we aim at embedding a unique identity number (ID) to the deep neural network for model ownership verification. To this end, a scheme of generating DNN ID is proposed, which is the criterion for model ownership verification. After embedding, the model can complete the original performance and own a unique ID of this model as well. DNN ID can only be generated by the owner to check the model authorship. We evaluate this method on MNIST. Experiment results demonstrate that the DNN ID can accurately verify the ownership of our trained model.

Paper Details

Date Published: 14 February 2020
PDF: 5 pages
Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 1142910 (14 February 2020); doi: 10.1117/12.2540293
Show Author Affiliations
Xiangrui Xu, Wuhan Polytechnic Univ. (China)
Yaqin Li, Wuhan Polytechnic Univ. (China)
Yunlong Gao, Wuhan Polytechnic Univ. (China)
Cao Yuan, Wuhan Polytechnic Univ. (China)


Published in SPIE Proceedings Vol. 11429:
MIPPR 2019: Automatic Target Recognition and Navigation
Jianguo Liu; Hanyu Hong; Xia Hua, Editor(s)

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