Adding identity numbers to deep neural networks

Xiangrui Xu; Yaqin Li; Yunlong Gao; Cao Yuan

doi:10.1117/12.2540293

14 February 2020 Adding identity numbers to deep neural networks

Xiangrui Xu, Yaqin Li, Yunlong Gao, Cao Yuan

Proceedings Volume 11429, MIPPR 2019: Automatic Target Recognition and Navigation; 1142910 (2020) https://doi.org/10.1117/12.2540293
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

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.

Citation Download Citation

Xiangrui Xu, Yaqin Li, Yunlong Gao, and Cao Yuan "Adding identity numbers to deep neural networks", Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 1142910 (14 February 2020); https://doi.org/10.1117/12.2540293

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