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Zhang Yingjun, Chen Kai, Zhou Geng, Lü Peizhuo, Liu Yong, Huang Liang. Research Progress of Neural Networks Watermarking Technology[J]. Journal of Computer Research and Development, 2021, 58(5): 964-976. DOI: 10.7544/issn1000-1239.2021.20200978
Citation: Zhang Yingjun, Chen Kai, Zhou Geng, Lü Peizhuo, Liu Yong, Huang Liang. Research Progress of Neural Networks Watermarking Technology[J]. Journal of Computer Research and Development, 2021, 58(5): 964-976. DOI: 10.7544/issn1000-1239.2021.20200978

Research Progress of Neural Networks Watermarking Technology

Funds: This work was supported by the Key Program of the National Natural Science Foundation of China (U1836211), the National Natural Science Foundation of China(62072448),the Beijing Natural Science Foundation (JQ18011), the Excellent Member of Youth Innovation Promotion Association, Chinese Academy of Sciences (Y202046), and the Open Project of National Engineering Laboratory of Big Data Collaborative Security.
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  • Published Date: April 30, 2021
  • With the popularization and application of deep neural networks, the trained neural network model has become an important asset and has been provided as machine learning services (MLaaS) for users. However, as a special kind of user, attackers can extract the models when using the services. Considering the high value of the models and risks of being stolen, service providers start to pay more attention to the copyright protection of their models. The main technique is adopted from the digital watermark and applied to neural networks, called neural network watermarking. In this paper, we first analyze this kind of watermarking and show the basic requirements of the design. Then we introduce the related technologies involved in neural network watermarking. Typically, service providers embed watermarks in the neural networks. Once they suspect a model is stolen from them, they can verify the existence of the watermark in the model. Sometimes, the providers can obtain the suspected model and check the existence of watermarks from the model parameters (white-box). But sometimes, the providers cannot acquire the model. What they can only do is to check the input/output pairs of the suspected model (black-box). We discuss these watermarking methods and potential attacks against the watermarks from the viewpoint of robustness, stealthiness, and security. In the end, we discuss future directions and potential challenges.
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