Titlebook: Digital Watermarking for Machine Learning Model; Techniques, Protocol Lixin Fan,Chee Seng Chan,Qiang Yang Book 2023 The Editor(s) (if appli

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https://doi.org/10.1057/9780230372917es, spearheaded by the watermark, have been proposed to establish the connection between a deep neural network and its owner; however, it is until that such connection is provably unambiguous and unforgeable that it can be leveraged for copyright protection. The ownership proof is feasible only afte

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https://doi.org/10.1057/9781137006509lectual properties of their owners. However, recent literature revealed that the adversaries can easily “steal” models by acquiring their function-similar copy, even when they have no training samples and information about the victim models. In this chapter, we introduce a robust and harmless model

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https://doi.org/10.1057/9781137006509s such that it does not need to train its own model, which requires a large amount of resources. Therefore, it becomes an urgent problem how to distinguish such compromise of IP. Watermarking has been widely adopted as a solution in the literature. However, watermarking requires modification of the

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https://doi.org/10.1057/9781137006509(IP) of such valuable image processing networks, the model vendor can sell the service in the manner of the application program interface (API). However, even if the attacker can only query the API, he is still able to conduct model extraction attacks, which can steal the functionality of the target