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Digital Watermarking for Machine Learning Model: Techniques, Protocols and Applications

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Digital Watermarking for Machine Learning Model: Techniques, Protocols and Applications - Fan, Lixin (Editor), and Chan, Chee Seng (Editor), and Yang, Qiang (Editor)
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Machine learning (ML) models, especially large pretrained deep learning (DL) models, are of high economic value and must be properly protected with regard to intellectual property rights (IPR). Model watermarking methods are proposed to embed watermarks into the target model, so that, in the event it is stolen, the model's owner can extract the pre-defined watermarks to assert ownership. Model watermarking methods adopt frequently used techniques like backdoor training, multi-task learning, decision boundary analysis etc. ...

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Digital Watermarking for Machine Learning Model: Techniques, Protocols and Applications 2023, Springer Verlag, Singapore, Singapore

ISBN-13: 9789811975530

Hardcover