| 书目名称 | Digital Watermarking for Machine Learning Model | | 副标题 | Techniques, Protocol | | 编辑 | Lixin Fan,Chee Seng Chan,Qiang Yang | | 视频video | | | 概述 | The first book to address the use of digital watermarking for verifying machine learning model ownerships.Presents essential protocols, methodologies and techniques for protecting machine learning mod | | 图书封面 |  | | 描述 | .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. to generate secret conditions that constitute model watermarks or fingerprints only known to model owners. These methods have little or no effect on model performance, which makes them applicable to a wide variety of contexts. In terms of robustness, embedded watermarks must be robustly detectable against varying adversarial attacks that attempt to remove the watermarks. The efficacy of model watermarking methods is showcased in diverse applications including image classification, image generation, image captions, natural language processing and reinforcement learning. ..This book covers the motivations, fundamentals, techniques and protocols for protecting M | | 出版日期 | Book 2023 | | 关键词 | Machine learning model protection; deep learning model protection; model ownerhsip verification; model | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-19-7554-7 | | isbn_softcover | 978-981-19-7556-1 | | isbn_ebook | 978-981-19-7554-7 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor |
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发表于 2025-3-21 17:41:06
