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Digital Watermarking for Machine Learning Model : Techniques, Protocols and Applications / edited by Lixin Fan, Chee Seng Chan, Qiang Yang

データ種別	電子ブック
版	1st ed. 2023.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2023
大きさ	XVI, 225 p. 1 illus : online resource
著者標目	Fan, Lixin editor Chan, Chee Seng editor Yang, Qiang editor SpringerLink (Online service)

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URL	巻　次	配架場所	請求記号	登録番号	コメント	刷　年	状　態	利用注記	ISBN	予約	請求メモ
URL		射水-電子	007	EB0002700	Computer Scinece R0 2005-6,2022-3				9789811975547

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一般注記	Part I. Preliminary -- Chapter 1. Introduction -- Chapter 2. Ownership Verification Protocols for Deep Neural Network Watermarks -- Part II Techniques -- Chapter 3. ModelWatermarking for Image Recovery DNNs -- Chapter 4. The Robust and Harmless ModelWatermarking -- Chapter 5. Protecting Intellectual Property of Machine Learning Models via Fingerprinting the Classification Boundary -- Chapter 6. Protecting Image Processing Networks via Model Water -- Chapter 7. Watermarks for Deep Reinforcement Learning -- Chapter 8. Ownership Protection for Image Captioning Models -- Chapter 9.Protecting Recurrent Neural Network by Embedding Key -- Part III Applications -- Chapter 10. FedIPR: Ownership Verification for Federated Deep Neural Network Models -- Chapter 11. Model Auditing For Data Intellectual Property 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 ML models using watermarking. Furthermore, it showcases cutting-edge work in e.g. model watermarking, signature and passport embedding and their use cases in distributed federated learning settings HTTP:URL=https://doi.org/10.1007/978-981-19-7554-7
件　名	LCSH:Machine learning LCSH:Data protection LCSH:Image processing—Digital techniques LCSH:Computer vision LCSH:Image processing FREE:Machine Learning FREE:Data and Information Security FREE:Computer Imaging, Vision, Pattern Recognition and Graphics FREE:Image Processing
分　類	LCC:Q325.5-.7 DC23:006.31
書誌ID	EB00002088
ISBN	9789811975547