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

Buch. Condici�n: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 244 pp. Englisch. N� de ref. del art�culo: 9789811975530

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

Publicado por Springer, 2023

ISBN 10: 9811975531 ISBN 13: 9789811975530

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

Fan, Lixin (EDT); Chan, Chee Seng (EDT); Yang, Qiang (EDT)

Publicado por Springer, 2023

ISBN 10: 9811975531 ISBN 13: 9789811975530

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

Fan, Lixin (EDT); Chan, Chee Seng (EDT); Yang, Qiang (EDT)

Publicado por Springer, 2023

ISBN 10: 9811975531 ISBN 13: 9789811975530

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Librería: GreatBookPricesUK, Woodford Green, Reino Unido

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

Lixin Fan

Publicado por Springer Nature Singapore, Springer Nature Singapore, 2023

ISBN 10: 9811975531 ISBN 13: 9789811975530

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Librería: AHA-BUCH GmbH, Einbeck, Alemania

Calificaci�n del vendedor: 5 de 5 estrellas

Buch. Condici�n: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N� de ref. del art�culo: 9789811975530

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