Digital Watermarking for Machine Learning Model

Beschreibung

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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 244 pp. Englisch.

Bestandsnummer des Verkäufers 9789811975530

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Bibliografische Details

Titel

Digital Watermarking for Machine Learning Model

Verlag

Springer Nature Singapore, Springer Nature Singapore Mai 2023

Erscheinungsjahr

2023

Sprache

Englisch

ISBN-10

9811975531

ISBN-13

9789811975530

Einband

Buch

Zustand

Neu

Abmessungen

241B x 160 H x 19 T Millimeter Breite: 241 mm, Höhe: 160 mm, Tiefe: 19 mm

Gewicht

535 Gramm