Digital watermarking for machine learning model

• Titel: Digital watermarking for machine learning model
• Titelzusatz: techniques, protocols and applications
• Mitwirkende: Fan, Lixin [HerausgeberIn]
• Mitwirkende: Chan, Chee Seng [HerausgeberIn]
• Mitwirkende: Yang, Qiang [HerausgeberIn]
• Verf.angabe: Lixin Fan, Chee Seng Chan, Qiang Yang, editors
• Verlagsort: Singapore
• Verlag: Springer
• E-Jahr: 2023
• Jahr: [2023]
• Umfang: xvi, 225 Seiten
• Illustrationen: Illustrationen, Diagramme
• ISBN: 978-981-19-7556-1
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Online-Ausgabe: Digital Watermarking for Machine Learning Model. - 1st ed. 2023.. - Singapore : Springer Nature Singapore, 2023. - 1 Online-Ressource(XVI, 225 p. 1 illus.)
• Sach-SW: Bildverarbeitung
• Sach-SW: COMPUTERS / Artificial Intelligence
• Sach-SW: COMPUTERS / Computer Graphics / Image Processing (see also PHOTOGRAPHY / Techniques / Digital)
• Sach-SW: COMPUTERS / Computer Vision & Pattern Recognition
• Sach-SW: COMPUTERS / Security / General
• Sach-SW: Computer security
• Sach-SW: Computersicherheit
• Sach-SW: Electronics engineering
• Sach-SW: Elektronik
• Sach-SW: Image processing
• Sach-SW: MATHEMATICS / Probability & Statistics / General
• Sach-SW: Machine learning
• Sach-SW: Maschinelles Lernen
• Sach-SW: Network security
• Sach-SW: Netzwerksicherheit
• K10plus-PPN: 1890532606

Abstract

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 models 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