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portada Digital Watermarking for Machine Learning Model
Type
Physical Book
Publisher
Author
Year
2023
Language
English
Pages
244
Format
Paperback
Dimensions
23.4 x 15.6 cm
ISBN13
9789811975554

Digital Watermarking for Machine Learning Model

Chee Seng Chan;Lixin Fan;Qiang Yang (Author) · Springer Nature B.V. · Paperback

Digital Watermarking for Machine Learning Model - Chee Seng Chan;Lixin Fan;Qiang Yang

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Synopsis "Digital Watermarking for Machine Learning Model"

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.

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