Name: Digital Watermarking for Machine Learning Model (en Inglés)
Price: 56.80 USD
Availability: InStock
Author: Chee Seng Chan;Lixin Fan;Qiang Yang
ISBN: 9789811975554

portada Digital Watermarking for Machine Learning Model (en Inglés)

Digital Watermarking for Machine Learning Model (en Inglés)

Formato

Libro Físico

Editorial

Springer Nature B.V.

Autor

Chee Seng Chan;Lixin Fan;Qiang Yang

Año

2023

Idioma

Inglés

N° páginas

244

Encuadernación

Tapa Blanda

Dimensiones

23.4 x 15.6 cm

ISBN13

9789811975554

Digital Watermarking for Machine Learning Model (en Inglés)

Chee Seng Chan;Lixin Fan;Qiang Yang (Autor) · Springer Nature B.V. · Tapa Blanda

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Reseña del libro "Digital Watermarking for Machine Learning Model (en Inglés)"

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.