Can I read Digital Watermarking for Machine Learning Model : Techniques, Protocols and Applications on EtoBox?

Digital Watermarking for Machine Learning Model : Techniques, Protocols and Applications by Lixin Fan, Chee Seng Chan, Qiang Yang, (eds.) is a nonfiction available to read on EtoBox.

What is Digital Watermarking for Machine Learning Model : Techniques, Protocols and Applications about?

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 (NLP) and Reinforcement Learning. This book covers the motivations, fundamentals, techniques and protocols for protecting ML

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

Author

Lixin Fan, Chee Seng Chan, Qiang Yang, (eds.)

Publisher

Springer Nature Singapore Pte Ltd Fka Springer Science + Business Media Singapore Pte Ltd

Published

2023

Language

EN

ISBN

9789811975530

Category

nonfiction

Subjects

Computer Science, Technology, Science