04475nam 22006735 450 99654683960331620230529115623.0981-19-7554-X10.1007/978-981-19-7554-7(MiAaPQ)EBC30554450(Au-PeEL)EBL30554450(DE-He213)978-981-19-7554-7(BIP)085784258(PPN)270617817(EXLCZ)992680150410004120230529d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDigital Watermarking for Machine Learning Model[electronic resource] Techniques, Protocols and Applications /edited by Lixin Fan, Chee Seng Chan, Qiang Yang1st ed. 2023.Singapore :Springer Nature Singapore :Imprint: Springer,2023.1 online resource (233 pages)Print version: Fan, Lixin Digital Watermarking for Machine Learning Model Singapore : Springer Singapore Pte. Limited,c2023 9789811975530 Part I. Preliminary -- Chapter 1. Introduction -- Chapter 2. Ownership Verification Protocols for Deep Neural Network Watermarks -- Part II Techniques -- Chapter 3. ModelWatermarking for Image Recovery DNNs -- Chapter 4. The Robust and Harmless ModelWatermarking -- Chapter 5. Protecting Intellectual Property of Machine Learning Models via Fingerprinting the Classification Boundary -- Chapter 6. Protecting Image Processing Networks via Model Water -- Chapter 7. Watermarks for Deep Reinforcement Learning -- Chapter 8. Ownership Protection for Image Captioning Models -- Chapter 9.Protecting Recurrent Neural Network by Embedding Key -- Part III Applications -- Chapter 10. FedIPR: Ownership Verification for Federated Deep Neural Network Models -- Chapter 11. Model Auditing For Data Intellectual Property .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.Machine learningData protectionImage processing—Digital techniquesComputer visionImage processingMachine LearningData and Information SecurityComputer Imaging, Vision, Pattern Recognition and GraphicsImage ProcessingEngineeringTechnology & EngineeringMachine learning.Data protection.Image processing—Digital techniques.Computer vision.Image processing.Machine Learning.Data and Information Security.Computer Imaging, Vision, Pattern Recognition and Graphics.Image Processing.005.82Fan Lixin1362784Chan Chee Seng1362785Yang Qiang867534MiAaPQMiAaPQMiAaPQBOOK996546839603316Digital Watermarking for Machine Learning Model3382324UNISA