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001468499 0247_ $$a10.1007/978-981-19-7554-7$$2doi
001468499 035__ $$aSP(OCoLC)1381096129
001468499 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dYDX$$dEBLCP
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001468499 050_4 $$aQA76.9.A25
001468499 08204 $$a005.82$$223/eng/20230614
001468499 24500 $$aDigital watermarking for machine learning model :$$btechniques, protocols and applications /$$cLixin Fan, Chee Seng Chan, Qiang Yang, editors.
001468499 260__ $$aSingapore :$$bSpringer,$$c2023.
001468499 300__ $$a1 online resource (233 p.)
001468499 500__ $$a5.3 Problem Formulation
001468499 5050_ $$aIntro -- Preface -- Contents -- Contributors -- About the Editors -- Acronyms -- Mathematical Notation -- Fundamentals -- Machine Learning -- Model Watermarking -- Part I Preliminary -- 1 Introduction -- 1.1 Why Digital Watermarking for Machine Learning Models? -- 1.2 How Digital Watermarking Is Used for Machine Learning Models? -- 1.2.1 Techniques -- 1.2.2 Protocols -- 1.2.3 Applications -- 1.3 Related Work -- 1.3.1 White-Box Watermarks -- 1.3.2 Black-Box Watermarks -- 1.3.3 Neural Network Fingerprints -- 1.4 About This Book -- References
001468499 5058_ $$a2 Ownership Verification Protocols for Deep Neural Network Watermarks -- 2.1 Introduction -- 2.2 Security Formulation -- 2.2.1 Functionality Preserving -- 2.2.2 Accuracy and Unambiguity -- 2.2.3 Persistency -- 2.2.4 Other Security Requirements -- 2.3 The Ownership Verification Protocol for DNN -- 2.3.1 The Boycotting Attack and the Corresponding Security -- 2.3.2 The Overwriting Attack and the Corresponding Security -- 2.3.3 Evidence Exposure and the Corresponding Security -- 2.3.4 A Logic Perspective of the OV Protocol -- 2.3.5 Remarks on Advanced Protocols -- 2.4 Conclusion -- References
001468499 5058_ $$aPart II Techniques -- 3 Model Watermarking for Deep Neural Networks of ImageRecovery -- 3.1 Introduction -- 3.2 Related Works -- 3.2.1 White-Box Model Watermarking -- 3.2.2 Black-Box Model Watermarking -- 3.3 Problem Formulation -- 3.3.1 Notations and Definitions -- 3.3.2 Principles for Watermarking Image Recovery DNNs -- 3.3.3 Model-Oriented Attacks to Model Watermarking -- 3.4 Proposed Method -- 3.4.1 Main Idea and Framework -- 3.4.2 Trigger Key Generation -- 3.4.3 Watermark Generation -- 3.4.4 Watermark Embedding -- 3.4.5 Watermark Verification -- 3.4.6 Auxiliary Copyright Visualizer
001468499 5058_ $$a3.5 Conclusion -- References -- 4 The Robust and Harmless Model Watermarking -- 4.1 Introduction -- 4.2 Related Work -- 4.2.1 Model Stealing -- 4.2.2 Defenses Against Model Stealing -- 4.3 Revisiting Existing Model Ownership Verification -- 4.3.1 The Limitation of Dataset Inference -- 4.3.2 The Limitation of Backdoor-Based Watermarking -- 4.4 The Proposed Method Under Centralized Training -- 4.4.1 Threat Model and Method Pipeline -- 4.4.2 Model Watermarking with Embedded External Features -- 4.4.3 Training Ownership Meta-Classifier -- 4.4.4 Model Ownership Verification with Hypothesis Test
001468499 5058_ $$a4.5 The Proposed Method Under Federated Learning -- 4.5.1 Problem Formulation and Threat Model -- 4.5.2 The Proposed Method -- 4.6 Experiments -- 4.6.1 Experimental Settings -- 4.6.2 Main Results Under Centralized Training -- 4.6.3 Main Results Under Federated Learning -- 4.6.4 The Effects of Key Hyper-Parameters -- 4.6.5 Ablation Study -- 4.7 Conclusion -- References -- 5 Protecting Intellectual Property of Machine Learning Models via Fingerprinting the Classification Boundary -- 5.1 Introduction -- 5.2 Related Works -- 5.2.1 Watermarking for IP Protection -- 5.2.2 Classification Boundary
001468499 506__ $$aAccess limited to authorized users.
001468499 520__ $$aMachine learning (ML) models, especially large pretrained deep learning (DL) models, are of high economic value and must be properly protected with regard to intellectual property rights (IPR). Model watermarking methods are proposed to embed watermarks into the target model, so that, in the event it is stolen, the models owner can extract the pre-defined watermarks to assert ownership. Model watermarking methods adopt frequently used techniques like backdoor training, multi-task learning, decision boundary analysis etc. to generate secret conditions that constitute model watermarks or fingerprints only known to model owners. These methods have little or no effect on model performance, which makes them applicable to a wide variety of contexts. In terms of robustness, embedded watermarks must be robustly detectable against varying adversarial attacks that attempt to remove the watermarks. The efficacy of model watermarking methods is showcased in diverse applications including image classification, image generation, image captions, natural language processing and reinforcement learning. This book covers the motivations, fundamentals, techniques and protocols for protecting ML models using watermarking. Furthermore, it showcases cutting-edge work in e.g. model watermarking, signature and passport embedding and their use cases in distributed federated learning settings.
001468499 650_0 $$aDigital watermarking.
001468499 650_0 $$aMachine learning$$xSafety measures.
001468499 655_0 $$aElectronic books.
001468499 7001_ $$aFan, Lixin$$c(Scientist)
001468499 7001_ $$aChan, Chee Seng.
001468499 7001_ $$aYang, Qiang,$$d1961-
001468499 77608 $$iPrint version:$$aFan, Lixin$$tDigital Watermarking for Machine Learning Model$$dSingapore : Springer Singapore Pte. Limited,c2023$$z9789811975530
001468499 852__ $$bebk
001468499 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-7554-7$$zOnline Access$$91397441.1
001468499 909CO $$ooai:library.usi.edu:1468499$$pGLOBAL_SET
001468499 980__ $$aBIB
001468499 980__ $$aEBOOK
001468499 982__ $$aEbook
001468499 983__ $$aOnline
001468499 994__ $$a92$$bISE