LEADER 06958nam 22004573 450 001 9910872182803321 005 20240709080305.0 010 $a9783031582226$b(electronic bk.) 010 $z9783031582219 035 $a(MiAaPQ)EBC31518794 035 $a(Au-PeEL)EBL31518794 035 $a(CKB)32674597800041 035 $a(EXLCZ)9932674597800041 100 $a20240709d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFace de-Identification 205 $a1st ed. 210 1$aCham :$cSpringer International Publishing AG,$d2024. 210 4$dİ2024. 215 $a1 online resource (195 pages) 311 08$aPrint version: Wen, Yunqian Face de-Identification: Safeguarding Identities in the Digital Era Cham : Springer International Publishing AG,c2024 9783031582219 327 $aIntro -- Preface -- Acknowledgments -- About the Book -- Contents -- Acronyms -- Part I Introduction -- 1 Introduction -- 1.1 Background and Motivation -- 1.2 Face Recognition and Face De-identification -- 1.2.1 Face Recognition -- 1.2.2 Face De-identification -- 1.3 Book Overview -- References -- 2 Facial Recognition Technology and the Privacy Risks -- 2.1 Face Recognition Technology -- 2.2 Threat Models and Privacy Risks -- 2.3 Regulations and Acts on Facial Data Privacy -- 2.4 Conclusion and Future Outlook -- References -- Part II Face De-identification Techniques -- 3 Overview of Face De-identification Techniques -- 3.1 Face Image De-identification -- 3.1.1 Obfuscation-Based Methods -- 3.1.2 k-Same Algorithm Based Methods -- 3.1.3 Adversarial Perturbation-Based Methods -- 3.1.4 Deep Generative Model-Based Methods -- 3.1.4.1 Attribute Manipulation-Based Methods -- 3.1.4.2 Conditional Inpainting-Based Methods -- 3.1.4.3 Identity Representation Manipulation-Based Methods -- 3.2 Face Video De-identification -- 3.2.1 Methods of Applying Image De-identification Methods to Videos -- 3.2.1.1 Methods of Applying Image Method Frame by Frame -- 3.2.1.2 Methods of Adding Smooth Transition Measures Between Frames -- 3.2.2 Methods Designed Specifically for Videos -- 3.2.2.1 Methods Based on Manipulating Identity Representation -- 3.2.2.2 Methods Based on Obfuscation -- 3.3 Evaluation Metrics -- 3.3.1 Privacy Protection -- 3.3.2 Utility Preservation -- 3.3.2.1 General Utility -- 3.3.2.2 Customized Utility -- References -- 4 Face Image Privacy Protection with Differential Private k-Anonymity -- 4.1 Introduction -- 4.2 Related Works -- 4.2.1 Privacy-Preserving Machine Learning -- 4.2.2 GAN-Based Face Manipulation -- 4.3 Preliminaries -- 4.3.1 Differential Privacy -- 4.3.2 Privacy Amplification -- 4.4 Our Approach -- 4.4.1 Step 1: Attributes Prediction. 327 $a4.4.2 Step 2: Obfuscation -- 4.4.2.1 k-Anonymity Average Attributes -- 4.4.2.2 Differential Privacy -- 4.4.3 Step 3: Image Generation -- 4.5 Experiments -- 4.5.1 Dataset -- 4.5.2 Implementation Details -- 4.5.3 Performance Analysis -- 4.5.4 Quantitative Evaluation -- 4.6 Conclusion -- References -- 5 Differential Private Identification Protection for Face Images -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Face De-identification Methods Guaranteed by k-Anonymity Theory -- 5.2.2 Face De-identification Methods Guaranteed by t-Closeness Theory -- 5.2.3 Face De-identification Method Guaranteed by Differential Privacy Theory -- 5.3 Preliminaries -- 5.3.1 Problem Formulation -- 5.3.2 Differential Privacy Theory -- 5.3.2.1 Differential Privacy -- 5.3.2.2 Local Differential Privacy -- 5.3.2.3 Two Important Properties -- 5.3.3 Face Verification and Our Assumptions -- 5.3.4 The Proposed IdentityDP Framework -- 5.3.5 Stage-I: Facial Representations Disentanglement -- 5.3.6 Stage-II: ?-IdentityDP Perturbation -- 5.3.7 Stage-III: Image Reconstruction -- 5.3.8 Training Process -- 5.3.9 Some Discussions About Our Research Topic -- 5.4 Experiments -- 5.4.1 Experimental Setup -- 5.4.2 Evaluation Metrics -- 5.4.3 Implementation Details -- 5.4.4 ?-IdentityDP Mechanism Analysis -- 5.4.5 Comparisons with Traditional Methods -- 5.4.6 Comparisons with SOTA Methods -- 5.4.7 Generalization Ability -- 5.4.8 Computational Overhead -- 5.5 Conclusion and Future Work -- References -- 6 Personalized and Invertible Face De-identification -- 6.1 Introduction -- 6.2 Problem Formulation -- 6.3 Our Approach -- 6.3.1 Network Architecture -- 6.3.1.1 Identity Encoder -- 6.3.1.2 Attribute Encoder -- 6.3.1.3 Identity Modification Module -- 6.3.1.4 Generator -- 6.3.2 Training Process -- 6.3.3 Protection Process -- 6.3.4 Recovery Process -- 6.4 Experiments -- 6.4.1 Implementation Details. 327 $a6.4.2 Evaluation Results -- 6.4.2.1 De-identification -- 6.4.2.2 Recovery -- 6.5 Conclusion -- References -- 7 High Quality Face De-identification with Model Explainability -- 7.1 Introduction -- 7.2 Related Work -- 7.2.1 3D Monocular Face Reconstruction -- 7.2.2 Blind Face Restoration -- 7.3 Methodology -- 7.3.1 Overview of IDeudemon -- 7.3.2 Step I: Parametric Identity Protection -- 7.3.3 Step II: Utility Preservation -- 7.3.4 Loss Function -- 7.4 Experiments -- 7.4.1 Experimental Setup -- 7.4.2 Protective Perturbation Analysis. -- 7.4.3 Comparison with SOTA Methods. -- 7.4.4 Model Analysis and Ablation Study -- 7.5 Discussion -- 7.6 Conclusion -- References -- 8 Deep Motion Flow Guided Reversible Face VideoDe-identification -- 8.1 Introduction -- 8.2 Related Work -- 8.2.1 Face Video De-identification -- 8.2.2 Surveillance Video De-identification -- 8.3 Preliminaries of Problem Formulation -- 8.4 Deep Motion Flow Guided Reversible Face VideoDe-identification -- 8.4.1 Protection Module -- 8.4.2 Recovery Module -- 8.4.3 Motion Flow Module -- 8.4.4 Affine Transformation Module -- 8.4.5 The Entire IdentityMask Pipeline -- 8.5 Implementation -- 8.5.1 Identity Disentanglement Network Configuration -- 8.5.2 Other Implementation Details -- 8.6 Experiments -- 8.6.1 Experimental Setup -- 8.6.2 Comparison in De-identification -- 8.6.3 Analysis in Identity Recovery -- 8.6.4 Model Analysis and Discussions -- 8.7 Conclusions -- References -- Part III Conclusion and Future Work -- 9 Future Prospects and Challenges -- 9.1 Future Prospects and Open Research Questions -- 9.2 Technical Challenges -- 9.2.1 Low-Complexity and Real-Time De-identificationMethods -- 9.2.2 Preventing Reverse Engineering Attacks of De-identified Faces -- 9.2.3 Moving Beyond Supervised Learningon Limited Datasets -- 9.2.4 Multimodal De-identification -- References -- 10 Conclusion. 327 $aGlossary. 676 $a006.37 700 $aWen$b Yunqian$01744487 701 $aLiu$b Bo$0592718 701 $aSong$b Li$01626344 701 $aCao$b Jingyi$01213862 701 $aXie$b Rong$01744488 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910872182803321 996 $aFace de-Identification$94174474 997 $aUNINA