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Cham : Springer International Publishing AG, 2022

1 online resource (481 pages)

ISBN 9783030876647 (electronic bk.)

ISBN 9783030876630

Advances in Computer Vision and Pattern Recognition Ser.

Print version: Rathgeb, Christian Handbook of Digital Face Manipulation and Detection Cham : Springer International Publishing AG,c2022 ISBN 9783030876630

Intro -- Preface -- Contents -- Part I Introduction -- 1 An Introduction to Digital Face Manipulation -- 1.1 Introduction -- 1.2 Types of Digital Face Manipulations -- 1.2.1 Entire Face Synthesis -- 1.2.2 Identity Swap -- 1.2.3 Face Morphing -- 1.2.4 Attribute Manipulation -- 1.2.5 Expression Swap -- 1.2.6 Audio-to-Video and Text-to-Video -- 1.3 Conclusions -- References -- 2 Digital Face Manipulation in Biometric Systems -- 2.1 Introduction -- 2.2 Biometric Systems -- 2.2.1 Processes -- 2.2.2 Face Recognition -- 2.3 Digital Face Manipulation in Biometric Systems -- 2.3.1 Impact on Biometric Performance -- 2.3.2 Manipulation Detection Scenarios -- 2.4 Experiments -- 2.4.1 Experimental Setup -- 2.4.2 Performance Evaluation -- 2.5 Summary and Outlook -- References -- 3 Multimedia Forensics Before the Deep Learning Era -- 3.1 Introduction -- 3.2 PRNU-Based Approach -- 3.2.1 PRNU Estimation -- 3.2.2 Noise Residual Computation -- 3.2.3 Forgery Detection Test -- 3.2.4 Estimation Through Guided Filtering -- 3.3 Blind Methods -- 3.3.1 Noise Patterns -- 3.3.2 Compression Artifacts -- 3.3.3 Editing Artifacts -- 3.4 Learning-Based Methods with Handcrafted Features -- 3.5 Conclusions -- References -- Part II Digital Face Manipulation and Security Implications -- 4 Toward the Creation and Obstruction of DeepFakes -- 4.1 Introduction -- 4.2 Backgrounds -- 4.2.1 DeepFake Video Generation -- 4.2.2 DeepFake Detection Methods -- 4.2.3 Existing DeepFake Datasets -- 4.3 Celeb-DF: the Creation of DeepFakes -- 4.3.1 Synthesis Method -- 4.3.2 Visual Quality -- 4.3.3 Evaluations -- 4.4 Landmark Breaker: the Obstruction of DeepFakes -- 4.4.1 Facial Landmark Extractors -- 4.4.2 Adversarial Perturbations -- 4.4.3 Notation and Formulation -- 4.4.4 Optimization -- 4.4.5 Experimental Settings -- 4.4.6 Results -- 4.4.7 Robustness Analysis -- 4.4.8 Ablation Study.

15 Face Morphing Attack Detection Methods -- 15.1 Introduction -- 15.2 Related Works -- 15.3 Morphing Attack Detection Pipeline -- 15.3.1 Data Preparation and Feature Extraction -- 15.3.2 Feature Preparation and Classifier Training -- 15.4 Database -- 15.4.1 Image Morphing -- 15.4.2 Image Post-Processing -- 15.5 Morphing Attack Detection Methods -- 15.5.1 Pre-Processing -- 15.5.2 Feature Extraction -- 15.5.3 Classification -- 15.6 Experiments -- 15.6.1 Generalisability -- 15.6.2 Detection Performance -- 15.6.3 Post-Processing -- 15.7 Summary -- References -- 16 Practical Evaluation of Face Morphing Attack Detection Methods -- 16.1 Introduction -- 16.2 Related Work -- 16.3 Creation of Morphing Datasets -- 16.3.1 Creating Morphs -- 16.3.2 Datasets -- 16.4 Texture-Based Face Morphing Attack Detection -- 16.5 Morphing Disguising -- 16.6 Experiments and Results -- 16.6.1 Within Dataset Performance -- 16.6.2 Cross Dataset Performance -- 16.6.3 Mixed Dataset Performance -- 16.6.4 Robustness Against Additive Gaussian Noise -- 16.6.5 Robustness Against Scaling -- 16.6.6 Selection of Similar Subjects -- 16.7 The SOTAMD Benchmark -- 16.8 Conclusion -- References -- 17 Facial Retouching and Alteration Detection -- 17.1 Introduction -- 17.2 Retouching and Alteration Detection-Review -- 17.2.1 Digital Retouching Detection -- 17.2.2 Digital Alteration Detection -- 17.2.3 Publicly Available Databases -- 17.3 Experimental Evaluation and Observations -- 17.3.1 Cross-Domain Alteration Detection -- 17.3.2 Cross Manipulation Alteration Detection -- 17.3.3 Cross Ethnicity Alteration Detection -- 17.4 Open Challenges -- 17.5 Conclusion -- References -- Part IV Further Topics, Trends, and Challenges -- 18 Detecting Soft-Biometric Privacy Enhancement -- 18.1 Introduction -- 18.2 Background and Related Work -- 18.2.1 Problem Formulation and Existing Solutions.

4.5 Conclusion -- References -- 5 The Threat of Deepfakes to Computer and Human Visions -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Databases and Methods -- 5.3.1 DeepfakeTIMIT -- 5.3.2 DF-Mobio -- 5.3.3 Google and Jigsaw -- 5.3.4 Facebook -- 5.3.5 Celeb-DF -- 5.4 Evaluation Protocols -- 5.4.1 Measuring Vulnerability -- 5.4.2 Measuring Detection -- 5.5 Vulnerability of Face Recognition -- 5.6 Subjective Assessment of Human Vision -- 5.6.1 Subjective Evaluation Results -- 5.7 Evaluation of Deepfake Detection Algorithms -- 5.8 Conclusion -- References -- 6 Morph Creation and Vulnerability of Face Recognition Systems to Morphing -- 6.1 Introduction -- 6.2 Face Morphing Generation -- 6.2.1 Landmark Based Morphing -- 6.2.2 Deep Learning-Based Face Morph Generation -- 6.3 Vulnerability of Face Recognition Systems to Face Morphing -- 6.3.1 Data Sets -- 6.3.2 Results -- 6.3.3 Deep Learning-Based Morphing Results -- 6.4 Conclusions -- References -- 7 Adversarial Attacks on Face Recognition Systems -- 7.1 Introduction -- 7.2 Taxonomy of Attacks on FRS -- 7.2.1 Threat Model -- 7.3 Poisoning Attacks on FRS -- 7.3.1 Fast Gradient Sign Method -- 7.3.2 Projected Gradient Descent -- 7.4 Carlini and Wagner (CW) Attacks -- 7.5 ArcFace FRS Model -- 7.6 Experiments and Analysis -- 7.6.1 Clean Dataset -- 7.6.2 Attack Dataset -- 7.6.3 FRS Model for Baseline Verification -- 7.6.4 FRS Baseline Performance Evaluation -- 7.6.5 FRS Performance on Probe Data Poisoning -- 7.6.6 FRS Performance on Enrolment Data Poisoning -- 7.7 Impact of Adversarial Training with FGSM Attacks -- 7.8 Discussion -- 7.9 Conclusions and Future Directions -- References -- 8 Talking Faces: Audio-to-Video Face Generation -- 8.1 Introduction -- 8.2 Related Work -- 8.2.1 Audio Representation -- 8.2.2 Face Modeling -- 8.2.3 Audio-to-Face Animation -- 8.2.4 Post-processing -- 8.3 Datasets and Metrics.

References -- 12 DeepFakes Detection Based on Heart Rate Estimation: Single- and Multi-frame -- 12.1 Introduction -- 12.2 Related Works -- 12.3 DeepFakesON-Phys -- 12.4 Databases -- 12.4.1 Celeb-DF v2 Database -- 12.4.2 DFDC Preview -- 12.5 Experimental Protocol -- 12.6 Fake Detection Results: DeepFakesON-Phys -- 12.6.1 DeepFakes Detection at Frame Level -- 12.6.2 DeepFakes Detection at Short-Term Video Level -- 12.7 Conclusions -- References -- 13 Capsule-Forensics Networks for Deepfake Detection -- 13.1 Introduction -- 13.2 Related Work -- 13.2.1 Deepfake Generation -- 13.2.2 Deepfake Detection -- 13.2.3 Challenges in Deepfake Detection -- 13.2.4 Capsule Networks -- 13.3 Capsule-Forensics -- 13.3.1 Why Capsule-Forensics? -- 13.3.2 Overview -- 13.3.3 Architecture -- 13.3.4 Dynamic Routing Algorithm -- 13.3.5 Visualization -- 13.4 Evaluation -- 13.4.1 Datasets -- 13.4.2 Metrics -- 13.4.3 Effect of Improvements -- 13.4.4 Feature Extractor Comparison -- 13.4.5 Effect of Statistical Pooling Layers -- 13.4.6 Capsule-Forensics Network Versus CNNs: Seen Attacks -- 13.4.7 Capsule-Forensics Network Versus CNNs: Unseen Attacks -- 13.5 Conclusion and Future Work -- 13.6 Appendix -- References -- 14 DeepFakes Detection: the DeeperForensics Dataset and Challenge -- 14.1 Introduction -- 14.2 Related Work -- 14.2.1 DeepFakes Generation Methods -- 14.2.2 DeepFakes Detection Methods -- 14.2.3 DeepFakes Detection Datasets -- 14.2.4 DeepFakes Detection Benchmarks -- 14.3 DeeperForensics-1.0 Dataset -- 14.3.1 Data Collection -- 14.3.2 DeepFake Variational Auto-Encoder -- 14.3.3 Scale and Diversity -- 14.3.4 Hidden Test Set -- 14.4 DeeperForensics Challenge 2020 -- 14.4.1 Platform -- 14.4.2 Challenge Dataset -- 14.4.3 Evaluation Metric -- 14.4.4 Timeline -- 14.4.5 Results and Solutions -- 14.5 Discussion -- 14.6 Further Reading -- References.

15 Face Morphing Attack Detection Methods -- 15.1 Introduction -- 15.2 Related Works -- 15.3 Morphing Attack Detection Pipeline -- 15.3.1 Data Preparation and Feature Extraction -- 15.3.2 Feature Preparation and Classifier Training -- 15.4 Database -- 15.4.1 Image Morphing -- 15.4.2 Image Post-Processing -- 15.5 Morphing Attack Detection Methods -- 15.5.1 Pre-Processing -- 15.5.2 Feature Extraction -- 15.5.3 Classification -- 15.6 Experiments -- 15.6.1 Generalisability -- 15.6.2 Detection Performance -- 15.6.3 Post-Processing -- 15.7 Summary -- References -- 16 Practical Evaluation of Face Morphing Attack Detection Methods -- 16.1 Introduction -- 16.2 Related Work -- 16.3 Creation of Morphing Datasets -- 16.3.1 Creating Morphs -- 16.3.2 Datasets -- 16.4 Texture-Based Face Morphing Attack Detection -- 16.5 Morphing Disguising -- 16.6 Experiments and Results -- 16.6.1 Within Dataset Performance -- 16.6.2 Cross Dataset Performance -- 16.6.3 Mixed Dataset Performance -- 16.6.4 Robustness Against Additive Gaussian Noise -- 16.6.5 Robustness Against Scaling -- 16.6.6 Selection of Similar Subjects -- 16.7 The SOTAMD Benchmark -- 16.8 Conclusion -- References -- 17 Facial Retouching and Alteration Detection -- 17.1 Introduction -- 17.2 Retouching and Alteration Detection-Review -- 17.2.1 Digital Retouching Detection -- 17.2.2 Digital Alteration Detection -- 17.2.3 Publicly Available Databases -- 17.3 Experimental Evaluation and Observations -- 17.3.1 Cross-Domain Alteration Detection -- 17.3.2 Cross Manipulation Alteration Detection -- 17.3.3 Cross Ethnicity Alteration Detection -- 17.4 Open Challenges -- 17.5 Conclusion -- References -- Part IV Further Topics, Trends, and Challenges -- 18 Detecting Soft-Biometric Privacy Enhancement -- 18.1 Introduction -- 18.2 Background and Related Work -- 18.2.1 Problem Formulation and Existing Solutions.

18.2.2 Soft-Biometric Privacy Models.

4.5 Conclusion -- References -- 5 The Threat of Deepfakes to Computer and Human Visions -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Databases and Methods -- 5.3.1 DeepfakeTIMIT -- 5.3.2 DF-Mobio -- 5.3.3 Google and Jigsaw -- 5.3.4 Facebook -- 5.3.5 Celeb-DF -- 5.4 Evaluation Protocols -- 5.4.1 Measuring Vulnerability -- 5.4.2 Measuring Detection -- 5.5 Vulnerability of Face Recognition -- 5.6 Subjective Assessment of Human Vision -- 5.6.1 Subjective Evaluation Results -- 5.7 Evaluation of Deepfake Detection Algorithms -- 5.8 Conclusion -- References -- 6 Morph Creation and Vulnerability of Face Recognition Systems to Morphing -- 6.1 Introduction -- 6.2 Face Morphing Generation -- 6.2.1 Landmark Based Morphing -- 6.2.2 Deep Learning-Based Face Morph Generation -- 6.3 Vulnerability of Face Recognition Systems to Face Morphing -- 6.3.1 Data Sets -- 6.3.2 Results -- 6.3.3 Deep Learning-Based Morphing Results -- 6.4 Conclusions -- References -- 7 Adversarial Attacks on Face Recognition Systems -- 7.1 Introduction -- 7.2 Taxonomy of Attacks on FRS -- 7.2.1 Threat Model -- 7.3 Poisoning Attacks on FRS -- 7.3.1 Fast Gradient Sign Method -- 7.3.2 Projected Gradient Descent -- 7.4 Carlini and Wagner (CW) Attacks -- 7.5 ArcFace FRS Model -- 7.6 Experiments and Analysis -- 7.6.1 Clean Dataset -- 7.6.2 Attack Dataset -- 7.6.3 FRS Model for Baseline Verification -- 7.6.4 FRS Baseline Performance Evaluation -- 7.6.5 FRS Performance on Probe Data Poisoning -- 7.6.6 FRS Performance on Enrolment Data Poisoning -- 7.7 Impact of Adversarial Training with FGSM Attacks -- 7.8 Discussion -- 7.9 Conclusions and Future Directions -- References -- 8 Talking Faces: Audio-to-Video Face Generation -- 8.1 Introduction -- 8.2 Related Work -- 8.2.1 Audio Representation -- 8.2.2 Face Modeling -- 8.2.3 Audio-to-Face Animation -- 8.2.4 Post-processing -- 8.3 Datasets and Metrics.

8.3.1 Dataset -- 8.3.2 Metrics -- 8.4 Discussion -- 8.4.1 Fine-Grained Facial Control -- 8.4.2 Generalization -- 8.5 Conclusion -- 8.6 Further Reading -- References -- Part III Digital Face Manipulation Detection -- 9 Detection of AI-Generated Synthetic Faces -- 9.1 Introduction -- 9.2 AI Face Generation -- 9.3 GAN Fingerprints -- 9.4 Detection Methods in the Spatial Domain -- 9.4.1 Handcrafted Features -- 9.4.2 Data-Driven Features -- 9.5 Detection Methods in the Frequency Domain -- 9.6 Learning Features that Generalize -- 9.7 Generalization Analysis -- 9.8 Robustness Analysis -- 9.9 Further Analyses on GAN Detection -- 9.10 Open Challenges -- References -- 10 3D CNN Architectures and Attention Mechanisms for Deepfake Detection -- 10.1 Introduction -- 10.2 Related Work -- 10.2.1 Deepfake Detection -- 10.2.2 Attention Mechanisms -- 10.3 Dataset -- 10.4 Algorithms -- 10.5 Experiments -- 10.5.1 All Manipulation Techniques -- 10.5.2 Single Manipulation Techniques -- 10.5.3 Cross-Manipulation Techniques -- 10.5.4 Effect of Attention in 3D ResNets -- 10.5.5 Visualization of Pertinent Features in Deepfake Detection -- 10.6 Conclusions -- References -- 11 Deepfake Detection Using Multiple Data Modalities -- 11.1 Introduction -- 11.2 Deepfake Detection via Video Spatiotemporal Features -- 11.2.1 Overview -- 11.2.2 Model Component -- 11.2.3 Training Details -- 11.2.4 Boosting Network -- 11.2.5 Test Time Augmentation -- 11.2.6 Result Analysis -- 11.3 Deepfake Detection via Audio Spectrogram Analysis -- 11.3.1 Overview -- 11.3.2 Dataset -- 11.3.3 Spectrogram Generation -- 11.3.4 Convolutional Neural Network (CNN) -- 11.3.5 Experimental Results -- 11.4 Deepfake Detection via Audio-Video Inconsistency Analysis -- 11.4.1 Finding Audio-Video Inconsistency via Phoneme-Viseme Mismatching -- 11.4.2 Deepfake Detection Using Affective Cues -- 11.5 Conclusion.

001896586

express

(Au-PeEL)EBL6878098

(MiAaPQ)EBC6878098

(OCoLC)1295276366