Multimedia Forensics.

• Superior document: Advances in Computer Vision and Pattern Recognition Series
• Author: Sencar, Husrev Taha.
• TeilnehmendeR: Verdoliva, Luisa.; Memon, Nasir.
• Place / Publishing House: Singapore : : Springer,, 2022.; Ã2022.
• Year of Publication: 2022
• Edition: 1st ed.
• Language: English
• Series: Advances in Computer Vision and Pattern Recognition Series
• Physical Description: 1 online resource (494 pages)

Table of Contents

Intro -- Preface -- Contents -- Symbols -- Notation -- Part I Present and Challenges -- 1 What's in This Book and Why? -- 1.1 Introduction -- 1.2 Overviews -- 2 Media Forensics in the Age of Disinformation -- 2.1 Media and the Human Experience -- 2.2 The Threat to Democracy -- 2.3 New Technologies, New Threats -- 2.3.1 End-to-End Trainable Speech Synthesis -- 2.3.2 GAN-Based Codecs for Still and Moving Pictures -- 2.3.3 Improvements in Image Manipulation -- 2.3.4 Trillion-Param Models -- 2.3.5 Lottery Tickets and Compression in Generative Models -- 2.4 New Developments in the Private Sector -- 2.4.1 Image and Video -- 2.4.2 Language Models -- 2.5 Threats in the Wild -- 2.5.1 User-Generated Manipulations -- 2.5.2 Corporate Manipulation Services -- 2.5.3 Nation State Manipulation Examples -- 2.5.4 Use of AI Techniques for Deception 2019-2020 -- 2.6 Threat Models -- 2.6.1 Carnegie Mellon BEND Framework -- 2.6.2 The ABC Framework -- 2.6.3 The AMITT Framework -- 2.6.4 The SCOTCH Framework -- 2.6.5 Deception Model Effects -- 2.6.6 4Ds -- 2.6.7 Advanced Persistent Manipulators -- 2.6.8 Scenarios for Financial Harm -- 2.7 Investments in Countering False Media -- 2.7.1 DARPA SEMAFOR -- 2.7.2 The Partnership on AI Steering Committee on Media Integrity Working Group -- 2.7.3 JPEG Committee -- 2.7.4 Content Authenticity Initiative (CAI) -- 2.7.5 Media Review -- 2.8 Excerpts on Susceptibility and Resilience to Media Manipulation -- 2.8.1 Susceptibility and Resilience -- 2.8.2 Case Studies: Threats and Actors -- 2.8.3 Dynamics of Exploitative Activities -- 2.8.4 Meta-Review -- 2.9 Conclusion -- References -- 3 Computational Imaging -- 3.1 Introduction to Computational Imaging -- 3.2 Automation of Geometrically Correct Synthetic Blur -- 3.2.1 Primary Cue: Image Noise -- 3.2.2 Additional Photo Forensic Cues -- 3.2.3 Focus Manipulation Detection.

3.2.4 Portrait Mode Detection Experiments -- 3.2.5 Conclusions on Detecting Geometrically Correct Synthetic Blur -- 3.3 Differences Between Optical and Digital Blur -- 3.3.1 Authentically Blurred Edges -- 3.3.2 Authentic Sharp Edge -- 3.3.3 Forged Blurred Edge -- 3.3.4 Forged Sharp Edge -- 3.3.5 Distinguishing IGHs of the Edge Types -- 3.3.6 Classifying IGHs -- 3.3.7 Splicing Logo Dataset -- 3.3.8 Experiments Differentiating Optical and Digital Blur -- 3.3.9 Conclusions: Differentiating Optical and Digital Blur -- 3.4 Additional Forensic Challenges from Computational Cameras -- References -- Part II Attribution -- 4 Sensor Fingerprints: Camera Identification and Beyond -- 4.1 Introduction -- 4.2 Sensor Noise Fingerprints -- 4.3 Camera Identification -- 4.4 Sensor Misalignment -- 4.5 Image Manipulation Localization -- 4.6 Counter-Forensics -- 4.7 Camera Fingerprints and Deep Learning -- 4.8 Public Datasets -- 4.9 Concluding Remarks -- References -- 5 Source Camera Attribution from Videos -- 5.1 Introduction -- 5.2 Challenges in Attributing Videos -- 5.3 Attribution of Downsized Media -- 5.3.1 The Effect of In-Camera Downsizing on PRNU -- 5.3.2 Media with Mismatching Resolutions -- 5.4 Mitigation of Video Coding Artifacts -- 5.4.1 Video Coding from Attribution Perspective -- 5.4.2 Compensation of Loop Filtering -- 5.4.3 Coping with Quantization-Related Weakening of PRNU -- 5.5 Tackling Digital Stabilization -- 5.5.1 Inverting Frame Level Stabilization Transformations -- 5.5.2 Inverting Spatially Variant Stabilization Transformations -- 5.6 Datasets -- 5.7 Conclusions and Outlook -- References -- 6 Camera Identification at Large Scale -- 6.1 Introduction -- 6.2 Naive Methods -- 6.2.1 Linear Search -- 6.2.2 Sequential Trimming -- 6.3 Efficient Pairwise Correlation -- 6.3.1 Search over Fingerprint Digests -- 6.3.2 Pixel Quantization -- 6.3.3 Downsizing.

6.3.4 Dimension Reduction Using PCA and LDA -- 6.3.5 PRNU Compression via Random Projection -- 6.3.6 Preprocessing, Quantization, Coding -- 6.4 Decreasing the Number of Comparisons -- 6.4.1 Clustering by Cameras -- 6.4.2 Composite Fingerprints -- 6.5 Hybrid Methods -- 6.5.1 Search over Composite-Digest Search Tree -- 6.5.2 Search over Full Digest Search Tree -- 6.6 Conclusion -- References -- 7 Source Camera Model Identification -- 7.1 Introduction -- 7.1.1 Image Acquisition Pipeline -- 7.1.2 Problem Formulation -- 7.2 Model-Based Approaches -- 7.2.1 Color Filter Array (CFA) -- 7.2.2 Lens Effects -- 7.2.3 Other Processing and Defects -- 7.3 Data-Driven Approaches -- 7.3.1 Hand-Crafted Features -- 7.3.2 Learned Features -- 7.4 Datasets and Benchmarks -- 7.4.1 Template Dataset -- 7.4.2 State-of-the-art Datasets -- 7.4.3 Benchmark Protocol -- 7.5 Case Studies -- 7.5.1 Experimental Setup -- 7.5.2 Comparison of Closed-Set Methods -- 7.5.3 Comparison of Open-Set Methods -- 7.6 Conclusions and Outlook -- References -- 8 GAN Fingerprints in Face Image Synthesis -- 8.1 Introduction -- 8.2 Related Work -- 8.2.1 Generative Adversarial Networks -- 8.2.2 GAN Detection Techniques -- 8.3 GAN Fingerprint Removal: GANprintR -- 8.4 Databases -- 8.4.1 Real Face Images -- 8.4.2 Synthetic Face Images -- 8.5 Experimental Setup -- 8.5.1 Pre-processing -- 8.5.2 Facial Manipulation Detection Systems -- 8.5.3 Protocol -- 8.6 Experimental Results -- 8.6.1 Controlled Scenarios -- 8.6.2 In-the-Wild Scenarios -- 8.6.3 GAN-Fingerprint Removal -- 8.6.4 Impact of GANprintR on Other Fake Detectors -- 8.7 Conclusions and Outlook -- References -- Part III Integrity and Authenticity -- 9 Physical Integrity -- 9.1 Introduction -- 9.1.1 Journalistic Fact Checking -- 9.1.2 Physics-Based Methods in Multimedia Forensics -- 9.1.3 Outline of This Chapter.

9.2 Physics-Based Models for Forensic Analysis -- 9.2.1 Geometry and Optics -- 9.2.2 Photometry and Reflectance -- 9.3 Algorithms for Physics-Based Forensic Analysis -- 9.3.1 Principal Points and Homographies -- 9.3.2 Photometric Methods -- 9.3.3 Point Light Sources and Line Constraints in the Projective Space -- 9.4 Discussion and Outlook -- 9.5 Picture Credits -- References -- 10 Power Signature for Multimedia Forensics -- 10.1 Electric Network Frequency (ENF): An Environmental Signature for Multimedia Recordings -- 10.2 Technical Foundations of ENF-Based Forensics -- 10.2.1 Reference Signal Acquisition -- 10.2.2 ENF Signal Estimation -- 10.2.3 Higher Order Harmonics for ENF Estimation -- 10.3 ENF Characteristics and Embedding Conditions -- 10.3.1 Establishing Presence of ENF Traces -- 10.3.2 Modeling ENF Behavior -- 10.4 ENF Traces in the Visual Track -- 10.4.1 Mechanism of ENF Embedding in Videos and Images -- 10.4.2 ENF Extraction from the Visual Track -- 10.4.3 ENF Extraction from a Single Image -- 10.5 Key Applications in Forensics and Security -- 10.5.1 Joint Time-Location Authentication -- 10.5.2 Integrity Authentication -- 10.5.3 ENF-Based Localization -- 10.5.4 ENF-Based Camera Forensics -- 10.6 Anti-Forensics and Countermeasures -- 10.6.1 Anti-Forensics and Detection of Anti-Forensics -- 10.6.2 Game-Theoretic Analysis on ENF-Based Forensics -- 10.7 Applications Beyond Forensics and Security -- 10.7.1 Multimedia Synchronization -- 10.7.2 Time-Stamping Historical Recordings -- 10.7.3 Audio Restoration -- 10.8 Conclusions and Outlook -- References -- 11 Data-Driven Digital Integrity Verification -- 11.1 Introduction -- 11.2 Forensics Clues -- 11.2.1 Camera-Based Artifacts -- 11.2.2 JPEG Artifacts -- 11.2.3 Editing Artifacts -- 11.3 Localization Versus Detection -- 11.3.1 Patch-Based Localization -- 11.3.2 Image-Based Localization.

11.3.3 Detection -- 11.4 Architectural Solutions -- 11.4.1 Constrained Networks -- 11.4.2 Two-Branch Networks -- 11.4.3 Fully Convolutional Networks -- 11.4.4 Siamese Networks -- 11.5 Datasets -- 11.6 Major Challenges -- 11.7 Conclusions and Future Directions -- References -- 12 DeepFake Detection -- 12.1 Introduction -- 12.2 DeepFake Video Generation -- 12.3 Current DeepFake Detection Methods -- 12.3.1 General Principles -- 12.3.2 Categorization Based on Methodology -- 12.3.3 Categorization Based on Input Types -- 12.3.4 Categorization Based on Output Types -- 12.3.5 The DeepFake-o-Meter Platform -- 12.3.6 Datasets -- 12.3.7 Challenges -- 12.4 Future Directions -- 12.5 Conclusion and Outlook -- References -- 13 Video Frame Deletion and Duplication -- 13.1 Introduction -- 13.2 Related Work -- 13.2.1 Frame Deletion Detection -- 13.2.2 Frame Duplication Detection -- 13.3 Frame Deletion Detection -- 13.3.1 Baseline Approaches -- 13.3.2 C3D Network for Frame Deletion Detection -- 13.3.3 Experimental Result -- 13.4 Frame Duplication Detection -- 13.4.1 Coarse-Level Search for Duplicated Frame Sequences -- 13.4.2 Fine-Level Search for Duplicated Frames -- 13.4.3 Inconsistency Detector for Duplication Localization -- 13.4.4 Experimental Results -- 13.5 Conclusions and Discussion -- References -- 14 Integrity Verification Through File Container Analysis -- 14.1 Introduction -- 14.1.1 Main Image File Format Specifications -- 14.1.2 Main Video File Format Specifications -- 14.2 Analysis of Image File Formats -- 14.2.1 Analysis of JPEG Tables and Image Resolution -- 14.2.2 Analysis of Exif Metadata Parameters -- 14.2.3 Analysis of the JPEG File Format -- 14.2.4 Automatic Analysis of JPEG Header Information -- 14.2.5 Methods for the Identification of Social Networks -- 14.3 Analysis of Video File Formats -- 14.3.1 Analysis of the Video File Structure.

14.3.2 Automated Analysis of mp4-like Videos.