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.