Multimedia Forensics.
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Superior document: | Advances in Computer Vision and Pattern Recognition Series |
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TeilnehmendeR: | |
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
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Online Access: | |
Physical Description: | 1 online resource (494 pages) |
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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.