Multimedia Forensics
This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications...
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| Main Authors | , , |
|---|---|
| Format | eBook |
| Language | English |
| Published |
Singapore
Springer Nature
2022
Springer Matthew C. Stamm |
| Edition | 1 |
| Series | Advances in Computer Vision and Pattern Recognition |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field. |
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| AbstractList | This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field. |
| Author | Sencar, Husrev Taha Verdoliva, Luisa Memon, Nasir |
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| Snippet | This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data... |
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| SubjectTerms | Applied optics Artificial intelligence Book Industry Communication Computer programming, programs, data Computer science Computer security Computer vision Computing & information technology Computing and Information Technology Counter-Forensics Data protection Deepfakes Digital Image Forensics Digital Integrity ENF Image processing Image Tampering Detection Imaging systems & technology Imaging systems and technology Machine learning Media Forensics Multimedia systems Other technologies & applied sciences Other technologies and applied sciences Sensor Noise (PRNU) Technology, engineering, agriculture Technology, Engineering, Agriculture, Industrial processes thema EDItEUR Video Forensics Video Tampering Detection |
| TableOfContents | 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 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 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 14.3.2 Automated Analysis of mp4-like Videos 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 |
| Title | Multimedia Forensics |
| URI | http://digital.casalini.it/9789811676215 https://directory.doabooks.org/handle/20.500.12854/81636 https://library.oapen.org/handle/20.500.12657/54043 https://ebookcentral.proquest.com/lib/[SITE_ID]/detail.action?docID=6944947 https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9789811676215 |
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