Video verification in the fake news era [electronic resource] / Vasileios Mezaris, Lyndon Nixon, Symeon Papadopoulos, Denis Teyssou, editors.
This book presents the latest technological advances and practical tools for discovering, verifying and visualizing social media video content, and managing related rights. The digital media revolution is bringing breaking news to online video platforms, and news organizations often rely on user-gen...
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| Other Authors: | , , , |
| Format: | Electronic eBook |
| Language: | English |
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Cham :
Springer,
2019.
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Table of Contents:
- Intro; Preface; Part I Problem Statement; Part II Technologies; Part III Applications; Part IV Concluding Remarks; Contents; Contributors; Part I Problem Statement; 1 Video Verification: Motivation and Requirements; 1.1 Introduction; 1.1.1 Who Verifies Video, Which Groups Are We Dealing With and Why Is This Important?; 1.1.2 The Value of Video Analysis; 1.1.3 Tools/Platforms ̀Made for Video Verification'; 1.1.4 A New Challenge: ̀Deep Fakes'; 1.1.5 Typology of Fake Videos; References; Part II Technologies; 2 Real-Time Story Detection and Video Retrieval from Social Media Streams.
- 2.1 Introduction2.2 News Story Detection: Related Work/State of the Art; 2.3 InVID Approach to News Story Detection; 2.3.1 Content Annotation; 2.3.2 Clustering; 2.3.3 Ranking; 2.3.4 Labeling; 2.4 Story Detection: Implementation and Evaluation; 2.5 Video Retrieval from Social Media Streams; 2.5.1 Video Information Retrieval: Related Work/State of the Art; 2.5.2 Video API Querying; 2.5.3 Relevance Filtering and Ranking; 2.6 Social Video Retrieval: Implementation and Evaluation; 2.7 Conclusions; References; 3 Video Fragmentation and Reverse Search on the Web; 3.1 Introduction; 3.2 Related Work.
- 3.2.1 Video Fragmentation3.2.2 Reverse Video Search on the Web; 3.3 State-of-the-Art Techniques and Tools; 3.3.1 Video Fragmentation; 3.3.2 Reverse Video Search on the Web; 3.4 Performance Evaluation and Benchmarking; 3.4.1 Video Fragmentation; 3.4.2 Reverse Video Search on the Web; 3.5 Conclusions and Future Work; References; 4 Finding Near-Duplicate Videos in Large-Scale Collections; 4.1 Introduction; 4.2 Literature Review; 4.2.1 Definition and Related Research Problems; 4.2.2 NDVR Approaches; 4.2.3 Benchmark Datasets; 4.3 NDVR Approaches in InVID; 4.3.1 Bag-of-Words Approach.
- 4.3.2 Deep Metric Learning Approach4.4 Evaluation; 4.4.1 Experimental Setup; 4.4.2 Experimental Results; 4.5 Conclusions and Future Work; References; 5 Finding Semantically Related Videos in Closed Collections; 5.1 Problem Definition and Challenge; 5.2 Semantic Video Annotation; 5.2.1 Related Work; 5.2.2 Methodology; 5.2.3 Results; 5.3 Logo Detection; 5.3.1 Related Work; 5.3.2 Methodology; 5.3.3 Results; 5.4 Conclusions and Future Work; References; 6 Detecting Manipulations in Video; 6.1 Introduction; 6.2 Background; 6.3 Related Work; 6.3.1 Image Forensics; 6.3.2 Video Forensics.
- 6.3.3 Detection of Double/Multiple Quantization6.3.4 Inter-frame Forgery Detection; 6.3.5 Video Deep Fakes and Their Detection; 6.4 Methodology; 6.4.1 Video Tampering Localization; 6.4.2 Tampering Detection; 6.5 Results; 6.5.1 Datasets and Experimental Setup; 6.5.2 Experiments and Results; 6.6 Conclusions and Future Work; References; 7 Verification of Web Videos Through Analysis of Their Online Context; 7.1 Introduction; 7.2 Related Work; 7.2.1 Journalistic Practices; 7.2.2 Machine Learning Approaches; 7.2.3 Verification Support; 7.3 Fake Video Corpus; 7.3.1 Dataset Collection and Overview.