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Detecting Manipulations in Video

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Video Verification in the Fake News Era

Abstract

This chapter presents the techniques researched and developed within InVID for the forensic analysis of videos, and the detection and localization of forgeries within User-Generated Videos (UGVs). Following an overview of state-of-the-art video tampering detection techniques, we observed that the bulk of current research is mainly dedicated to frame-based tampering analysis or encoding-based inconsistency characterization. We built upon this existing research, by designing forensics filters aimed to highlight any traces left behind by video tampering, with a focus on identifying disruptions in the temporal aspects of a video. As for many other data analysis domains, deep neural networks show very promising results in tampering detection as well. Thus, following the development of a number of analysis filters aimed to help human users in highlighting inconsistencies in video content, we proceeded to develop a deep learning approach aimed to analyze the outputs of these forensics filters and automatically detect tampered videos. In this chapter, we present our survey of the state of the art with respect to its relevance to the goals of InVID, the forensics filters we developed and their potential role in localizing video forgeries, as well as our deep learning approach for automatic tampering detection. We present experimental results on benchmark and real-world data, and analyze the results. We observe that the proposed method yields promising results compared to the state of the art, especially with respect to the algorithm’s ability to generalize to unknown data taken from the real world. We conclude with the research directions that our work in InVID has opened for the future.

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Notes

  1. 1.

    https://github.com/MKLab-ITI/image-forensics.

  2. 2.

    Video of errors: a video constructed by per-pixel differences of frames between the two videos.

  3. 3.

    https://fotoforensics.com/tutorial-ela.php.

  4. 4.

    https://www.nist.gov/itl/iad/mig/media-forensics-challenge-2018.

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Author information

Authors and Affiliations

  1. eXo maKina, Paris, France

    Grégoire Mercier & Roger Cozien

  2. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Foteini Markatopoulou, Markos Zampoglou, Evlampios Apostolidis, Alexandros I. Metsai, Symeon Papadopoulos, Vasileios Mezaris & Ioannis Kompatsiaris

  3. School of Electronic Engineering and Computer Science, Queen Mary University, London, UK

    Evlampios Apostolidis & Ioannis Patras

Authors
  1. Grégoire Mercier
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  2. Foteini Markatopoulou
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  3. Roger Cozien
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  4. Markos Zampoglou
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  5. Evlampios Apostolidis
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  6. Alexandros I. Metsai
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  7. Symeon Papadopoulos
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  8. Vasileios Mezaris
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  9. Ioannis Patras
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  10. Ioannis Kompatsiaris
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Corresponding author

Correspondence to Markos Zampoglou .

Editor information

Editors and Affiliations

  1. Centre for Research and Technology Hellas, Information Technologies Institute, Thermi, Thessaloniki, Greece

    Vasileios Mezaris

  2. MODUL Technology GmbH, MODUL University Vienna, Vienna, Austria

    Lyndon Nixon

  3. Centre for Research and Technology Hellas, Information Technologies Institute, Thermi, Thessaloniki, Greece

    Symeon Papadopoulos

  4. Agence France-Presse, Paris, France

    Denis Teyssou

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Mercier, G. et al. (2019). Detecting Manipulations in Video. In: Mezaris, V., Nixon, L., Papadopoulos, S., Teyssou, D. (eds) Video Verification in the Fake News Era. Springer, Cham. https://doi.org/10.1007/978-3-030-26752-0_6

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  • DOI: https://doi.org/10.1007/978-3-030-26752-0_6

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  • Online ISBN: 978-3-030-26752-0

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