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Detecting Tampered Videos with Multimedia Forensics and Deep Learning

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MultiMedia Modeling (MMM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11295))

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Abstract

User-Generated Content (UGC) has become an integral part of the news reporting cycle. As a result, the need to verify videos collected from social media and Web sources is becoming increasingly important for news organisations. While video verification is attracting a lot of attention, there has been limited effort so far in applying video forensics to real-world data. In this work we present an approach for automatic video manipulation detection inspired by manual verification approaches. In a typical manual verification setting, video filter outputs are visually interpreted by human experts. We use two such forensics filters designed for manual verification, one based on Discrete Cosine Transform (DCT) coefficients and a second based on video requantization errors, and combine them with Deep Convolutional Neural Networks (CNN) designed for image classification. We compare the performance of the proposed approach to other works from the state of the art, and discover that, while competing approaches perform better when trained with videos from the same dataset, one of the proposed filters demonstrates superior performance in cross-dataset settings. We discuss the implications of our work and the limitations of the current experimental setup, and propose directions for future research in this area.

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Notes

  1. 1.

    While not all maps are technically the result of filtering, the term filters is widely used in the market and will also be used here.

  2. 2.

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

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Acknowledgements

This work is supported by the InVID project, which is funded by the European Commission’s Horizon 2020 program under contract number 687786.

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Authors and Affiliations

  1. Centre for Research and Technology Hellas, Thermi-Thessaloniki, Greece

    Markos Zampoglou, Foteini Markatopoulou, Despoina Touska, Evlampios Apostolidis, Symeon Papadopoulos, Vasileios Mezaris & Ioannis Kompatsiaris

  2. eXo maKina, Paris, France

    Gregoire Mercier & Roger Cozien

  3. School of EECS, Queen Mary University of London, London, UK

    Evlampios Apostolidis & Ioannis Patras

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

Correspondence to Markos Zampoglou .

Editor information

Editors and Affiliations

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

    Ioannis Kompatsiaris

  2. EURECOM, Sophia Antipolis, France

    Benoit Huet

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

    Vasileios Mezaris

  4. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  5. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

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

    Stefanos Vrochidis

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Zampoglou, M. et al. (2019). Detecting Tampered Videos with Multimedia Forensics and Deep Learning. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11295. Springer, Cham. https://doi.org/10.1007/978-3-030-05710-7_31

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  • DOI: https://doi.org/10.1007/978-3-030-05710-7_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05709-1

  • Online ISBN: 978-3-030-05710-7

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