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Detecting Tampered Videos with Multimedia Forensics and Deep Learning

  • Markos ZampoglouEmail author
  • Foteini Markatopoulou
  • Gregoire Mercier
  • Despoina Touska
  • Evlampios Apostolidis
  • Symeon Papadopoulos
  • Roger Cozien
  • Ioannis Patras
  • Vasileios Mezaris
  • Ioannis Kompatsiaris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11295)

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.

Keywords

Video forensics Video tampering detection Video verification Video manipulation detection User-generated video 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Markos Zampoglou
    • 1
    Email author
  • Foteini Markatopoulou
    • 1
  • Gregoire Mercier
    • 2
  • Despoina Touska
    • 1
  • Evlampios Apostolidis
    • 1
    • 3
  • Symeon Papadopoulos
    • 1
  • Roger Cozien
    • 2
  • Ioannis Patras
    • 3
  • Vasileios Mezaris
    • 1
  • Ioannis Kompatsiaris
    • 1
  1. 1.Centre for Research and Technology HellasThermi-ThessalonikiGreece
  2. 2.eXo maKinaParisFrance
  3. 3.School of EECSQueen Mary University of LondonLondonUK

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