Misleading Metadata Detection on YouTube

  • Priyank Palod
  • Ayush Patwari
  • Sudhanshu Bahety
  • Saurabh Bagchi
  • Pawan GoyalEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11438)


YouTube is the leading social media platform for sharing videos. As a result, it is plagued with misleading content that includes staged videos presented as real footages from an incident, videos with misrepresented context and videos where audio/video content is morphed. We tackle the problem of detecting such misleading videos as a supervised classification task. We develop UCNet - a deep network to detect fake videos and perform our experiments on two datasets - VAVD created by us and publicly available FVC [8]. We achieve a macro averaged F-score of 0.82 while training and testing on a 70:30 split of FVC, while the baseline model scores 0.36. We find that the proposed model generalizes well when trained on one dataset and tested on the other.



This material is based in part upon work supported by a Google Faculty Award to Saurabh. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsor.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Priyank Palod
    • 1
  • Ayush Patwari
    • 2
  • Sudhanshu Bahety
    • 3
  • Saurabh Bagchi
    • 2
  • Pawan Goyal
    • 1
    Email author
  1. 1.IIT KharagpurKharagpurIndia
  2. 2.Purdue UniversityWest LafayetteUSA
  3. 3.Salesforce.comSan FranciscoUSA

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