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Video Tampering Detection for Decentralized Video Transcoding Networks

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12131)

Abstract

This paper introduces a complete methodology based on Machine Learning and Computer Vision techniques for the verification of video transcoding computations in decentralized networks, particularly the Open Source project Livepeer. A base video dataset is presented, with over 180k samples transcoded using the x264 codec. As a novelty, we propose a set of four features computed as a full reference comparison between the source and the rendered videos. Using these features, a One Class Support Vector Machine is trained to identify good encodings with a high accuracy. Experimental results are presented and the particular constraints of this use case are explained.

Keywords

Decentralized networks Novelty detection Computer vision feature extraction 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.HaivisionMontrealCanada
  2. 2.LivepeerNew YorkUSA

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