StreamGuard: A Bayesian Network Approach to Copyright Infringement Detection Problem in Large-scale Live Video Sharing Systems

Daniel Yue Zhang, Lixing Song, Qi Li, Yang Zhang, Dong Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Copyright infringement detection is a critical problem in large-scale online video sharing systems: the copyright-infringing videos must be correctly identified and removed from the system to protect the copyright of the content owners. This paper focuses on a challenging problem of detecting copyright infringement in live video streams. The problem is particularly difficult because i) streamers can be sophisticated and modify the title or tweak the presentation of the video to bypass the detection system; ii) legal videos and copyright-infringing ones may have very similar visual content and descriptions. We found current commercial copyright detection systems did not address this problem well: a large amount of copyrighted content bypasses the detection system while legal streams are taken down by mistake. In this paper, we develop the StreamGuard, an unsupervised Bayesian network based copyright infringement detection system that addresses the above challenges by leveraging live chat messages from the audience. We evaluate StreamGuard on real-world live video streams collected from YouTube. The results show that StreamGuard is effective and efficient in identifying the copyright-infringing videos.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
EditorsYang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages901-910
Number of pages10
ISBN (Electronic)9781538650356
DOIs
StatePublished - Jan 22 2019
Externally publishedYes
Event2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
Duration: Dec 10 2018Dec 13 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

Conference

Conference2018 IEEE International Conference on Big Data, Big Data 2018
Country/TerritoryUnited States
CitySeattle
Period12/10/1812/13/18

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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