@inproceedings{f8663dfb3a704bd49ead8ca6c386ae5f,
title = "StreamGuard: A Bayesian Network Approach to Copyright Infringement Detection Problem in Large-scale Live Video Sharing Systems",
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.",
author = "Zhang, {Daniel Yue} and Lixing Song and Qi Li and Yang Zhang and Dong Wang",
note = "Funding Information: This research is supported in part by the National Science Foundation under Grant No. CNS-1831669, CBET-1637251, CNS-1566465, and IIS-1447795, Army Research Office under Grant W911NF-17-1-0409, Google 2017 Faculty Research Award. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Big Data, Big Data 2018 ; Conference date: 10-12-2018 Through 13-12-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/BigData.2018.8622306",
language = "English (US)",
series = "Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "901--910",
editor = "Naoki Abe and Huan Liu and Calton Pu and Xiaohua Hu and Nesreen Ahmed and Mu Qiao and Yang Song and Donald Kossmann and Bing Liu and Kisung Lee and Jiliang Tang and Jingrui He and Jeffrey Saltz",
booktitle = "Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018",
address = "United States",
}