TY - GEN
T1 - Crowdsourcing-based Copyright Infringement Detection in Live Video Streams
AU - Zhang, Daniel Yue
AU - Li, Qi
AU - Tong, Herman
AU - Badilla, Jose
AU - Zhang, Yang
AU - Wang, Dong
N1 - Funding Information:
ACKNOWLEDGEMENT This research is supported in part by the National Science Foundation under Grant No. CBET-1637251, CNS-1566465 and IIS-1447795, Army Research Office under Grant W911NF-17-1-0409, Google 2018 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:
© 2018 IEEE.
PY - 2018/10/24
Y1 - 2018/10/24
N2 - With the increasing popularity of online video sharing platforms (such as YouTube and Twitch), the detection of content that infringes copyright has emerged as a new critical problem in online social media. In contrast to the traditional copyright detection problem that studies the static content (e.g., music, films, digital documents), this paper focuses on a much more challenging problem: One in which the content of interest is from live videos. We found that the state-of-the-art commercial copyright infringement detection systems, such as the ContentID from YouTube, did not solve this problem well: Large amounts of copyright-infringing videos bypass the detector while many legal videos are taken down by mistake. In addressing the copyright infringement detection problem for live videos, we identify several critical challenges: I) live streams are generated in real-time and the original copyright content from the owner may not be accessible; ii) streamers are getting more and more sophisticated in bypassing the copyright detection system (e.g., by modifying the title, tweaking the presentation of the video); iii) similar video descriptions and visual contents make it difficult to distinguish between legal streams and copyright-infringing ones. In this paper, we develop a crowdsourcing-based copyright infringement detection (CCID) scheme to address the above challenges by exploring a rich set of valuable clues from live chat messages. We evaluate CCID on two real world live video datasets collected from YouTube. The results show our scheme is significantly more effective and efficient than ContentID in detecting copyright-infringing live videos on YouTube.
AB - With the increasing popularity of online video sharing platforms (such as YouTube and Twitch), the detection of content that infringes copyright has emerged as a new critical problem in online social media. In contrast to the traditional copyright detection problem that studies the static content (e.g., music, films, digital documents), this paper focuses on a much more challenging problem: One in which the content of interest is from live videos. We found that the state-of-the-art commercial copyright infringement detection systems, such as the ContentID from YouTube, did not solve this problem well: Large amounts of copyright-infringing videos bypass the detector while many legal videos are taken down by mistake. In addressing the copyright infringement detection problem for live videos, we identify several critical challenges: I) live streams are generated in real-time and the original copyright content from the owner may not be accessible; ii) streamers are getting more and more sophisticated in bypassing the copyright detection system (e.g., by modifying the title, tweaking the presentation of the video); iii) similar video descriptions and visual contents make it difficult to distinguish between legal streams and copyright-infringing ones. In this paper, we develop a crowdsourcing-based copyright infringement detection (CCID) scheme to address the above challenges by exploring a rich set of valuable clues from live chat messages. We evaluate CCID on two real world live video datasets collected from YouTube. The results show our scheme is significantly more effective and efficient than ContentID in detecting copyright-infringing live videos on YouTube.
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U2 - 10.1109/ASONAM.2018.8508523
DO - 10.1109/ASONAM.2018.8508523
M3 - Conference contribution
AN - SCOPUS:85057294840
T3 - Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
SP - 367
EP - 374
BT - Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
A2 - Tagarelli, Andrea
A2 - Reddy, Chandan
A2 - Brandes, Ulrik
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
Y2 - 28 August 2018 through 31 August 2018
ER -