Scalable misbehavior detection in online video chat services

Xinyu Xing, Yu Li Liang, Sui Huang, Hanqiang Cheng, Richard Han, Qin Lv, Xue Liu, Shivakant Mishra, Yi Zhu

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

7 Scopus citations

Abstract

The need for highly scalable and accurate detection and filtering of misbehaving users and obscene content in online video chat services has grown as the popularity of these services has exploded in popularity. This is a challenging problem because processing large amounts of video is compute intensive, decisions about whether a user is misbehaving or not must be made online and quickly, and moreover these video chats are characterized by low quality video, poorly lit scenes, diversity of users and their behaviors, diversity of the content, and typically short sessions. This paper presents EMeralD, a highly scalable system for accurately detecting and filtering misbehaving users in online video chat applications. EMeralD substantially improves upon the state-of-the-art filtering mechanisms by achieving much lower computational cost and higher accuracy. We demonstrate EMeralD's improvement via experimental evaluations on real-world data sets obtained from Chatroulette.com.

Original languageEnglish (US)
Title of host publicationKDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages552-560
Number of pages9
DOIs
StatePublished - Sep 14 2012
Event18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012 - Beijing, China
Duration: Aug 12 2012Aug 16 2012

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

Other18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012
CountryChina
CityBeijing
Period8/12/128/16/12

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

Fingerprint Dive into the research topics of 'Scalable misbehavior detection in online video chat services'. Together they form a unique fingerprint.

  • Cite this

    Xing, X., Liang, Y. L., Huang, S., Cheng, H., Han, R., Lv, Q., Liu, X., Mishra, S., & Zhu, Y. (2012). Scalable misbehavior detection in online video chat services. In KDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 552-560). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/2339530.2339619