Paper Info
Title
Scalable misbehavior detection in online video chat services
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.
Year
DOI
Venue
2012
10.1145/2339530.2339619
KDD
Keywords
Field
DocType
online video chat service,misbehaving user,video chat,computational cost,challenging problem,scalable system,obscene content,low quality video,accurate detection,scalable misbehavior detection,online video chat application
World Wide Web,Computer science,Popularity,Filter (signal processing),Scalable system,Online video,Multimedia,Scalability
Conference
Citations 
PageRank 
References 
5
0.57
13
Authors
9
Name
Order
Citations
PageRank
Xinyu Xing137035.71
Yu-Li Liang2242.69
Sui Huang3161.58
Hanqiang Cheng4293.48
Richard Han52771200.83
Lv Qin6111691.95
Xue Liu73058193.41
shivakant mishra81521138.23
Yi Zhu993.12