TY - GEN
T1 - Scalable misbehavior detection in online video chat services
AU - Xing, Xinyu
AU - Liang, Yu Li
AU - Huang, Sui
AU - Cheng, Hanqiang
AU - Han, Richard
AU - Lv, Qin
AU - Liu, Xue
AU - Mishra, Shivakant
AU - Zhu, Yi
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - misbehavior detection
KW - online video chat
KW - video safety
UR - http://www.scopus.com/inward/record.url?scp=84866038977&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866038977&partnerID=8YFLogxK
U2 - 10.1145/2339530.2339619
DO - 10.1145/2339530.2339619
M3 - Conference contribution
AN - SCOPUS:84866038977
SN - 9781450314626
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 552
EP - 560
BT - KDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
T2 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012
Y2 - 12 August 2012 through 16 August 2012
ER -