Paper Info
Title
Efficient misbehaving user detection in online video chat services
Abstract
Online video chat services, such as Chatroulette, Omegle, and vChatter are becoming increasingly popular and have attracted millions of users. One critical problem encountered in such applications is the presence of misbehaving users ("flashers") and obscene content. Automatically filtering out obscene content from these systems in an efficient manner poses a difficult challenge. This paper presents a novel Fine-Grained Cascaded (FGC) classification solution that significantly speeds up the compute-intensive process of classifying misbehaving users by dividing image feature extraction into multiple stages and filtering out easily classified images in earlier stages, thus saving unnecessary computation costs of feature extraction in later stages. Our work is further enhanced by integrating new webcam-related contextual information (illumination and color) into the classification process, and a 2-stage soft margin SVM algorithm for combining multiple features. Evaluation results using real-world data set obtained from Chatroulette show that the proposed FGC based classification solution significantly outperforms state-of-the-art techniques.
Year
DOI
Venue
2012
10.1145/2124295.2124301
WSDM
Keywords
Field
DocType
online video chat service,efficient misbehaving user detection,compute-intensive process,misbehaving user,feature extraction,chatroulette show,image feature extraction,classification process,multiple stage,obscene content,classification solution,multiple feature,image features,performance,design
Data mining,Contextual information,Computer science,Support vector machine,Filter (signal processing),Feature extraction,Artificial intelligence,Online video,Machine learning,Computation
Conference
Citations 
PageRank 
References 
8
0.77
25
Authors
7
Name
Order
Citations
PageRank
Hanqiang Cheng1293.48
Yu-Li Liang2242.69
Xinyu Xing337035.71
Xue Liu43058193.41
Richard Han52771200.83
Lv Qin6111691.95
shivakant mishra71521138.23