Efficient misbehaving user detection in online video chat services
Hanqiang Cheng, Yuli Liang, Xinyu Xing, Xue Liu, Richard Y. Han, Qin Lv, Shivakant Mishra · 2012
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.