Efficient misbehaving user detection in online video chat services

  • Authors:

  • Hanqiang Cheng;Yu-Li Liang;Xinyu Xing;Xue Liu;Richard Han;Qin Lv;Shivakant Mishra

  • Affiliations:

  • McGill University, Montreal, PQ, Canada;University of Colorado at Boulder, Boulder, CO, USA;Georgia Institute of Technology, Atlanta, GA, USA;McGill University, Montreal, PQ, Canada;University of Colorado at Boulder, Boulder, CO, USA;University of Colorado at Boulder, Boulder, CO, USA;University of Colorado at Boul, Boulder, CO, USA

  • Venue:
  • Proceedings of the fifth ACM international conference on Web search and data mining
  • Year:
  • 2012

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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.