SafeVchat: Detecting Obscene Content and Misbehaving Users in Online Video Chat Services

TL;DR

SafeVchat is a system that detects obscene content and misbehaving users in online video chat services using advanced image detection and data fusion techniques, improving accuracy and reliability.

Contribution

The paper introduces a novel motion-based skin detection method and a Dempster-Shafer Theory-based fusion approach for flasher detection in online video chats.

Findings

Higher recall and better precision in skin detection.

Effective detection of misbehaving users in real-world data.

Improved classification accuracy over existing methods.

Abstract

Online video chat services such as Chatroulette, Omegle, and vChatter that randomly match pairs of users in video chat sessions are fast becoming very popular, with over a million users per month in the case of Chatroulette. A key problem encountered in such systems is the presence of flashers and obscene content. This problem is especially acute given the presence of underage minors in such systems. This paper presents SafeVchat, a novel solution to the problem of flasher detection that employs an array of image detection algorithms. A key contribution of the paper concerns how the results of the individual detectors are fused together into an overall decision classifying the user as misbehaving or not, based on Dempster-Shafer Theory. The paper introduces a novel, motion-based skin detection method that achieves significantly higher recall and better precision. The proposed methods have been evaluated over real world data and image traces obtained from Chatroulette.com.