Poster: Video Chat Scam Detection Leveraging Screen Light Reflection

Abstract

The rapid advancement of social media and communication technology enables video chat to become an important and convenient way of daily communication. However, such convenience also makes personal video clips easily obtained and exploited by malicious users who launch scam attacks. Existing studies only deal with the attacks that use fabricated facial masks, while the liveness detection that targets the playback attacks using a virtual camera is still elusive. In this work, we develop a novel video chat liveness detection system, which can track the weak light changes reflected off the skin of a human face leveraging chromatic eigenspace differences. We design an inconspicuous challenge frame with minimal intervention to the video chat and develop a robust anomaly frame detector to verify the liveness of remote user in a video chat session. Furthermore, we propose a resilient defense strategy to defeat both naive and intelligent playback attacks leveraging spatial and temporal verification. The evaluation results show that our system can achieve accurate and robust liveness detection with the accuracy and false detection rate as high as 97.7% (94.8%) and 1% (1.6%) on smartphones (laptops), respectively.