Interpretable Multimodal Deception Detection in Videos

Abstract

There are various real-world applications such as video ads, airport screenings, courtroom trials, and job interviews where deception detection can play a crucial role. Hence, there are immense demands on deception detection in videos. Videos contain rich information including acoustic, visual, temporal, and/or linguistic information, which provides great opportunities for advanced deception detection. However, videos are inherently complex; moreover, they lack detective labels in many real-world applications, which poses tremendous challenges to traditional deception detection. In this manuscript, I present my Ph.D. research on the problem of deception detection in videos. In particular, I provide a principled way to capture rich information into a coherent model and propose an end-to-end framework DEV to detect DEceptive Videos automatically. Preliminary results on real-world videos demonstrate the effectiveness of the proposed framework.