Toward End-to-End 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. However, videos are inherently complex; moreover, they lack detective labels in many real-world applications, which poses tremendous challenges to traditional deception detection methods. In this paper, we study the problem of deception detection in videos. In particular, we 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, which is robust to the small number of training data. Experimental results on real-world videos demonstrate the effectiveness of the proposed framework and further experiments are conducted to understand important factors of deception detection in videos.

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