Most work on automated deception detection (ADD) in video has two restrictions: (i) it focuses on a video of one person, and (ii) it focuses on a single act of deception in a one or two minute video. In this paper, we propose a new ADD framework which captures long term deception in a group setting. We study deception in the well-known Resistance game (like Mafia and Werewolf) which consists of 5-8 players of whom 2-3 are spies. Spies are deceptive throughout the game (typically 30-65 minutes) to keep their identity hidden. We develop an ensemble predictive model to identify spies in Resistance videos. We show that features from low-level and high-level video analysis are insufficient, but when combined with a new class of features that we call LiarRank, produce the best results. We achieve AUCs of over 0.70 in a fully automated setting. Our demo can be found at http://home.cs.dartmouth.edu/~mbolonkin/scan/demo/
Cite
@article{arxiv.1905.08617,
title = {Automatic Long-Term Deception Detection in Group Interaction Videos},
author = {Chongyang Bai and Maksim Bolonkin and Judee Burgoon and Chao Chen and Norah Dunbar and Bharat Singh and V. S. Subrahmanian and Zhe Wu},
journal= {arXiv preprint arXiv:1905.08617},
year = {2019}
}