  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/
