  Automated systems that detect deception in high-stakes situations can enhance
societal well-being across medical, social work, and legal domains. Existing
models for detecting high-stakes deception in videos have been supervised, but
labeled datasets to train models can rarely be collected for most real-world
applications. To address this problem, we propose the first multimodal
unsupervised transfer learning approach that detects real-world, high-stakes
deception in videos without using high-stakes labels. Our subspace-alignment
(SA) approach adapts audio-visual representations of deception in
lab-controlled low-stakes scenarios to detect deception in real-world,
high-stakes situations. Our best unsupervised SA models outperform models
without SA, outperform human ability, and perform comparably to a number of
existing supervised models. Our research demonstrates the potential for
introducing subspace-based transfer learning to model high-stakes deception and
other social behaviors in real-world contexts with a scarcity of labeled
behavioral data.
