Deception detection in videos

Abstract

This study explores a multi-modal approach for deception detection in videos, classi- fying videos as deceptive or truthful by using four modalities: video, audio, transcript, and micro-expressions. The video modality captures body language and gestures, while audio is used to analyze vocal variations such as pitch and stutters. Transcript data analyses linguistic patterns and semantic choices, while micro-expressions are used to detect subtle facial cues that may indicate underlying emotions. To integrate these diverse data sources, we investigate the use of techniques such as early and late fusion, alongside neural approaches such as multi-layer perceptrons and convolutional neural networks.