Multimodal Deception Detection: A Survey

Deception detection is a critical yet challenging task in forensic analysis, security, and social interaction. The complexity of deceptive behaviors has motivated growing interest in multimodal deception detection (MMDD), which integrates diverse signals to improve reliability. This survey provides a comprehensive overview of recent advances in MMDD, covering research background, benchmark datasets, evaluation metrics, feature fusion methods, and deception detection architectures that have evolved from traditional machine learning to deep learning. We conclude with a discussion of existing challenges, future research directions, and the ethical issues involved. An open-source Github repository (https://github.com/open-code-and-source/awesome-MMDD) is maintained alongside this survey, offering curated datasets and an awesome list of related works for MMDD.