Scholarlink

<p>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 (<a href="https://github.com/open-code-and-source/awesome-MMDD">https://github.com/open-code-and-source/awesome-MMDD</a>) is maintained alongside this survey, offering curated datasets and an awesome list of related works for MMDD.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multimodal Deception Detection: A Survey

  • 期刊论文

  • 发表日期：

    2026年4月8日

查看全文
  • Jiayu Zhang,
  • Xun Lin,
  • Jiajian Huang,
  • Shuo Ye,
  • Xiaobao Guo,
  • Dongliang Zhu,
  • Ruimin Hu,
  • Dan Guo,
  • Yanyan Liang,
  • Zitong Yu,
  • Xiaochun Cao

摘要

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.