arXiv 2603.260642026-04-05cs.CVcs.AI

MuDD：一种多模态欺骗检测数据集和GSR引导的渐进性知识蒸馏用于非接触欺骗检测

MuDD: A Multimodal Deception Detection Dataset and GSR-Guided Progressive Distillation for Non-Contact Deception Detection

School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；School of Information and Software Engineering, University of Electronic Science and Technology of China（电子科技大学信息与软件工程学院）

Peiyuan Jiang, Yao Liu, Yanglei Gan, Jiaye Yang, Lu Liu, Daibing Yao, Qiao Liu

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AI总结：

本文提出MuDD多模态欺骗检测数据集及GSR引导的渐进蒸馏方法，通过跨模态知识蒸馏提升非接触欺骗检测性能，实验表明其在欺骗检测和隐藏数字识别上达到最优。

摘要：

非接触自动欺骗检测仍具挑战性，因视觉和听觉欺骗线索缺乏稳定的跨受试者模式。相比之下，皮肤电反应（GSR）提供更可靠的生理线索，并广泛用于接触式欺骗检测。本文利用GSR中稳定的欺骗相关知识，通过跨模态知识蒸馏指导非接触模态的表示学习。然而，缺乏合适的数据集是主要障碍。为此，我们引入MuDD，包含130名参与者690分钟的记录，包含视频、音频、GSR、脉搏波容积图、心率和人格特征，支持更广泛的人类欺骗研究。基于此数据集，我们提出GSR引导的渐进蒸馏（GPD），一种跨模态蒸馏框架，用于缓解GSR与非接触信号之间大模态差异导致的负迁移。GPD的核心创新是整合渐进的特征级和数字级蒸馏与动态路由，使模型在训练中自适应决定如何转移教师知识，从而实现更稳定的跨模态知识转移。大量实验和可视化表明，GPD优于现有方法，在欺骗检测和隐藏数字识别上达到最优性能。

英文摘要：

Non-contact automatic deception detection remains challenging because visual and auditory deception cues often lack stable cross-subject patterns. In contrast, galvanic skin response (GSR) provides more reliable physiological cues and has been widely used in contact-based deception detection. In this work, we leverage stable deception-related knowledge in GSR to guide representation learning in non-contact modalities through cross-modal knowledge distillation. A key obstacle, however, is the lack of a suitable dataset for this setting. To address this, we introduce MuDD, a large-scale Multimodal Deception Detection dataset containing recordings from 130 participants over 690 minutes. In addition to video, audio, and GSR, MuDD also provides Photoplethysmography, heart rate, and personality traits, supporting broader scientific studies of deception. Based on this dataset, we propose GSR-guided Progressive Distillation (GPD), a cross-modal distillation framework for mitigating the negative transfer caused by the large modality mismatch between GSR and non-contact signals. The core innovation of GPD is the integration of progressive feature-level and digit-level distillation with dynamic routing, which allows the model to adaptively determine how teacher knowledge should be transferred during training, leading to more stable cross-modal knowledge transfer. Extensive experiments and visualizations show that GPD outperforms existing methods and achieves state-of-the-art performance on both deception detection and concealed-digit identification.

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