首个跨域多模态说谎检测挑战赛，检验模型在真实场景中的泛化能力。
SVC 2025: the First Multimodal Deception Detection Challenge
- 设计跨域多模态数据集，测试模型在不同场景下的说谎识别能力。
- 21支团队参与，验证了多模态融合对提升鲁棒性的重要性。
- 适合关注可信计算、AI安全与跨域泛化的研究者。
说谎检测在安全筛查、欺诈防范和可信度评估等实际应用中至关重要。尽管深度学习方法已展现出超越人类的表现，但其效果往往依赖于高质量且多样化的说谎样本。现有研究主要集中在单一领域，忽略了域偏移带来的性能下降。为此，我们提出SVC 2025多模态说谎检测挑战赛，这是一个用于评估音频-视觉说谎检测跨域泛化能力的新基准。参赛者需开发不仅在单个领域表现良好，还能在多个异构数据集间泛化的模型。通过利用包括音频、视频和文本在内的多模态数据，该挑战鼓励设计能够捕捉细微隐含欺骗线索的模型。本基准旨在推动更适应性强、可解释且可实际部署的说谎检测系统的发展，促进多模态学习领域的进步。研讨会结束时，共有21支队伍提交了最终结果。更多信息请访问：https://sites.google.com/view/svc-mm25。
原文摘要 · Abstract (English)
Deception detection is a critical task in real-world applications such as security screening, fraud prevention, and credibility assessment. While deep learning methods have shown promise in surpassing human-level performance, their effectiveness often depends on the availability of high-quality and diverse deception samples. Existing research predominantly focuses on single-domain scenarios, overlooking the significant performance degradation caused by domain shifts. To address this gap, we present the SVC 2025 Multimodal Deception Detection Challenge, a new benchmark designed to evaluate cross-domain generalization in audio-visual deception detection. Participants are required to develop models that not only perform well within individual domains but also generalize across multiple heterogeneous datasets. By leveraging multimodal data, including audio, video, and text, this challenge encourages the design of models capable of capturing subtle and implicit deceptive cues. Through this benchmark, we aim to foster the development of more adaptable, explainable, and practically deployable deception detection systems, advancing the broader field of multimodal learning. By the conclusion of the workshop competition, a total of 21 teams had submitted their final results. https://sites.google.com/view/svc-mm25 for more information.
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