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arXiv · 2508.04129

SVC 2025: the First Multimodal Deception Detection Challenge

Xun Lin·Xiaobao Guo·Taorui Wang·Yingjie Ma·Jiajian Huang·Jiayu Zhang·Junzhe Cao·Zitong Yu
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Abstract

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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Xun Lin, Xiaobao Guo, Taorui Wang, Yingjie Ma, Jiajian Huang, Jiayu Zhang, Junzhe Cao, Zitong Yu. 2025-08-06. SVC 2025: the First Multimodal Deception Detection Challenge. https://arxiv.org/abs/2508.04129

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