# SVC 2025: the First Multimodal Deception Detection Challenge

arXiv: https://arxiv.org/abs/2508.04129 · HTML: https://mlsanity.com/paper/2508.04129/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Deception Detection in Videos](https://mlsanity.com/paper/1712.04415/) (2017)
- [Audio-Visual Deception Detection: DOLOS Dataset and Parameter-Efficient Crossmodal Learning](https://mlsanity.com/paper/2303.12745/) (2023)
- [Benchmarking Cross-Domain Audio-Visual Deception Detection](https://mlsanity.com/paper/2405.06995/) (2024)

## Built on by (most cited first, 5 of 5)

- [AV-Master: Dual-Path Comprehensive Perception Makes Better Audio-Visual Question Answering](https://mlsanity.com/paper/2510.18346/) (2025)
- [Text-guided Fine-Grained Video Anomaly Understanding](https://mlsanity.com/paper/2511.00524/) (2025)
- [Retrieving to Recover: Towards Incomplete Audio-Visual Question Answering via Semantic-consistent Purification](https://mlsanity.com/paper/2604.10695/) (2026)
- [Multi-source Multimodal Progressive Domain Adaption for Audio-Visual Deception Detection](https://mlsanity.com/paper/2508.12842/) (2025)
- [SVC 2026: the Second Multimodal Deception Detection Challenge and the First Domain Generalized Remote Physiological Measurement Challenge](https://mlsanity.com/paper/2604.05748/) (2026)

## Abstract (arXiv)

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.