---
license: mit
task_categories:
- text-to-audio
language:
- en
tags:
- audio
- environmental sound
- deepfake
- TTA
- V2A
size_categories:
- 100K<n<1M
---
# Dataset Card for VCapAV

VCapAV is a large-scale audio-visual deepfake detection dataset focused on **non-speech environmental sounds**. It introduces new multimodal deepfake scenarios using both **Text-to-Audio (TTA)** and **Video-to-Audio (V2A)** pipelines, together with **Text-to-Video (TTV)** synthesis.  
The dataset contains **90,990 clips, totaling 252.75 hours**, and supports audio-only, visual-only, and audio-visual detection tasks.

### Dataset Description

VCapAV addresses the lack of multimodal deepfake data involving environmental sounds. Unlike existing datasets focused on speech or face-centric manipulations, VCapAV introduces a comprehensive set of environmental audio generation methods and high-fidelity video forgeries.

- **Curated by:** Duke Kunshan University, University of Yamanashi, Wuhan University  
- **Funded by:** DKU Foundation Project “Emerging AI Technologies for Natural Language Processing”  
- **Shared by:** Authors of the VCapAV paper  
- **Language(s):** English (captions)  
- **License:** MIT License  

### Dataset Sources

- **Repository:** https://github.com/wailywang/VCapAV/
- **Paper:** [*VCapAV: A Video-Caption Based Audio-Visual Deepfake Detection Dataset*](https://www.isca-archive.org/interspeech_2025/wang25q_interspeech.html)
- **Demo:** https://vcapav.github.io/

### Dataset Uses

- Audio anti-spoofing research  
- Audio-visual deepfake detection  
- Evaluation of general-purpose audio generation methods  
- Studying modality consistency between vision and sound  
- Research on multimodal synchronization, scene-aware generation, and cross-modal alignment

### Dataset Creation

Most deepfake datasets focus on speech or human faces. VCapAV fills this gap by focusing on **general environmental audio** and **video–audio consistency**, enabling research on non-speech deepfake detection.

The dataset is constructed from a subset of **VGGSound** (15,446 videos).

### Citation

```bibtex
@inproceedings{wang2025vcapav,
  title={VCapAV: A Video-Caption Based Audio-Visual Deepfake Detection Dataset},
  author={Wang, Yuxi and Wang, Yikang and Zhang, Qishan and Nishizaki, Hiromitsu and Li, Ming},
  booktitle={Interspeech},
  year={2025}
}
```
---