| --- |
| 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} |
| } |
| ``` |
| --- |
|
|
