---
title: Deepfakes-QA-Leaning
canonical_url: "https://www.modelscope.cn/datasets/prithivMLmods/Deepfakes-QA-Leaning"
md_url: "https://www.modelscope.cn/datasets/prithivMLmods/Deepfakes-QA-Leaning.md"
repository: prithivMLmods/Deepfakes-QA-Leaning
last_updated: 2025-02-22
license: "Apache License 2.0"
storage_size: "3.0 GB"
downloads: 1182
stars: 0
---

# Deepfakes-QA-Leaning

> Deepfakes-QA-Leaning - prithivMLmods 在 ModelScope 开源的数据集。Deepfake Quality Assessment

prithivMLmods/Deepfakes-QA-Leaning 是 ModelScope 魔搭社区上的数据集，存储大小 3.0 GB，采用 Apache License 2.0 许可。

- **Repository**: prithivMLmods/Deepfakes-QA-Leaning
- **License**: Apache License 2.0
- **Storage size**: 3.0 GB
- **Downloads**: 1182
- **Stars**: 0
- **Last updated**: 2025-02-22

Source: https://www.modelscope.cn/datasets/prithivMLmods/Deepfakes-QA-Leaning

---

# **Deepfake Quality Assessment** 

Deepfake QA is a Deepfake Quality Assessment model designed to analyze the quality of deepfake images & videos. It evaluates whether a deepfake is of good or bad quality, where:  
- **0** represents a bad-quality deepfake  
- **1** represents a good-quality deepfake  

This classification serves as the foundation for training models on deepfake quality assessment, helping improve deepfake detection and enhancement techniques.  

## Citation  

```bibtex
@misc{deepfake_quality_assessment_2025,
  author = {Wildy AI Team Collaborations},
  title = {Deepfake Quality Assessment Models},
  year = {2025},
  note = {Early release},
  models_training = {@prithivMLmods},
  dataset_curation_strategy = {@prithivMLmods},
  dataset_curation = {Wildy AI Team}
}
```