Deepfakes-QA-Patch1

# **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

If you use our dataset or models, please cite:

```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}
}
```

# **深度伪造质量评估（Deepfake Quality Assessment）**

深度伪造质量评估（Deepfake QA，简称Deepfake QA）是一款专为分析深度伪造（Deepfake）图像与视频质量而研发的模型，可对深度伪造内容的质量优劣进行评判，评判规则如下：
- **0** 代表劣质深度伪造内容
- **1** 代表优质深度伪造内容

该分类任务是深度伪造质量评估模型训练的核心基础，可为深度伪造检测与增强技术的优化提供有力支撑。

## 引用

若使用本数据集或模型，请引用如下文献：

bibtex
@misc{deepfake_quality_assessment_2025,
author = {Wildy AI团队协作组},
title = {深度伪造质量评估模型},
year = {2025},
note = {早期发布版本},
models_training = {@prithivMLmods},
dataset_curation_strategy = {@prithivMLmods},
dataset_curation = {Wildy AI团队}
}