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}
}
```

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

Deepfake QA 是一款面向深度伪造（Deepfake）图像与视频的质量评估模型，旨在分析其质量水平。该模型可判别深度伪造内容的质量优劣，具体规则如下：
- **0** 代表低质量深度伪造内容
- **1** 代表高质量深度伪造内容

该分类任务是开展深度伪造质量评估模型训练的核心基础，有助于优化深度伪造检测与增强技术。

## 引用

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}
}

搜集汇总

背景与挑战

背景概述

Deepfakes-QA-Leaning 是一个深度伪造质量评估模型，用于分析图像和视频的质量，通过二元分类（0表示差质量，1表示好质量）来支持检测和增强技术。该数据集基于Apache 2.0许可证发布，由Wildy AI Team在2025年贡献。