Deepfake-QA-10K-OPT

# **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是一款专为分析深度伪造图像与视频质量而设计的深度伪造质量评估模型，可判别深度伪造内容的质量优劣，具体标签规则如下：
- **0** 代表低质量深度伪造内容
- **1** 代表高质量深度伪造内容

该分类任务可作为深度伪造质量评估模型训练的核心基础，助力优化深度伪造检测与增强技术。

## 引用
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},
models_training = {@prithivMLmods},
dataset_curation_strategy = {@prithivMLmods},
dataset_curation = {Wildy AI团队}
}

搜集汇总

背景与挑战

背景概述

Deepfake-QA-10K-OPT是一个用于深度伪造质量评估的数据集，旨在分析深度伪造图像和视频的质量，通过0和1分别表示低质量与高质量的分类，以支持相关模型的训练和改进。