Deepfakes-QA-Patch2

# **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）**

深度伪造质量评估模型（下称Deepfake QA）是一款专为分析深度伪造（Deepfake）图像与视频质量而设计的模型，可对深度伪造内容的质量优劣进行评估，具体分类规则如下：
- **0** 代表劣质深度伪造内容
- **1** 代表优质深度伪造内容

该分类任务可作为深度伪造质量评估模型训练的基础，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 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-Patch2是一个用于评估深度伪造图像和视频质量的模型，采用二元分类（0表示质量差，1表示质量好），旨在支持深度伪造检测和增强技术的训练。该模型基于Apache 2.0许可协议，于2025年2月更新。