Deepfake-Unfiltered-30K


# **Deepfake-Unfiltered-30K**

Deepfake-Unfiltered-30K is a Deepfake Quality Assessment model dataset 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,
authors = {Wildy AI Team Collaborations, @prithivMLmods},
title = {Deepfake Quality Assessment Models},
year = {2025},
note = {April release},
models_training = {@prithivMLmods},
dataset_curation_strategy = {@prithivMLmods},
dataset_curation = {Wildy AI Team}
}
```


# **Deepfake-Unfiltered-30K**

Deepfake-Unfiltered-30K是一款面向深度伪造（Deepfake）质量评估的数据集，专为深度伪造图像与视频的质量分析设计。该数据集用于判定深度伪造样本的质量优劣，其中：
- **0** 代表劣质深度伪造样本
- **1** 代表优质深度伪造样本

该分类标签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,
authors = {Wildy AI Team Collaborations, @prithivMLmods},
title = {Deepfake Quality Assessment Models},
year = {2025},
note = {April 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

Deepfake-Unfiltered-30K是一个深度伪造质量评估数据集，用于分析图像和视频的质量，通过二元分类（0表示坏质量，1表示好质量）来支持模型训练，以提升深度伪造检测和增强技术的性能。