---
title: AI-vs-Deepfake-vs-Real
canonical_url: "https://www.modelscope.cn/datasets/prithivMLmods/AI-vs-Deepfake-vs-Real"
md_url: "https://www.modelscope.cn/datasets/prithivMLmods/AI-vs-Deepfake-vs-Real.md"
repository: prithivMLmods/AI-vs-Deepfake-vs-Real
last_updated: 2025-02-23
license: "Apache License 2.0"
storage_size: "1.8 GB"
downloads: 1192
stars: 1
---

# AI-vs-Deepfake-vs-Real

> AI-vs-Deepfake-vs-Real - prithivMLmods 在 ModelScope 开源的数据集。AI vs Deepfake vs Real

prithivMLmods/AI-vs-Deepfake-vs-Real 是 ModelScope 魔搭社区上的数据集，存储大小 1.8 GB，采用 Apache License 2.0 许可。

- **Repository**: prithivMLmods/AI-vs-Deepfake-vs-Real
- **License**: Apache License 2.0
- **Storage size**: 1.8 GB
- **Downloads**: 1192
- **Stars**: 1
- **Last updated**: 2025-02-23

Source: https://www.modelscope.cn/datasets/prithivMLmods/AI-vs-Deepfake-vs-Real

---

# **AI vs Deepfake vs Real**  

**AI vs Deepfake vs Real** is a dataset designed for image classification, distinguishing between artificial, deepfake, and real images. This dataset includes a diverse collection of high-quality images to enhance classification accuracy and improve the model’s overall efficiency. By providing a well-balanced dataset, it aims to support the development of more robust AI-generated and deepfake detection models.  

# **Label Mappings**  
- **Mapping of IDs to Labels:** `{0: 'Artificial', 1: 'Deepfake', 2: 'Real'}`  
- **Mapping of Labels to IDs:** `{'Artificial': 0, 'Deepfake': 1, 'Real': 2}`  

This dataset serves as a valuable resource for training, evaluating, and benchmarking AI models in the field of deepfake and AI-generated image detection.  

# **Dataset Composition**  

The **AI vs Deepfake vs Real** dataset is composed of modular subsets derived from the following datasets:  

- [open-image-preferences-v1](https://huggingface.co/datasets/data-is-better-together/open-image-preferences-v1)
- [Deepfakes-QA-Patch1](https://huggingface.co/datasets/prithivMLmods/Deepfakes-QA-Patch1)  
- [Deepfakes-QA-Patch2](https://huggingface.co/datasets/prithivMLmods/Deepfakes-QA-Patch2)  

The dataset is evenly distributed across three categories:  
- **Artificial** (33.3%)  
- **Deepfake** (33.3%)  
- **Real** (33.3%)  

With a total of **9,999 entries**, this balanced distribution ensures better generalization and improved robustness in distinguishing between AI-generated, deepfake, and real images.