license:apache-2.0 arxiv:2304.13023 region:us
---
license: apache-2.0
---
# Fake Image Dataset
Fake Image Dataset is now open-sourced at [huggingface (InfImagine Organization)](https://huggingface.co/datasets/InfImagine/FakeImageDataset/tree/main/ImageData/train). ↗ It consists of two folders, *ImageData* and *MetaData*. *ImageData* contains the compressed packages of the Fake Image Dataset, while *MetaData* contains the labeling information of the corresponding data indicating whether they are real or fake.

Sentry-Image is now open-sourced at [Sentry-Image (github repository)](https://github.com/Inf-imagine/Sentry) which provides the SOTA fake image detection models in [Sentry-Image Leaderboard](http://sentry.infimagine.com/) pretraining in [Fake Image Dataset](https://huggingface.co/datasets/InfImagine/FakeImageDataset/tree/main/ImageData/train) to detect whether the image provided is an AI-generated or real image.

## Why we need [Fake Image Dataset](https://huggingface.co/datasets/InfImagine/FakeImageDataset/tree/main/ImageData/train) and [Sentry-Image](http://sentry.infimagine.com/)?
* 🧐 Recent [study](https://arxiv.org/abs/2304.13023) have shown that humans struggle significantly to distinguish real photos from AI-generated ones, with a misclassification rate of **38.7%**.

* 🤗 To help people confirm whether the images they see are real images or AI-generated images, we launched the Sentry-Image project.

* 💻 Sentry-Image is an open source project which provides the SOTA fake image detection models in [Sentry-Image Leaderboard](http://sentry.infimagine.com/) to detect whether the image provided is an AI-generated or real image.


# Dataset card for Fake Image Dataset

## Dataset Description

* **Homepage:** [Sentry-Image](http://sentry.infimagine.com/)
* **Paper:** [https://arxiv.org/pdf/2304.13023.pdf](https://arxiv.org/pdf/2304.13023.pdf)
* **Point of Contact:** [contact@infimagine.com](mailto:contact@infimagine.com)

## How to Download
You can use following codes to download the dataset：
```shell
git lfs install
git clone https://huggingface.co/datasets/InfImagine/FakeImageDataset
```
You can use following codes to extract the files in each subfolder (take the *IF-CC95K* subfolder in ImageData/val/IF-CC95K as an example)：
```shell
cat IF-CC95K.tar.gz.* > IF-CC95K.tar.gz
tar -xvf IF-CC95K.tar.gz
```

## Dataset Summary

FakeImageDataset was created to serve as an large-scale dataset for the pretraining of detecting fake images.

It was built on StableDiffusion v1.5, IF and StyleGAN3.

## Supported Tasks and Leaderboards

FakeImageDataset is intended to be primarly used as a pretraining dataset for detecting fake images.

## Sub Dataset
### Training Dataset (Fake2M)

| Dataset     | SD-V1.5Real-dpms-25 | IF-V1.0-dpms++-25 | StyleGAN3     |
| :----------- | :-----------:       | :-----------:     | :-----------: |
| Generator   | Diffusion           | Diffusion         | GAN           |
| Numbers     | 1M                  | 1M                | 87K           |
| Resolution  | 512                 | 256               | (>=512)       |
| Caption     | CC3M-Train          | CC3M-Train        | -             |
| ImageData Path     | ImageData/train/SDv15R-CC1M | ImageData/train/IFv1-CC1M | ImageData/train/stylegan3-80K |
| MetaData Path     | MetaData/train/SDv15R-CC1M.csv          | MetaData/train/IF-CC1M.csv        | MetaData/train/stylegan3-80K.csv             |

### Validation Dataset (MPBench)

| Dataset     | SDv15               | SDv21             | IF            | Cogview2      | StyleGAN3     | Midjourneyv5  |
| :---------- | :-----------:       | :-----------:     | :-----------: | :-----------: | :-----------: | :-----------: |
| Generator   | Diffusion           | Diffusion         | Diffusion     | AR            | GAN           | -             |
| Numbers     | 30K                 | 15K               | 95K           | 22K           | 60K           | 5K            |
| Resolution  | 512                 | 512               | 256           | 480           | (>=512)       | (>=512)       |
| Caption     | CC15K-val           | CC15K-val         | CC15K-val     | CC15K-val     | -             | -             |
| ImageData Path | ImageData/val/SDv15-CC30K | ImageData/val/SDv21-CC15K | ImageData/val/IF-CC95K | ImageData/val/cogview2-22K | ImageData/val/stylegan3-60K | ImageData/val/Midjourneyv5-5K|
| MetaData Path  | MetaData/val/SDv15-CC30K.csv| MetaData/val/SDv21-CC15K.csv | MetaData/val/IF-CC95K.csv | MetaData/val/cogview2-22K.csv | MetaData/val/stylegan3-60K.csv | MetaData/val/Midjourneyv5-5K.csv |

# News
* [2023/07] We open source the [Sentry-Image repository](https://github.com/Inf-imagine/Sentry) and [Sentry-Image Demo & Leaderboard](http://sentry.infimagine.com/). 
* [2023/07] We open source the [Sentry-Image dataset](https://huggingface.co/datasets/InfImagine/FakeImageDataset).
Stay tuned for this project! Feel free to contact [contact@infimagine.com](contact@infimagine.com)! 😆

# License
This project is open-sourced under the [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0). These weights and datasets are fully open for academic research and can be used for commercial purposes with official written permission. If you find our open-source models and datasets useful for your business, we welcome your donation to support the development of the next-generation Sentry-Image model. Please contact [contact@infimagine.com](contact@infimagine.com) for commercial licensing and donation inquiries.

# Citation
The code and model in this repository is mostly developed for or derived from the paper below. Please cite it if you find the repository helpful.
```
@misc{sentry-image-leaderboard,
      title = {Sentry-Image Leaderboard},
      author = {Zeyu Lu, Di Huang, Chunli Zhang, Chengyue Wu, Xihui Liu, Lei Bai, Wanli Ouyang},
      year = {2023},
      publisher = {InfImagine, Shanghai AI Laboratory},
      howpublished = "\url{https://github.com/Inf-imagine/Sentry}"
},
@misc{lu2023seeing,
      title = {Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images}, 
      author = {Zeyu Lu, Di Huang, Lei Bai, Jingjing Qu, Chengyue Wu, Xihui Liu, Wanli Ouyang},
      year = {2023},
      eprint = {2304.13023},
      archivePrefix = {arXiv},
      primaryClass = {cs.AI}
}
```