InfImagine/FakeImageDataset

Fake Image Dataset

Fake Image Dataset is now open-sourced at huggingface (InfImagine Organization) and openxlab. ↗ 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) which provides the SOTA fake image detection models in Sentry-Image Leaderboard pretraining in Fake Image Dataset to detect whether the image provided is an AI-generated or real image.

Why we need Fake Image Dataset and Sentry-Image?

• 🧐 Recent study 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 to detect whether the image provided is an AI-generated or real image.

Dataset card for Fake Image Dataset

How to Download

You can use following codes to download the dataset：

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)：

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 and Sentry-Image Demo & Leaderboard.
• [2023/07] We open source the Sentry-Image dataset. Stay tuned for this project! Feel free to contact [email protected]! 😆

License

This project is open-sourced under the Apache-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 [email protected] 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}
}