| | --- |
| | 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) and [openxlab](https://openxlab.org.cn/datasets/whlzy/FakeImageDataset/tree/main). ↗ 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. |
| |
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| | 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. |
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| |
|
| | # 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} |
| | } |
| | ``` |
