Update README.md

README.md

@@ -7,7 +7,91 @@ tags:
 - image
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 - 1M<n<10M
+language:
+- en
 ---
+
+# Dataset Description
 [Paper](https://arxiv.org/abs/2411.04125)/[Project Page](https://jespark.net/projects/2024/community_forensics/)
 
-Currently working on formatting and uploading the data. Full dataset will be released in the coming weeks.
+Currently working on formatting and uploading the data. Full dataset will be released in the coming weeks.
+
+## Dataset Summary
+- The Community Forensics dataset is a dataset intended for developing and benchmarking forensics methods that detect or analyze AI-generated images. It contains 2.7M generated images collected from 4803 generator models.
+
+## Supported Tasks
+- Image Classification: identify whether the given image is AI-generated. We mainly study this task in our paper, but other tasks may be possible with our dataset.
+
+
+# Dataset Structure
+## Data Instances
+Our dataset is formatted in a Parquet data frame of the following structure:
+```
+{
+  "image_name": "00000162.png", 
+  "format": "PNG",
+  "resolution": "[512, 512]", 
+  "image_data": "b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\..." 
+  "model_name": "stabilityai/stable-diffusion-2", 
+  "prompt": "montreal grand prix 2018 von icrdesigns",
+  "real_source": "LAION",
+  "subset": "Systematic",
+  "split": "train",
+}
+```
+
+## Data Fields
+`image_name`: Filename of an image. \
+`format`: PIL image format. \
+`resolution`: Image resolution. \
+`image_data`: Image data in byte format. Can be read using Python's BytesIO. \
+`model_name`: Name of the model used to sample this image. Has format {author_name}/{model_name} for `Systematic` subset, and {model_name} for other subsets. \
+`prompt`: Input prompt (if exists). \
+`real_source`: Paired real dataset(s) that was used to source the prompts or to train the generators. \
+`subset`: Denotes which subset the image belongs to (Systematic: Hugging Face models, Manual: manually downloaded models, Commercial: commercial models). \
+`split`: Train/test split.
+
+## Data splits
+`Systematic`: Systematically downloaded subset of the data (data downloaded from Hugging Face via automatic pipeline) \
+`Manual`: Manually downloaded subset of the data \
+`Commercial`: Commercial models subset
+
+# Dataset Creation
+## Curation Rationale
+This dataset is created to address the limited model diversity of the existing datasets for generated image detection. While some existing datasets contain millions of images, they are typically sampled from handful of generator models. We instead sample 2.7M images from 4803 generator models, approximately 34 times more generators than the most extensive previous dataset that we are aware of.
+
+## Collection Methodology
+We collect generators in three different subgroups. (1) We systematically download and sample open source latent diffusion models from Hugging Face. (2) We manually sample open source generators with various architectures and training procedures. (3) We sample from both open and closed commercially available generators.
+
+## Personal and Sensitive Information
+The dataset does not contain any sensitive identifying information (i.e., does not contain data that reveals information such as racial or ethnic origin, sexual orientation, religious or political beliefs).
+
+# Considerations of Using the Data
+## Social Impact of Dataset
+This dataset may be useful for researchers in developing and benchmarking forensics methods. Such methods may aid users in better understanding the given image. However, we believe the classifiers, at least the ones that we have trained or benchmarked, still show far too high error rates to be used directly in the wild which can lead to unwanted consequences (e.g., falsely accusing an author of creating fake images or allowing generated content to be certified as real).
+
+## Discussion of Biases
+The dataset has been primarily sampled from LAION captions. This may introduce biases that could be present in web-scale data (e.g., favoring human photos instead of other categories of photos). In addition, a vast majority of the generators we collect are derivatives of Stable Diffusion, which may introduce bias towards detecting certain types of generators.
+
+## Other Known Limitations
+The generative models are sourced from the community and may contain inappropriate content. While in many contexts it is important to detect such images, these generated images may require further scrutiny before being used in other downstream applications.
+
+# Additional Information
+## Acknowledgement
+We thank the creators of the many open source models that we used to collect the Community Forensics dataset. We thank Chenhao Zheng, Cameron Johnson, Matthias Kirchner, Daniel Geng, Ziyang Chen, Ayush Shrivastava, Yiming Dou, Chao Feng, Xuanchen Lu, Zihao Wei, Zixuan Pan, Inbum Park, Rohit Banerjee, and Ang Cao for the valuable discussions and feedback. This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001120C0123.
+
+## Licensing Information
+We release the dataset with a `cc-by-4.0` license for research purposes only. In addition, we note that each image in this dataset has been generated by the models with their respective licenses. We therefore provide metadata of all models present in our dataset with their license information. A vast majority of the generators use the [CreativeML OpenRAIL-M license](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE). Please refer to the metadata for detailed licensing information for your specific application.
+
+## Citation Information
+```
+@misc{park2024communityforensics,
+    title={Community Forensics: Using Thousands of Generators to Train Fake Image Detectors}, 
+    author={Jeongsoo Park and Andrew Owens},
+    year={2024},
+    eprint={2411.04125},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV},
+    url={https://arxiv.org/abs/2411.04125}, 
+}
+```