Merge `updating_v1` into `main`

README.md

@@ -2,7 +2,168 @@
 license: cc-by-nc-sa-4.0
 task_categories:
 - image-classification
+pretty_name: Community Forensics (small)
+configs:
+- config_name: default
+  data_files:
+    - split: train
+      path:
+        - data/*.parquet
+tags:
+- image
+size_categories:
+- 100K<n<1M
 language:
 - en
-pretty_name: Community Forensics-small
----
+---
+
+# *Community Forensics: Using Thousands of Generators to Train Fake Image Detectors (CVPR 2025)*
+[Paper](https://arxiv.org/abs/2411.04125)/[Project Page](https://jespark.net/projects/2024/community_forensics/)
+
+This is a small version of the [Community Forensics dataset](https://huggingface.co/datasets/OwensLab/CommunityForensics). It contains roughly 11% of the generated images of the base dataset and are paired with real data with redistributable license. This dataset is intended for easier prototyping as you do not have to download the corresponding real datasets separately.
+
+We distribute this dataset with `cc-nc-by-sa-4.0` license for non-commercial research purposes only .
+
+Following table shows the performance (AP) difference between the classifier trained on the base dataset and this version of the dataset:
+
+| Version | GAN   | Lat. Diff. | Pix. Diff. | Commercial | Other  | Mean  |
+| :------ | :---: | :--------: | :--------: | :--------: | :----: | :---: |
+| Base    | 0.995 | 0.996      | 0.947      | 0.985      | 0.998  | 0.984 |
+| Small   | 0.986 | 0.995      | 0.888      | 0.852      | 0.993  | 0.943 |
+
+## Dataset Summary
+- The Community Forensics (small) dataset is intended for developing and benchmarking forensics methods that detect or analyze AI-generated images. It contains 278K generated images collected from 4803 generator models, and paired with 278K "real" images, sourced from [FFHQ](https://github.com/NVlabs/ffhq-dataset), [VISION](https://lesc.dinfo.unifi.it/VISION/), [COCO](https://cocodataset.org/), and [Landscapes HQ](https://github.com/universome/alis) datasets.
+
+## 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]", 
+  "mode": "RGB",
+  "image_data": "b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\..." 
+  "model_name": "stabilityai/stable-diffusion-2", 
+  "nsfw_flag": False,
+  "prompt": "montreal grand prix 2018 von icrdesigns",
+  "real_source": "LAION",
+  "subset": "Systematic",
+  "split": "train",
+  "label": "1",
+  "architecture": "LatDiff"
+}
+```
+
+## Data Fields
+`image_name`: Filename of an image. \
+`format`: PIL image format. \
+`resolution`: Image resolution. \
+`mode`: PIL image mode (e.g., RGB) \
+`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. \
+`nsfw_flag`: NSFW flag determined using [Stable Diffusion Safety Checker](https://huggingface.co/CompVis/stable-diffusion-safety-checker). \
+`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. \
+`label`: Fake/Real label. (1: Fake, 0: Real) \
+`architecture`: Architecture of the generative model that is used to generate this image. (Categories: `LatDiff`, `PixDiff`, `GAN`, `other`, `real`)
+
+## Data splits
+`train`: Default split containing the paired dataset (278K real and 278K generated images).
+
+## Usage examples
+
+Default train/eval settings:
+```python
+import datasets as ds
+import PIL.Image as Image
+import io
+
+# default training set
+commfor_small_train = ds.load_dataset("OwensLab/CommunityForensics-Small", split="train", cache_dir="~/.cache/huggingface/datasets")
+
+# optionally shuffle the dataset
+commfor_small_train = commfor_small_train.shuffle(seed=123, writer_batch_size=3000)
+
+for i, data in enumerate(commfor_small_train):
+  img, label = Image.open(io.BytesIO(data['image_data'])), data['label']
+  ## Your operations here ##
+  # e.g., img_torch = torchvision.transforms.functional.pil_to_tensor(img)
+```
+
+*Note:*
+- Downloading and indexing the data can take some time, but only for the first time. **Downloading may use up to ~600GB** (278GB data + 278GB re-indexed `arrow` files)
+- It is possible to randomly access data by passing an index (e.g., `commfor_small_train[10]`, `commfor_small_train[247]`).
+- You can set `cache_dir` to some other directory if your home directory is limited. By default, it will download data to `~/.cache/huggingface/datasets`. 
+
+It is also possible to use streaming for some use cases (e.g., downloading only a certain subset or a small portion of data).
+```python
+import datasets as ds
+import PIL.Image as Image
+import io
+
+# steaming only the systematic set. Note that when streaming, you can only load specific splits
+commfor_train_stream = ds.load_dataset("OwensLab/CommunityForensics-Small", split='train', streaming=True)
+
+# optionally shuffle the streaming dataset
+commfor_train_stream = commfor_train_stream.shuffle(seed=123, buffer_size=3000)
+
+# usage example
+for i, data in enumerate(commfor_train_stream):
+  if i>=10000: # use only first 10000 samples
+    break
+  img, label = Image.open(io.BytesIO(data['image_data'])), data['label']
+  ## Your operations here ##
+  # e.g., img_torch = torchvision.transforms.functional.pil_to_tensor(img)
+  
+```
+Please check [Hugging Face documentation](https://huggingface.co/docs/datasets/v3.5.0/loading#slice-splits) for more usage examples.
+
+
+# Below is the dataset card of the base dataset with minor modifications.
+
+# 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.
+
+This is the "small" version of the dataset which contains approximately 11% of the base dataset (278K generated images) which are then paired with 278K "real" images for easier prototyping.
+
+## 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, and 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-nc-sa-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](https://huggingface.co/datasets/OwensLab/CommunityForensics/tree/main/data/metadata) for detailed licensing information for your specific application.
+
+## Citation Information
+```
+@InProceedings{Park_2025_CVPR,
+    author    = {Park, Jeongsoo and Owens, Andrew},
+    title     = {Community Forensics: Using Thousands of Generators to Train Fake Image Detectors},
+    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
+    month     = {June},
+    year      = {2025},
+    pages     = {8245-8257}
+}
+```

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