Merge `v1.0_updating` into `main`

README.md

@@ -3,6 +3,21 @@ license: cc-by-4.0
 task_categories:
 - image-classification
 pretty_name: Community Forensics
+configs:
+- config_name: default
+  data_files:
+    - split: Systematic
+      path:
+        - data/systematic/*.parquet
+    - split: Manual
+      path:
+        - data/manual/*.parquet
+    - split: PublicEval
+      path:
+        - data/publicEval/*.parquet
+    - split: Commercial
+      path:
+        - data/commercial/*.parquet
 tags:
 - image
 size_categories:
@@ -11,10 +26,13 @@ language:
 - en
 ---
 
-# Dataset Description
+# *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/)
 
-Currently working on formatting and uploading the data. Full dataset will be released in the coming weeks.
+*We are currently working on releasing a smaller version of our dataset paired with redistributable "real" data for easier prototyping.*
+
+*Changes:* \
+*04/09/25: Initial version released.*
 
 ## 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.
@@ -22,7 +40,6 @@ Currently working on formatting and uploading the data. Full dataset will be rel
 ## 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:
@@ -31,12 +48,15 @@ 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"
 }
 ```
 
@@ -44,17 +64,80 @@ Our dataset is formatted in a Parquet data frame of the following structure:
 `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.
+`split`: Train/test split. \
+`label`: Fake/Real label. (1: Fake, 0: Real)
 
 ## 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
+`Commercial`: Commercial models subset \
+`PublicEval`: Evaluation set where generated images are paired with COCO or FFHQ for license-compliant redistribution. Note that these are not the "source" datasets used to sample the generated images. 
+
+## Usage examples
+
+Default train/eval settings:
+```python
+import datasets as ds
+import PIL.Image as Image
+import io
+
+# default training set
+commfor_train = ds.load_dataset("OwensLab/CommunityForensics", split="Systematic+Manual", cache_dir="~/.cache/huggingface/datasets")
+commfor_eval = ds.load_dataset("OwensLab/CommunityForensics", split="PublicEval", cache_dir="~/.cache/huggingface/datasets")
+
+# optionally shuffle the dataset
+commfor_train = commfor_train.shuffle(seed=123, writer_batch_size=3000)
+
+for i, data in enumerate(commfor_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 2.2TB** (1.1TB data + 1.1TB re-indexed `arrow` files)
+- It is possible to randomly access data by passing an index (e.g., `commfor_train[10]`, `commfor_train[247]`).
+- It may be wise to 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
+
+# full data streaming
+commfor_train_stream = ds.load_dataset("OwensLab/CommunityForensics", split='Systematic+Manual', streaming=True)
+
+# streaming only the evaluation set
+commfor_eval_stream = ds.load_dataset("OwensLab/CommunityForensics", split='PublicEval', streaming=True)
+
+# streaming only 10% of training data. Note that this does not contain the full set of models!
+commfor_train_stream_10p = ds.load_dataset("OwensLab/CommunityForensics", split='Systematic[:10%]+Manual[:10%]', streaming=True)
+
+# optionally shuffle the streaming dataset
+commfor_train_stream_10p = commfor_train_stream_10p.shuffle(seed=123, buffer_size=3000)
+
+# usage example
+for i, data in enumerate(commfor_train_stream_10p):
+  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.
+
+### Training fake image classifiers
+For training a fake image classifier, it is necessary to pair the generated images with "real" images (here, "real" refers to images that are not generated by AI).
+In our [paper](https://arxiv.org/abs/2411.04125), we used 11 different image datasets: [LAION](https://laion.ai/), [ImageNet](https://www.image-net.org/), [COCO](https://cocodataset.org/), [FFHQ](https://github.com/NVlabs/ffhq-dataset), [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html), [MetFaces](https://github.com/NVlabs/metfaces-dataset), [AFHQ-v2](https://github.com/clovaai/stargan-v2/), [Forchheim](https://faui1-files.cs.fau.de/public/mmsec/datasets/fodb/), [IMD2020](https://staff.utia.cas.cz/novozada/db/), [Landscapes HQ](https://github.com/universome/alis), and [VISION](https://lesc.dinfo.unifi.it/VISION/), for sampling the generators and training the classifiers.
+To accurately reproduce our training settings, it is necessary to download all datasets and pair them with the generated images.
+We understand that this may be inconvenient for simple prototyping, and thus are also working on releasing a smaller subset of our dataset, paired with datasets with licenses that allow redistribution (e.g., COCO, FFHQ).
 
 # Dataset Creation
 ## Curation Rationale
@@ -81,7 +164,7 @@ The generative models are sourced from the community and may contain inappropria
 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.
+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](data/metadata) for detailed licensing information for your specific application.
 
 ## Citation Information
 ```

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