Add OpenSDID+ dataset card

README.md

@@ -1,4 +1,21 @@
 ---
+pretty_name: OpenSDID+
+license: cc-by-sa-4.0
+task_categories:
+- image-classification
+- image-segmentation
+size_categories:
+- 100K<n<1M
+tags:
+- diffusion
+- image-generation
+- ai-generated-content
+- deepfake-detection
+- synthetic-image-detection
+- image-forensics
+- localization
+- opensdi
+- arxiv:2503.19653
 dataset_info:
   features:
   - name: key
@@ -40,3 +57,104 @@ configs:
   - split: flux
     path: data/flux-*
 ---
+
+# OpenSDID+
+
+OpenSDID+ is an extended release of the OpenSDI dataset. It complements the original SD1.5 training split with large-scale images from the remaining OpenSDI generators: SD2, SD3, SDXL, and FLUX.
+
+The dataset follows the OpenSDI challenge introduced in **"OpenSDI: Spotting Diffusion-Generated Images in the Open World"**. OpenSDI studies detection and localization of diffusion-generated images under realistic open-world settings, including diverse user intentions, evolving diffusion models, and both global and local image manipulations.
+
+## Relationship to OpenSDI
+
+The original OpenSDI release is split across:
+
+- [OpenSDI_train](https://huggingface.co/datasets/nebula/OpenSDI_train): SD1.5 training data
+- [OpenSDI_test](https://huggingface.co/datasets/nebula/OpenSDI_test): benchmark test data across OpenSDI generators
+
+This repository provides the expanded generator-specific data for SD2, SD3, SDXL, and FLUX. It is intended for researchers who want broader generator coverage beyond the original SD1.5 training split.
+
+## Dataset Details
+
+| Split | Generator family | Samples |
+| --- | --- | ---: |
+| `sd2` | Stable Diffusion 2.x | 182,196 |
+| `sd3` | Stable Diffusion 3 | 211,676 |
+| `sdxl` | Stable Diffusion XL | 200,302 |
+| `flux` | FLUX | 208,344 |
+| **Total** |  | **802,518** |
+
+Each example contains:
+
+- `key`: sample identifier
+- `image`: input image
+- `mask`: manipulation/localization mask
+- `label`: binary class label, where `0 = real` and `1 = fake`
+
+## Supported Tasks
+
+### Diffusion-Generated Image Detection
+
+Classify whether an image is real or diffusion-generated.
+
+### Manipulation Localization
+
+Use the mask annotations to evaluate or train pixel-level localization methods for locally manipulated images.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Choose one generator split: "sd2", "sd3", "sdxl", or "flux".
+ds = load_dataset("nebula/OpenSDIDplus", split="sdxl", streaming=True)
+sample = next(iter(ds))
+print(sample.keys())
+print(sample["label"])
+```
+
+For full-scale training, streaming or selective split download is recommended because the release is large.
+
+## Intended Uses
+
+OpenSDID+ is intended for academic research on:
+
+- AI-generated image detection
+- diffusion-generated image detection
+- image manipulation localization
+- open-world image forensics
+- generalization across unseen or evolving text-to-image generators
+
+## Limitations
+
+OpenSDID+ should be interpreted as an extension of the OpenSDI benchmark rather than a standalone replacement for the original release. The splits in this repository cover SD2, SD3, SDXL, and FLUX; use [OpenSDI_train](https://huggingface.co/datasets/nebula/OpenSDI_train) for the original SD1.5 training split.
+
+The dataset reflects the generator families, prompts, editing procedures, and image sources used in OpenSDI. Performance on OpenSDID+ may not fully predict robustness to future generators, post-processing pipelines, social media compression, or real-world distribution shifts.
+
+## Ethical Considerations
+
+This dataset is released for research on synthetic media detection and image forensics. Users should not use it to create deceptive content, identify private individuals, or deploy forensic systems without appropriate validation, fairness analysis, and legal review.
+
+Some generated or source images may contain sensitive or biased content inherited from public image and generative model ecosystems. Users should apply appropriate filtering and access controls where needed.
+
+## Citation
+
+If you use OpenSDID+ or the OpenSDI benchmark, please cite:
+
+```bibtex
+@InProceedings{Wang_2025_CVPR,
+  author    = {Wang, Yabin and Huang, Zhiwu and Hong, Xiaopeng},
+  title     = {OpenSDI: Spotting Diffusion-Generated Images in the Open World},
+  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  month     = {June},
+  year      = {2025},
+  pages     = {4291--4301}
+}
+```
+
+## Links
+
+- Paper: https://openaccess.thecvf.com/content/CVPR2025/html/Wang_OpenSDI_Spotting_Diffusion-Generated_Images_in_the_Open_World_CVPR_2025_paper.html
+- arXiv: https://arxiv.org/abs/2503.19653
+- Project page and code: https://github.com/iamwangyabin/OpenSDI
+- OpenSDI training data: https://huggingface.co/datasets/nebula/OpenSDI_train
+- OpenSDI test data: https://huggingface.co/datasets/nebula/OpenSDI_test