---
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
    dtype: string
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': real
          '1': fake
  splits:
  - name: sd3
    num_bytes: 48619528217.1
    num_examples: 211676
  - name: sd2
    num_bytes: 29114150644.648
    num_examples: 182196
  - name: sdxl
    num_bytes: 41797503707.276
    num_examples: 200302
  - name: flux
    num_bytes: 35912899091.312
    num_examples: 208344
  download_size: 144545739362
  dataset_size: 155444081660.336
configs:
- config_name: default
  data_files:
  - split: sd3
    path: data/sd3-*
  - split: sd2
    path: data/sd2-*
  - split: sdxl
    path: data/sdxl-*
  - 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