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---
license: cc-by-nc-4.0
task_categories:
- image-classification
- text-to-image
library_name: datasets
tags:
- ai-generated-content
- image-quality-assessment
- real-vs-fake
- multimodal
---
# DANI: Discrepancy Assessing for Natural and AI Images
[Paper: D-Judge: How Far Are We? Evaluating the Discrepancies Between AI-synthesized Images and Natural Images through Multimodal Guidance](https://huggingface.co/papers/2412.17632)
[Code: https://github.com/RenyangLiu/DJudge](https://github.com/RenyangLiu/DJudge)
**A Large-Scale Dataset for Visual Research on AI-Synthesized and Natural Images**
## Overview
DANI (Discrepancy Assessing for Natural and AI Images) is a large-scale, multimodal dataset for benchmarking and broad visual research on both AI-generated images (AIGIs) and natural images.
The dataset is designed to support a wide range of computer vision and multimodal research tasks, including but not limited to:
- AI-generated vs. real image discrimination
- Representation learning
- Image quality assessment
- Style transfer
- Image reconstruction
- Domain adaptation
- Multimodal understanding and beyond
DANI accompanies the paper:
> Liu, Renyang; Lyu, Ziyu; Zhou, Wei; Ng, See-Kiong.
> *D-Judge: How Far Are We? Assessing the Discrepancies Between AI-synthesized Images and Natural Images through Multimodal Guidance.*
> ACM International Conference on Multimedia (MM), 2025.
## Dataset Summary
DANI contains over **445,000 images**, including 5,000 natural images (from COCO, with resolutions 224, 256, 512, 1024) and more than 440,000 AI-generated images produced by diverse state-of-the-art generative models.
Each sample is annotated with detailed metadata, enabling comprehensive evaluation and flexible use for a broad range of visual and multimodal research.
Images are generated using a wide range of generative models and protocols:
- **Models:** GALIP, DFGAN, SD_V14, SD_V15, Versatile Diffusion (VD), SD_V21, SD_XL, Dalle2, Dalle3, and COCO (real images)
- **Image Sizes:** 224, 256, 512, 768, 1024
- **Generation Types:** Text-to-Image (T2I), Image-to-Image (I2I), Text and Image-to-Image (TI2I)
- **Categories:** indoor, outdoor, etc.
## Data Fields
Each sample in the dataset contains the following fields:
| Field | Description |
|------------|------------------------------------------------------------------------------|
| index | Unique index for each image |
| image | The image itself (as a file, not just path) |
| size | Image resolution (e.g., 224, 256, 512, 768, 1024) |
| category | Scene category (e.g., `indoor`, `outdoor`, etc.) |
| class_id | COCO class or semantic category ID/name |
| model | Generative model used (`GALIP`, `DFGAN`, `SD_V14`, `SD_V15`, `VD`, etc.) |
| gen_type | Generation method (`T2I`, `I2I`, `TI2I`) |
| reference | Whether it is a real/natural image (`True` for real, `False` for generated) |
> *Note:*
> - **COCO** images have `reference=True`, and may appear at multiple resolutions.
> - For AI-generated images, the `model` and `gen_type` fields indicate the specific generative model and generation protocol (T2I, I2I, or TI2I) used for each sample.
## Model/Generation Configurations
The dataset covers the following models and settings:
| Model | Image Size | Generation Types Supported |
|----------|------------|---------------------------------------|
| GALIP | 224 | T2I |
| DFGAN | 256 | T2I |
| SD_V14 | 512 | T2I, I2I, TI2I |
| SD_V15 | 512 | T2I, I2I, TI2I |
| VD | 512 | T2I, I2I, TI2I |
| SD_V21 | 768 | T2I, I2I, TI2I |
| SD_XL | 1024 | T2I, I2I, TI2I |
| Dalle2 | 512 | T2I, I2I |
| Dalle3 | 1024 | T2I |
| COCO | 224,256,512,1024 | Reference/Real Images |
For each generation type (`T2I`, `I2I`, `TI2I`), a diverse set of models are covered.
## Usage
You can load DANI directly using the 🤗 datasets library:
```python
from datasets import load_dataset
ds = load_dataset("Renyang/DANI")
print(ds)
# Output: DatasetDict({
# train: Dataset({
# features: ['index', 'image', 'size', 'category', 'class_id','model', 'gen_type','reference'],
# num_rows: 540257
# })
# })
# Access images and metadata
img = ds["train"][0]["image"]
meta = {k: ds["train"][0][k] for k in ds["train"].column_names if k != "image"}
```
*Note:* Images are loaded as PIL Images. Use `.convert("RGB")` if needed.
## Citation
If you use this dataset or the associated benchmark, please cite:
```bibtex
@inproceedings{liu2024djudge,
title = {D-Judge: How Far Are We? Assessing the Discrepancies Between AI-synthesized Images and Natural Images through Multimodal Guidance},
author = {Liu, Renyang and Lyu, Ziyu and Zhou, Wei and Ng, See-Kiong},
booktitle = {ACM International Conference on Multimedia (MM)},
organization = {ACM},
year = {2025},
}
```
## License
This dataset is released under the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license (for non-commercial research use).
## Contact
For questions or collaborations, please visit [Renyang Liu's homepage](https://ryliu68.github.io/). |