Datasets:
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license: apache-2.0
task_categories:
- image-segmentation
tags:
- aigc-detection
- diffusion-editing
- image-forgery-detection
- diffusion-models
---
# 🖼️ DiffSeg30k -- A multi-turn diffusion-editing dataset for localized AIGC detection
A dataset for **segmenting diffusion-based edits** — ideal for training and evaluating models that localize edited regions and identify the underlying diffusion model, as presented in the paper [DiffSeg30k: A Multi-Turn Diffusion Editing Benchmark for Localized AIGC Detection](https://huggingface.co/papers/2511.19111).
## 📁 Dataset Usage
- `xxxxxxxx.image.png`: Edited images. Each image may have undergone 1, 2, or 3 editing operations.
- `xxxxxxxx.mask.png`: The corresponding mask indicating edited regions, where pixel values encode both the type of edit and the diffusion model used.
Load images and masks as follows:
```python
from datasets import load_dataset
dataset = load_dataset("Chaos2629/Diffseg30k", split="train")
image, mask = dataset[0]['image'], dataset[0]['mask']
```
## 🧠 Mask Annotation
Each mask is a grayscale image (PNG format), where pixel values correspond to a specific editing model. The mapping is as follows:
| Mask Value | Editing Model |
|------------|------------------------------------------------------|
| 0 | background |
| 1 | stabilityai/stable-diffusion-2-inpainting |
| 2 | kolors |
| 3 | stabilityai/stable-diffusion-3.5-medium |
| 4 | flux |
| 5 | diffusers/stable-diffusion-xl-1.0-inpainting-0.1 |
| 6 | glide |
| 7 | Tencent-Hunyuan/HunyuanDiT-Diffusers |
| 8 | kandinsky-community/kandinsky-2-2-decoder-inpaint |
## 📌 Notes
- Each edited image may be edited **multiple turns**, so the corresponding mask may contain several different **label values** ranging from 0 to 8. |