Datasets:
metadata
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.
📁 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:
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.