Datasets:
| 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. |