| --- |
| license: other |
| task_categories: |
| - image-segmentation |
| title: Doc Protocol Data |
| description: 'The 4 cross-domain test sets (T-SROIE, OSTF, TPIC-13, RTM). All samples |
| in the cross-domain test sets are cropped to 512 × 512 patches without additional |
| compression. The training set and the three in-domain test sets share the same three |
| forgery synthesis types: copy-move, splicing, and print-based edits. The cross-domain |
| test sets have more diverse forgery sources, including AIGC-based text editing models |
| and manual manipulation.' |
| --- |
| |
| # Doc Protocol Data |
|
|
| The 4 cross-domain test sets (T-SROIE, OSTF, TPIC-13, RTM). All samples in the cross-domain test sets are cropped to 512 × 512 patches without additional compression. The cross-domain test sets have more diverse forgery sources, including **AIGC-based text editing models** and **manual manipulation**. |
|
|
| This dataset is used in the paper [RiSE: Residual Subspace Expert for Generalizable Text-Centric Image Forgery Localization](https://huggingface.co/papers/2610.24480). Code is available at [https://github.com/KahimWong/RiSE](https://github.com/KahimWong/RiSE). |
|
|
| ## Cross-domain test sets |
|
|
| | Dataset | Split | Domain | #Samples | Description | |
| |---|---|---|---|---| |
| | T-SROIE [Wang et al., 2022b] | Test | Cross-domain | 1,579 | Scanned receipts tampered using the AIGC text editing model SR-Net. | |
| | OSTF [Qu et al., 2025] | Test | Cross-domain | 3,046 | Natural scene text images tampered using eight different AIGC-based text editing models. | |
| | TPIC-13 [Wang et al., 2022a] | Test | Cross-domain | 589 | Naturally captured scene-text images tampered using the AIGC text editing model SR-Net. | |
| | RTM [Luo et al., 2025] | Test | Cross-domain | 3,444 | Document images with both synthetic and manual manipulations, covering copy-move, splicing, print, and erasure edits across diverse document types such as scanned forms. | |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite: |
|
|
| ```bibtex |
| @article{du2025forensichub, |
| title={ForensicHub: A unified benchmark \& codebase for all-domain fake image detection and localization}, |
| author={Bo Du and Xuekang Zhu and Xiaochen Ma and Chenfan Qu and Kaiwen Feng and Zhe Yang and Chi-Man Pun and Jian Liu and Ji-Zhe Zhou}, |
| journal={Advances in Neural Information Processing Systems}, |
| year={2025} |
| } |
| ``` |
