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Add task category and paper/code links to dataset card (#1)

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- Add task category and paper/code links to dataset card (eb7099ca73a09edd319b1d1afaff1a1d408b98be)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

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  1. README.md +12 -9
README.md CHANGED
@@ -1,19 +1,22 @@
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  ---
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- title: Doc Protocol Data
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- description: >-
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- The 4 cross-domain test sets (T-SROIE, OSTF, TPIC-13, RTM). All samples in
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- the cross-domain test sets are cropped to 512 × 512 patches without
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- additional compression. The training set and the three in-domain test sets
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- share the same three forgery synthesis types: copy-move, splicing, and
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- print-based edits. The cross-domain test sets have more diverse forgery
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- sources, including AIGC-based text editing models and manual manipulation.
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  license: other
 
 
 
 
 
 
 
 
 
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  ---
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  # Doc Protocol Data
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  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**.
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  ## Cross-domain test sets
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  | Dataset | Split | Domain | #Samples | Description |
@@ -34,4 +37,4 @@ If you use this dataset, please cite:
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  journal={Advances in Neural Information Processing Systems},
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  year={2025}
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  }
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- ```
 
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  ---
 
 
 
 
 
 
 
 
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  license: other
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+ task_categories:
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+ - image-segmentation
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+ title: Doc Protocol Data
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+ description: 'The 4 cross-domain test sets (T-SROIE, OSTF, TPIC-13, RTM). All samples
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+ in the cross-domain test sets are cropped to 512 × 512 patches without additional
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+ compression. The training set and the three in-domain test sets share the same three
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+ forgery synthesis types: copy-move, splicing, and print-based edits. The cross-domain
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+ test sets have more diverse forgery sources, including AIGC-based text editing models
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+ and manual manipulation.'
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  ---
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  # Doc Protocol Data
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  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**.
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+ 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).
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+
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  ## Cross-domain test sets
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  | Dataset | Split | Domain | #Samples | Description |
 
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  journal={Advances in Neural Information Processing Systems},
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  year={2025}
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  }
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+ ```