Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: ValueError
Message: Invalid string class label Doc-Protocol-Data@cefc5e73f0cb36979d520d93d0655210a3ff3aad
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2386, in __iter__
example = _apply_feature_types_on_example(
example, self.features, token_per_repo_id=self.token_per_repo_id
)
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2303, in _apply_feature_types_on_example
encoded_example = features.encode_example(example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2178, in encode_example
return encode_nested_example(self, example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1460, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1483, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
~~~~~~~~~~~~~~~~~~~~~^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1158, in encode_example
example_data = self.str2int(example_data)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1095, in str2int
output = [self._strval2int(value) for value in values]
~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1116, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label Doc-Protocol-Data@cefc5e73f0cb36979d520d93d0655210a3ff3aadNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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 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.
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. |
File structure
cutted_datasets_fakes.zip
└── cutted_datasets_fakes/
├── T-SROIE/ # 1,579 cropped 512×512 patches
├── OSTF/ # 3,046 cropped 512×512 patches
├── TPIC-13/ # 589 cropped 512×512 patches
└── RTM/ # 3,444 cropped 512×512 patches
Citation
If you use this dataset, please cite:
@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}
}
References
- Wang et al., 2022a: TPIC-13
- Wang et al., 2022b: T-SROIE
- Qu et al., 2025: OSTF
- Luo et al., 2025: RTM
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