Datasets:

PatrickZZZ
/

SIGMA

---
pretty_name: SIGMA-Annotated IML Dataset
license: other
language:
- en
task_categories:
- image-segmentation
tags:
- image-manipulation-localization
- image-forensics
- text-driven-image-editing
- automatic-annotation
size_categories:
- 1M<n<10M
---
# SIGMA: Full 1.1M Image Manipulation Localization Dataset

**This repository is the canonical full release of the SIGMA-annotated
dataset.** It contains **1,063,140 samples** (approximately 1.1 million) across
**24 editing-operation categories**, converted from four public text-driven
image-editing corpora:

- AnyEdit
- CrispEdit-2M
- OmniEdit-Filtered-1.2M
- PromptfixData

Two smaller convenience subsets of the same annotations are available on
ModelScope:

- **10,000 samples:** [PeiyuZhuang/SIGMA_small](https://www.modelscope.ai/datasets/PeiyuZhuang/SIGMA_small)
- **100,000 samples:** [PeiyuZhuang/SIGMA_partial](https://www.modelscope.ai/datasets/PeiyuZhuang/SIGMA_partial)

The 10K and 100K releases are subsets of this repository, not separate
datasets. Use this full release for large-scale training.

## Dataset Summary

Text-driven editing corpora already contain millions of
`(original image, edited image, editing instruction)` records, but they do not
provide the pixel-level masks required to train image manipulation localization
(IML) models. SIGMA (*Semantic-Difference Instruction-Grounding Mask
Annotator*) converts these existing records into IML supervision.

SIGMA performs semantic-feature differencing between the original and edited
images and injects an instruction-derived spatial prior through bidirectional
cross-modal refinement. This combination helps suppress diffusion-induced
perturbations while retaining both intended edits and editor side-effects that
instruction-only grounding may miss.

Each released sample is derived from a public editing record and adds a binary
localization mask generated by SIGMA. These masks are automatic annotations,
not human-drawn ground truth.

## Validation and Downstream Utility

Across CoCoGlide, AutoSplice, MagicBrush, DEAL-300K, and OpenSDI, SIGMA reaches
**89.83% average F1** and **83.52% average IoU** for automatic mask generation.
This is an improvement of **12.20 F1 points** and **11.16 IoU points** over
DDPM-CD. On DEAL-300K, SIGMA reaches 76.43% F1, compared with 20.90% for
DDPM-CD.

The annotations are also useful as training supervision. Fine-tuning CAT-Net,
MVSS-Net, PSCC-Net, and TruFor on SIGMA-annotated editing data improves the
average F1 of all four detectors over their public weights. The four gains
range from **4.58 to 30.79 points**, with a mean gain of **20.98 points** across
five held-out datasets.

## Dataset Family

- **Full release (this repository):** 1,063,140 samples from four source
  corpora and 24 operation categories.
- **100K subset:** selected from this full release for medium-scale
  experiments.
- **10K subset:** 2,500 samples from each source corpus, intended for quick
  inspection and pipeline testing.

No additional annotation method is used for the subsets.

## Repository Layout

```text
SIGMA.zip.001
SIGMA.zip.002
...
SIGMA.zip.113
SIGMA_instructions/
  AnyEdit.jsonl
  CrispEdit-2M.jsonl
  OmniEdit-Filtered-1.2M.jsonl
  PromptfixData.jsonl
```

`SIGMA.zip.001` through `SIGMA.zip.113` are sequential fragments of the full
ZIP archive. They must be downloaded completely and concatenated in numeric
order before extraction. The JSONL files under `SIGMA_instructions/` provide
the editing-instruction manifests grouped by source corpus.

## Download and Extraction

Download with the Hugging Face CLI:

```bash
hf download PatrickZZZ/SIGMA \
  --repo-type dataset \
  --local-dir SIGMA
cd SIGMA
```

Alternatively, use Git LFS:

```bash
git lfs install
git clone https://huggingface.co/datasets/PatrickZZZ/SIGMA
cd SIGMA
```

Reassemble the numbered fragments with Python:

```python
from pathlib import Path
parts = sorted(Path(".").glob("SIGMA.zip.[0-9][0-9][0-9]"))
if len(parts) != 113:
    raise RuntimeError(f"Expected 113 archive fragments, found {len(parts)}")
with open("SIGMA.zip", "wb") as output:
    for part in parts:
        print(f"Appending {part}")
        with open(part, "rb") as source:
            while chunk := source.read(1024 * 1024):
                output.write(chunk)
```

Then extract the reconstructed archive:

```bash
unzip SIGMA.zip
```

On Windows, the same Python reassembly script can be used before running:

```powershell
Expand-Archive SIGMA.zip
```

## Intended Uses

Appropriate uses include:

- training IML models on text-driven edits;
- studying generalization across image editors and generators;
- investigating learning from automatically generated pixel masks;
- comparing annotation and data-curation strategies for image forensics.

The ModelScope subsets are recommended for pipeline debugging before
downloading this full release.

## Limitations

- The masks are generated automatically and can contain residual errors.
- Ambiguous or compositional instructions may be parsed incompletely.
- Extremely subtle edits and unintended editor side-effects remain difficult.
- The source corpora and operation distribution do not cover every image
  editor, manipulation type, or deployment domain.
- This dataset should not replace manually annotated benchmarks when reporting
  final mask-quality results.

## License and Terms of Use

**The SIGMA-annotated data are released for academic research only.** This
repository does not replace or override the licenses of the source corpora.
Users must comply with the applicable source-dataset terms and cite both SIGMA
and the corresponding source dataset.

- **PromptfixData:** research use only; the dataset and models trained on it may
  not be used commercially. Users must also follow the licenses of its source
  data.
- **AnyEdit:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
- **CrispEdit-2M:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
- **OmniEdit-Filtered-1.2M:** [MIT](https://opensource.org/license/mit/)

Source repositories:

- [AnyEdit](https://huggingface.co/datasets/Bin1117/AnyEdit)
- [CrispEdit-2M](https://huggingface.co/datasets/WeiChow/CrispEdit-2M)
- [OmniEdit-Filtered-1.2M](https://huggingface.co/datasets/TIGER-Lab/OmniEdit-Filtered-1.2M)
- [PromptfixData](https://huggingface.co/datasets/yeates/PromptfixData)

## Citation

Please cite SIGMA and the source corpus associated with each sample:

```bibtex
@misc{zhuang2026sigma,
  title         = {SIGMA: Semantic-Difference Instruction-Grounding Mask Annotator for Text-Driven Image Manipulation Localization},
  author        = {Zhuang, Peiyu and Yang, Jianquan and Li, Haodong and Cai, Zhuoying and Xie, Ruitao and Zeng, Jishen and Chen, Baoying and Huang, Jiwu and Cao, Xiaochun},
  year          = {2026},
  eprint        = {2605.27924},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2605.27924}
}
```

The source-corpus papers are:

- AnyEdit: *AnyEdit: Mastering Unified High-Quality Image Editing for Any Idea*
- CrispEdit-2M: *EditMGT: Unleashing Potentials of Masked Generative
  Transformers in Image Editing*
- OmniEdit-Filtered-1.2M: *OmniEdit: Building Image Editing Generalist Models
  Through Specialist Supervision*
- PromptfixData: *PromptFix: You Prompt and We Fix the Photo*

## Related Resources

- [Project page](https://zhuangpeiyu.github.io/projects/SIGMA/)
- [Paper](https://arxiv.org/abs/2605.27924)
- [Code](https://github.com/ZhuangPeiyu/SIGMA)
- [Model checkpoint](https://huggingface.co/PatrickZZZ/SIGMA)

	---
	pretty_name: SIGMA-Annotated IML Dataset
	license: other
	language:
	- en
	task_categories:
	- image-segmentation
	tags:
	- image-manipulation-localization
	- image-forensics
	- text-driven-image-editing
	- automatic-annotation
	size_categories:
	- 1M<n<10M
	---

	# SIGMA: Full 1.1M Image Manipulation Localization Dataset

	**This repository is the canonical full release of the SIGMA-annotated
	dataset. It contains 1,063,140 samples** (approximately 1.1 million) across
	24 editing-operation categories, converted from four public text-driven
	image-editing corpora:

	- AnyEdit
	- CrispEdit-2M
	- OmniEdit-Filtered-1.2M
	- PromptfixData

	Two smaller convenience subsets of the same annotations are available on
	ModelScope:

	- 10,000 samples: [PeiyuZhuang/SIGMA_small](https://www.modelscope.ai/datasets/PeiyuZhuang/SIGMA_small)
	- 100,000 samples: [PeiyuZhuang/SIGMA_partial](https://www.modelscope.ai/datasets/PeiyuZhuang/SIGMA_partial)

	The 10K and 100K releases are subsets of this repository, not separate
	datasets. Use this full release for large-scale training.

	## Dataset Summary

	Text-driven editing corpora already contain millions of
	`(original image, edited image, editing instruction)` records, but they do not
	provide the pixel-level masks required to train image manipulation localization
	(IML) models. SIGMA (*Semantic-Difference Instruction-Grounding Mask
	Annotator*) converts these existing records into IML supervision.

	SIGMA performs semantic-feature differencing between the original and edited
	images and injects an instruction-derived spatial prior through bidirectional
	cross-modal refinement. This combination helps suppress diffusion-induced
	perturbations while retaining both intended edits and editor side-effects that
	instruction-only grounding may miss.

	Each released sample is derived from a public editing record and adds a binary
	localization mask generated by SIGMA. These masks are automatic annotations,
	not human-drawn ground truth.

	## Validation and Downstream Utility

	Across CoCoGlide, AutoSplice, MagicBrush, DEAL-300K, and OpenSDI, SIGMA reaches
	89.83% average F1 and 83.52% average IoU for automatic mask generation.
	This is an improvement of 12.20 F1 points and 11.16 IoU points over
	DDPM-CD. On DEAL-300K, SIGMA reaches 76.43% F1, compared with 20.90% for
	DDPM-CD.

	The annotations are also useful as training supervision. Fine-tuning CAT-Net,
	MVSS-Net, PSCC-Net, and TruFor on SIGMA-annotated editing data improves the
	average F1 of all four detectors over their public weights. The four gains
	range from 4.58 to 30.79 points, with a mean gain of 20.98 points across
	five held-out datasets.

	## Dataset Family

	- Full release (this repository): 1,063,140 samples from four source
	corpora and 24 operation categories.
	- 100K subset: selected from this full release for medium-scale
	experiments.
	- 10K subset: 2,500 samples from each source corpus, intended for quick
	inspection and pipeline testing.

	No additional annotation method is used for the subsets.

	## Repository Layout

	```text
	SIGMA.zip.001
	SIGMA.zip.002
	...
	SIGMA.zip.113
	SIGMA_instructions/
	AnyEdit.jsonl
	CrispEdit-2M.jsonl
	OmniEdit-Filtered-1.2M.jsonl
	PromptfixData.jsonl
	```

	`SIGMA.zip.001` through `SIGMA.zip.113` are sequential fragments of the full
	ZIP archive. They must be downloaded completely and concatenated in numeric
	order before extraction. The JSONL files under `SIGMA_instructions/` provide
	the editing-instruction manifests grouped by source corpus.

	## Download and Extraction

	Download with the Hugging Face CLI:

	```bash
	hf download PatrickZZZ/SIGMA \
	--repo-type dataset \
	--local-dir SIGMA
	cd SIGMA
	```

	Alternatively, use Git LFS:

	```bash
	git lfs install
	git clone https://huggingface.co/datasets/PatrickZZZ/SIGMA
	cd SIGMA
	```

	Reassemble the numbered fragments with Python:

	```python
	from pathlib import Path

	parts = sorted(Path(".").glob("SIGMA.zip.[0-9][0-9][0-9]"))
	if len(parts) != 113:
	raise RuntimeError(f"Expected 113 archive fragments, found {len(parts)}")

	with open("SIGMA.zip", "wb") as output:
	for part in parts:
	print(f"Appending {part}")
	with open(part, "rb") as source:
	while chunk := source.read(1024 * 1024):
	output.write(chunk)
	```

	Then extract the reconstructed archive:

	```bash
	unzip SIGMA.zip
	```

	On Windows, the same Python reassembly script can be used before running:

	```powershell
	Expand-Archive SIGMA.zip
	```

	## Intended Uses

	Appropriate uses include:

	- training IML models on text-driven edits;
	- studying generalization across image editors and generators;
	- investigating learning from automatically generated pixel masks;
	- comparing annotation and data-curation strategies for image forensics.

	The ModelScope subsets are recommended for pipeline debugging before
	downloading this full release.

	## Limitations

	- The masks are generated automatically and can contain residual errors.
	- Ambiguous or compositional instructions may be parsed incompletely.
	- Extremely subtle edits and unintended editor side-effects remain difficult.
	- The source corpora and operation distribution do not cover every image
	editor, manipulation type, or deployment domain.
	- This dataset should not replace manually annotated benchmarks when reporting
	final mask-quality results.

	## License and Terms of Use

	The SIGMA-annotated data are released for academic research only. This
	repository does not replace or override the licenses of the source corpora.
	Users must comply with the applicable source-dataset terms and cite both SIGMA
	and the corresponding source dataset.

	- PromptfixData: research use only; the dataset and models trained on it may
	not be used commercially. Users must also follow the licenses of its source
	data.
	- AnyEdit: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
	- CrispEdit-2M: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
	- OmniEdit-Filtered-1.2M: [MIT](https://opensource.org/license/mit/)

	Source repositories:

	- [AnyEdit](https://huggingface.co/datasets/Bin1117/AnyEdit)
	- [CrispEdit-2M](https://huggingface.co/datasets/WeiChow/CrispEdit-2M)
	- [OmniEdit-Filtered-1.2M](https://huggingface.co/datasets/TIGER-Lab/OmniEdit-Filtered-1.2M)
	- [PromptfixData](https://huggingface.co/datasets/yeates/PromptfixData)

	## Citation

	Please cite SIGMA and the source corpus associated with each sample:

	```bibtex
	@misc{zhuang2026sigma,
	title = {SIGMA: Semantic-Difference Instruction-Grounding Mask Annotator for Text-Driven Image Manipulation Localization},
	author = {Zhuang, Peiyu and Yang, Jianquan and Li, Haodong and Cai, Zhuoying and Xie, Ruitao and Zeng, Jishen and Chen, Baoying and Huang, Jiwu and Cao, Xiaochun},
	year = {2026},
	eprint = {2605.27924},
	archivePrefix = {arXiv},
	primaryClass = {cs.CV},
	url = {https://arxiv.org/abs/2605.27924}
	}
	```

	The source-corpus papers are:

	- AnyEdit: AnyEdit: Mastering Unified High-Quality Image Editing for Any Idea
	- CrispEdit-2M: *EditMGT: Unleashing Potentials of Masked Generative
	Transformers in Image Editing*
	- OmniEdit-Filtered-1.2M: *OmniEdit: Building Image Editing Generalist Models
	Through Specialist Supervision*
	- PromptfixData: PromptFix: You Prompt and We Fix the Photo

	## Related Resources

	- [Project page](https://zhuangpeiyu.github.io/projects/SIGMA/)
	- [Paper](https://arxiv.org/abs/2605.27924)
	- [Code](https://github.com/ZhuangPeiyu/SIGMA)
	- [Model checkpoint](https://huggingface.co/PatrickZZZ/SIGMA)