| --- |
| license: other |
| license_name: research-use-only |
| task_categories: |
| - image-text-to-text |
| language: |
| - en |
| size_categories: |
| - 10K<n<100K |
| tags: |
| - safety |
| - vision-language-model |
| - preference-data |
| - dpo |
| - red-teaming |
| - contrastive |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| --- |
| |
| # ContraSafe-VL |
|
|
| A contrastive safety preference dataset for vision-language models. |
| For a **fixed text query**, paired images are provided whose safety verdicts |
| *flip*: the same question is unsafe with one visual context and safe with another. |
|
|
| ## Dataset statistics |
|
|
| | item | value | |
| |---|---| |
| | multimodal queries (question, image) | **53,101** | |
| | unsafe / safe | **28,676 / 24,425** | |
| | contrastive pairs (unsafe × safe, same text) | **10,115** across 10,022 questions | |
| | queries participating in contrastive pairs | 20,230 | |
| | additional (non-paired) queries | 32,871 — 18,561 unsafe + 14,310 safe | |
| | unique images | 17,862 | |
| | response candidates | 9 VLM backbones, 460,529 responses in total | |
| | reward scores | `helpful_score` / `harmless_score` per response | |
|
|
| ## Files |
|
|
| ``` |
| data/train-*.parquet single table, one row per (question, image); images embedded as bytes |
| ``` |
|
|
| ## Fields |
|
|
| | field | type | description | |
| |---|---|---| |
| | `id` | string | stable hash `csvl-` + sha1(question + image name)[:12] | |
| | `question` | string | textual query | |
| | `images` | list[struct] | `{bytes, path}` per image (HF `Image` compatible) | |
| | `image_meta` | list[struct] | aligned with `images`: `is_edited`, `source_dataset` | |
| | `has_safety_risk` | string | `Yes` = this (question, image) pair is unsafe, `No` = safe | |
| | `flip_images` | list[str] | paths of all opposite-label partners | |
| | `pair_data_id` | string \| null | partner id, set only when `pair_count == 1` | |
| | `pair_data_ids` | list[str] | ids of all opposite-label partners with the same question | |
| | `pair_count` | int | number of opposite-label partners | |
| | `responses` | list[struct] | `model_name`, `response`, `helpful_score`, `harmless_score` | |
|
|
| `source_dataset` records the upstream dataset of the image (`spa_vl`, `safe_rlhf_v`, |
| `datacomp`, `llava`) when applicable. |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("<user>/ContraSafe-VL", split="train") # 53,101 rows |
| |
| ds[0]["images"][0].show() # 图片已解码为 PIL.Image |
| |
| # contrastive pairs |
| pairs = [r for r in ds if r["pair_data_ids"]] |
| n_pairs = len({tuple(sorted((r["id"], p))) for r in pairs for p in r["pair_data_ids"]}) # 10,115 |
| ``` |
|
|
| ## Intended use and ethics |
|
|
| This dataset **contains harmful and sensitive images** (violence, illegal acts, |
| regulated substances, hate, self-harm, etc.) collected for safety research only. It is |
| released for research on vision-language model safety, red-teaming and alignment. |
| Do not use it to build or train models for harmful purposes. Access may be gated; |
| please agree to the terms before use and cite the paper in any publication. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{wu2026contrasafe, |
| title = {ContraSafe-VL: A Contrastive Safety Preference Dataset for Vision-Language Models}, |
| author = {Wu, Di and Zhao, Yanyan and Lu, Xin and Yang, Hao and Zhang, Taotao and Zhao, Yunzhen}, |
| journal = {arXiv preprint arXiv:2610.23908}, |
| year = {2026} |
| } |
| ``` |
|
|
