| --- |
| license: other |
| license_name: mixed-upstream |
| extra_gated_prompt: >- |
| This dataset aggregates media from ~20 upstream sources with differing terms. |
| Several (ASVspoof, DF40, LAV-DF, MSR-VTT, ImageNet, MLAAD) are research-only, |
| EULA-bound, or not redistributable. Accepting here does NOT relieve you of |
| complying with each upstream source's own licence. |
| |
| The audio and video splits contain recordings of real people. Treat them as |
| biometric data and handle accordingly. |
| extra_gated_fields: |
| I will use this dataset for non-commercial research only: checkbox |
| I will comply with each upstream source's own licence: checkbox |
| I understand this contains biometric data: checkbox |
| task_categories: |
| - audio-classification |
| - image-classification |
| - video-classification |
| tags: |
| - deepfake-detection |
| - multimodal |
| - benchmark |
| size_categories: |
| - 10K<n<100K |
| pretty_name: DeepSafe Evaluation Dataset |
| --- |
| |
| # DeepSafe Evaluation Dataset |
|
|
| Evaluation set for [DeepSafe](https://github.com/deepsafehq/deepsafe-bench), |
| a deepfake detection benchmark. |
|
|
| ## Tiers |
|
|
| | Tier | Samples | Generators | Size | Use | |
| |---|---|---|---|---| |
| | `master_eval_small/` | 198 | 116 | 1.7 GB | smoke test, under 2 min | |
| | `master_eval/` | 15,454 | 411 | 10 GB | the standard benchmark | |
| | `master_eval_full/` | 45,954 | 411 | 25 GB | complete set | |
|
|
| Medium tier composition: 9,954 image, 3,500 audio, 2,000 video. |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| snapshot_download("deepsafe/evaluation-dataset", repo_type="dataset", |
| allow_patterns="master_eval_small/**", local_dir="./data") |
| ``` |
|
|
| ## What the data shows |
|
|
| Measured on this set, an ensemble of 19 published detectors catches **66.2% of |
| fakes at a 6.5% false positive rate**, and the failures are concentrated: |
|
|
| | Generator | Caught | |
| |---|---| |
| | Hunyuan (video) | 1.7% | |
| | Sora (video) | 7.0% | |
| | Veo (video) | 8.0% | |
| | Stable Diffusion 1.4 (image) | 100% | |
| | Midjourney v5 (image) | 100% | |
|
|
| Detectors are strong on the 2022-2023 diffusion models the literature was built |
| around and weak on recent video generators. That contrast is the reason this |
| set exists. |
|
|
| ## Upstream sources |
|
|
| Freely redistributable: LibriSpeech (CC-BY-4.0), LJSpeech (public domain), |
| VCTK (CC-BY-4.0), Common Voice (CC-0), COCO (CC-BY-4.0). |
|
|
| Research-only, EULA-bound, or not redistributable: ImageNet, ASVspoof, DF40, |
| LAV-DF, MSR-VTT, MLAAD. |
|
|
| This repository is **gated** because of the second group. Each upstream source |
| remains governed by its own terms, which you must satisfy independently. |
|
|
| ## Rebuilding from source |
|
|
| The manifests (`metadata.json` per tier) record every sample's origin, so the |
| set can be reconstructed from the original sources rather than from this mirror. |
| See `eval/build_master_eval.py` in the DeepSafe repository. |
|
|
| ## Takedown |
|
|
| If you hold rights to any included material and want it removed, open an issue |
| at https://github.com/deepsafehq/deepsafe-bench. Removal within 48 hours, no |
| questions asked. |
|
|
| ## Caveat |
|
|
| The manifests carry no `language` field. Do not infer multilingual performance |
| from this set without adding language labels first. |
|
|
