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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.
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DeepSafe Evaluation Dataset
Evaluation set for DeepSafe, 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.
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.
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