---
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.