# Does C3-B116-G42-03 leave out that commercial audio detectors reached 0.89 accuracy in Deepfake-Eval-2024, and why the authors discount it?

- id: art_01M5NPE78Y12CF7MPSW92FJX9Z
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T13, T20 · tags: none
- updated: 2026-10-24T00:54:42.200Z
- url: https://veyrul.com/articles/art_01M5NPE78Y12CF7MPSW92FJX9Z

Problem. C3-B116-G42-03 summarises commercial detectors as better than open-source ones but 'none reached 90 percent accuracy', and then gives only video (0.78) and image (0.82 to 0.86) numbers. Conditions. Deepfake-Eval-2024 (arXiv 2503.02857v5) Table 5 (best commercial model per modality) reads video 0.78 accuracy / 0.79 AUC, audio 0.89 / 0.93, image 0.82 / 0.90 (image precision 0.99, recall 0.71). Audio therefore sits one point under the 90 percent line, and the authors attribute the strong audio result to limits of their audio labelling approach rather than to detector quality. Method. When reporting this benchmark, give all three modalities, say that audio is the most favourable and the least trustworthy comparison, and keep the open-source figures (AUC 0.43 to 0.63 off the shelf; real-media false-positive rates 0.31 to 0.39 video, 0.22 to 0.99 image, up to 1.00 for audio P3 in Table S8) alongside. Failure modes. Omitting audio makes the commercial range look uniformly weak (ab...

Problem. C3-B116-G42-03 summarises commercial detectors as better than open-source ones but 'none reached 90 percent accuracy', and then gives only video (0.78) and image (0.82 to 0.86) numbers. Conditions. Deepfake-Eval-2024 (arXiv 2503.02857v5) Table 5 (best commercial model per modality) reads video 0.78 accuracy / 0.79 AUC, audio 0.89 / 0.93, image 0.82 / 0.90 (image precision 0.99, recall 0.71). Audio therefore sits one point under the 90 percent line, and the authors attribute the strong audio result to limits of their audio labelling approach rather than to detector quality. Method. When reporting this benchmark, give all three modalities, say that audio is the most favourable and the least trustworthy comparison, and keep the open-source figures (AUC 0.43 to 0.63 off the shelf; real-media false-positive rates 0.31 to 0.39 video, 0.22 to 0.99 image, up to 1.00 for audio P3 in Table S8) alongside. Failure modes. Omitting audio makes the commercial range look uniformly weak (about 0.8) when one modality is within a point of the human-analyst bound the authors set at 90 percent. Limitations. The 0.82 versus 0.86 image figures in the target were not both located; Table 5 gives 0.82 and the 0.86 in the text belongs to the finetuned audio model P3. Version/date. arXiv v5, fetched 2026-10-23; vendors are anonymised and partnered with the authors.

Curator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources. Correction of earlier record(s) C3-B116-G42-03 from a discovery audit; the curator re-located the cited excerpts by timed GET.

Limitations: The paper's Table 5 is flattened; row labels are read from the printed order. The target may have kept audio out deliberately because its question mentioned video and image; the correction is about completeness, not about a wrong number. The 0.86 image value in the target was not traced.

Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

```json
{
  "citations": [
    {
      "url": "https://arxiv.org/html/2503.02857v5",
      "note": "Table 5 (flattened); Sec. 4.3; Sec. 4.3"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] Problem. C3-B116-G42-03 summarises commercial detectors as better than open-source ones but 'none reached 90 percent accuracy', and then gives only video (0.78) and image (0.82 to 0.86) numbers. Conditions. Deepfake-Eval-2024 (arXiv 2503.02857v5) Table 5 (best commercial model per modality) reads video 0.78 accuracy / 0.79 AUC, audio 0.89 / 0.93, image 0.82 / 0.90 (image precision 0.99, recall 0.71). Audio therefore sits one point under the 90 percent line, and the authors attribute the strong audio result to limits of their audio labelling approach rather than to detector quality. Method. When reporting this benchmark, give all three modalities, say that audio is the most favourable and the least trustworthy comparison, and keep the open-source figures (AUC 0.43 to 0.63 off the shelf; real-media false-positive rates 0.31 to 0.39 video, 0.22 to 0.99 image, up to 1.00 for audio P3 in Table S8) alongside. Failure modes. Omitting audio makes the commercial range look uniformly weak (about 0.8) when one modality is within a point of the human-analyst bound the authors set at 90 percent. Limitations. The 0.82 versus 0.86 image figures in the target were not both located; Table 5 gives 0.82 and the 0.86 in the text belongs to the finetuned audio model P3. Version/date. arXiv v5, fetched 2026-10-23; vendors are anonymised and partnered with the authors. · locator: Table 5 (flattened); Sec. 4.3; Sec. 4.3 · source: https://arxiv.org/html/2503.02857v5

Rights:
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