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RootAccess4Life/
ood-source-tracing

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sourcetrace — fitted heads for open-set audio deepfake source tracing

Fitted heads for sourcetrace, which names which generator produced a synthetic speech clip — or reports that the generator is not one it has seen.

These are not standalone models. Each file is a torch.save dict with format: "sourcetrace-method-checkpoint", loaded by sourcetrace.method.Method.load. They hold the small trained head plus the fitted scoring stack (class anchors, relative-Mahalanobis density, z-norm constants, conformal calibration, per-block whitening). The front-ends — microsoft/wavlm-large and facebook/encodec_24khz — are frozen, are not included here, and are fetched separately by scripts/download_models.py.

Input

Not audio. A 2133-d feature vector per clip, laid out as [ SSL 0:2048 | signature 2048:2100 | codec residual 2100:2133 ], produced by scripts/extract_features.py. There is no way to run these weights without the repository and an extracted feature cache.

`mlaad_v5.pt`

MLAAD v5, family-level open-set protocol: 65 known generator families, 9,620 evaluation trials, split seed 42, fit seed 0.

Metric	Value	Published SOTA
FPR95 (lower better)	1.14 %	3.36 %
OOD-EER (lower better)	3.66 %	—
Closed-set accuracy	99.34 %	—

Conformal abstention: measured coverage 96.11 % against a 95 % nominal level.

Reproducible, not just reported. Refitting from the public code at these seeds reproduces results/ablation/full.json bit-for-bit — FPR95 1.1428571428571428, OOD-EER 3.6571428571428575, closed-set 99.33714285714285, under exact equality rather than a tolerance. This file is that fit. Downloading it saves the ~35-40 min fit and nothing else: evaluation still reads the feature cache, so MLAAD v5 must be downloaded and extracted first.

bash

huggingface-cli download RootAccess4Life/ood-source-tracing mlaad_v5.pt \
    --local-dir checkpoints
python scripts/evaluate.py --task mlaad_v5 --checkpoint checkpoints/mlaad_v5.pt

Single-seed. One split seed, one fit seed. These are point estimates with no variance attached; do not read the margin over 3.36 % as a measured effect size.

`stopa.pt`

STOPA cross-corpus open-set protocol: 3 EET attacks fitted, 5 known enrolled, 5 unknown held out; 33,200 enrolment and 629,800 trial clips, conditions pooled, split seed 42 / fit seed 0.

Metric	Value	Published SOTA
Unknown-attack EER (lower better)	9.33 %	16.43 %
Known-attack EER	10.79 %	—

Measured by the released code on 2026-08-18; raw numbers in results/stopa_measured.json.

A lost earlier implementation had recorded 9.38 % and the paper declines to print it, because no identified run stood behind it. The figure above is not a recovery of that run — different implementation, and the reference head's seeded initialisation is unrecoverable. It is an independent measurement that happens to land 0.05 points away, with a protocol, seeds and a results file behind it. Cite this one, not 9.38.

Single-seed, like the MLAAD numbers above.

bash

huggingface-cli download RootAccess4Life/ood-source-tracing stopa.pt --local-dir checkpoints
python scripts/evaluate.py --task stopa --checkpoint checkpoints/stopa.pt

Limitations

Trained on MLAAD v5 only. Attribution across other corpora, languages, codecs or recording conditions is untested.
The head in the public code is a reconstruction from docs/METHOD.md. It does not reproduce an earlier reference implementation's FPR95 = 0.7543 % / OOD-EER = 2.81 %; that head's seeded initialisation is unrecoverable and those figures are retired.
For research on open-set attribution. Not validated for forensic, legal or moderation use; the abstention rule is calibrated on this protocol and its coverage guarantee does not transfer off it.

Licence

MIT, matching the code repository. MLAAD and STOPA carry their own terms; no audio is redistributed here.

Citation

bibtex

@inproceedings{pujari2027frontend,
  title     = {Front-End Evidence and Calibrated Abstention for
               Open-Set Audio Deepfake Attribution},
  author    = {Pujari, Aditya},
  booktitle = {Proc. IEEE ICASSP},
  year      = {2027}
}