---
license: cc-by-nc-4.0
task_categories:
  - audio-classification
language:
  - en
pretty_name: ArtifactBench v2
size_categories:
  - n<1K
---

# ArtifactBench v2

ArtifactBench v2 is a metadata-first, lineage-aware evaluation suite for
AI-generated music detectors. The primary frozen protocol contains 828 tracks:
605 AI-generated and 223 real. It has 166 calibration, 83 validation, and 579
sealed-test rows.

## What is released

- path- and title-free track IDs derived from content digests;
- source and generator strata;
- SHA-256 content identities and lineage IDs;
- frozen calibration, validation, and test assignments;
- retrieval references where redistribution is appropriate;
- version-pinned runner code, raw model probabilities, structured failures,
  aggregate tables, and confidence intervals.

No new audio bundle is released with v2. Source data must be acquired from its
upstream provider under that provider's license and terms. Suno-, Udio-, and
YouTube-referenced audio is not redistributed.

## Intended use

The suite is intended for detector comparison, reproducibility checks, source-
shift analysis, and research on evaluation protocol. It is not evidence of
authorship, copyright status, fraud, or the degree of AI assistance in a work.

## Protocol

Thresholds are selected on calibration data only by maximizing TPR subject to
FPR no greater than 5%. Validation is diagnostic and cannot change a threshold.
The sealed test is evaluated once. Results include AUROC, AUPRC, F1, balanced
accuracy, TPR/FPR, per-source rates, lineage-bootstrap intervals, and inference
coverage. Pairwise comparisons use the common successfully scored track-ID set.
The split unit is `lineage_id`, stratified by source with the published salt
`artifactbench-v2-primary-20260920`; `split_rank_sha256` allows the frozen
assignment to be verified without exposing the private lineage key.

ArtifactNet evaluates seven evenly spaced four-second chunks. The declared v2
policy reports the median of finite scores when at least four chunks are valid;
otherwise the track is an inference failure. A strict any-non-finite sensitivity
result and the chunk-level failure log are included in the result bundle.

## Frozen result snapshot

On the 562-track common-success test intersection, the calibration-selected
results are:

| Model | AUROC | AUPRC | F1 | Balanced accuracy | TPR | FPR |
|---|---:|---:|---:|---:|---:|---:|
| ArtifactNet v9.4 | 0.982 | 0.994 | 0.923 | 0.918 | 0.865 | 0.029 |
| Deezer ISMIR | 0.761 | 0.925 | 0.758 | 0.776 | 0.624 | 0.072 |
| SpecTTTra alpha | 0.299 | 0.688 | 0.159 | 0.526 | 0.087 | 0.036 |
| CLAM | 0.284 | 0.647 | 0.000 | 0.500 | 0.000 | 0.000 |

CLAM's calibration-selected threshold is the next representable value above
1.0 because lower thresholds cannot satisfy the 5% calibration-FPR constraint;
its native 0.5 threshold has TPR 0.889 and FPR 0.942 on the same intersection.
This degenerate operating point must therefore be read together with AUROC and
native-threshold results. ArtifactNet scores 562 of 579 sealed-test attempts;
the other three models score all 579. Treating every ArtifactNet failure as an
adversarial class error gives balanced accuracy 0.865. Exact values, bootstrap
intervals, raw probabilities, and structured failures are in
`results/frozen_protocol_metrics.json`.

## Evaluated baselines

- ArtifactNet v9.4 ONNX revision `e915f0dc5962a57536bbe1f78b66adcb48dfae4c`;
- SpecTTTra alpha 120-second revision `094b32a5545098a71c113f6ae9d5c55310564268`;
- Deezer ISMIR 2025 research detector revision
  `d2180598fed79e3f917e8050a00439982466e5c6`;
- CLAM repository revision `74e3a3277e1dfe9ae9ed433b6e8c51d74e9e1d9b`,
  with checkpoint and encoder digests recorded in the run manifest.

The frozen inference runner revision used for this snapshot is
`55444b33b1725e6f69905716b02e6a881d7c8a07`. Reproducible analysis, independent
metric verification, public-result sanitation, and release-audit tooling were
added in release-candidate revision
`1a4ce7ffae7513dc6ca97380d57c983ee56028ac`.

## Limitations

The cohort is modest and concentrated in selected generator families. Some
platform version labels are incomplete. Public training lineage cannot resolve
all CLAM real-track identities. Cryptographic hashes do not detect every
perceptual derivative. The binary task does not cover hybrid human--AI production.

## Licensing

Benchmark-authored metadata and result files are released under CC BY-NC 4.0.
Runner code is released separately under the repository's MIT License. These
licenses do not grant rights to upstream audio, model weights, platform content,
or third-party metadata. See `LICENSES.md` and `fma_track_licenses.json` before
use.