README.md

metadata

license: cc-by-nc-4.0
task_categories:
  - audio-classification
tags:
  - ai-music-detection
  - benchmark
  - forensic
  - audio
language:
  - en
size_categories:
  - 1K<n<10K

ArtifactBench v1 — AI-Generated Music Detection Benchmark

A multi-generator evaluation benchmark for AI-generated music forensic detection, covering 22 AI generators and 6 real music sources.

Dataset Description

• Total tracks: 8,766 (4,383 AI + 4,383 Real, 1:1 balanced)
• AI generators: 22 (MusicGen, Stable Audio, Suno v3/v3.5/v4, Udio, Riffusion, DiffRhythm, Yue, Chirp v2/v3/v3.5, etc.)
• Real sources: 6 (SONICS, MoM, FMA, YouTube)
• Format: AI tracks as Parquet (audio bytes embedded), Real tracks as CSV (YouTube IDs for user download)

Motivation

Existing benchmarks (SONICS: 5 generators, MoM: 6 generators) only measure in-distribution performance. Models reporting high F1 on these benchmarks fail catastrophically on out-of-distribution generators:

• CLAM (194M params, F1=0.925 on MoM) → F1=0.824 on ArtifactBench
• SpecTTTra (19M params, F1=0.97 on SONICS) → F1=0.766 on ArtifactBench

ArtifactBench evaluates what matters for deployment: generalization across diverse generators.

Sanity Check Protocol

Per-source pass/fail thresholds:

• Real source FPR ≤ 5%
• AI source TPR ≥ 90% (Stable Audio: ≥ 60%)
• Codec invariance: mean Δ ≤ 0.15, max Δ ≤ 0.35

Baseline Results

Model	Params	F1	FAIL	Suno v4 TPR	Real FPR
ArtifactNet v9.4	4.2M	0.983	4/28	98%	1.5%
CLAM (MoM)	194M	0.824	16/28	78%	70.5%
SpecTTTra	19M	0.766	23/28	55%	21.4%

Usage

from artifactbench.bench import main
# or
# python -m artifactbench.bench --model artifactnet --manifest artifactbench_v1_manifest.json

Per-Source Breakdown (v1.0.1)

Source	Class	Tracks	bench_origin: test	Generator
aime_musicgen_large	AI	200	30	MusicGen Large
aime_musicgen_medium	AI	200	30	MusicGen Medium
aime_musicgen_small	AI	200	30	MusicGen Small
aime_riffusion	AI	200	30	Riffusion
aime_stable_audio_v1	AI	200	50	Stable Audio v1
aime_stable_audio_v2	AI	200	50	Stable Audio v2
aime_suno_v3	AI	200	30	Suno v3
aime_suno_v35	AI	200	30	Suno v3.5
aime_udio	AI	200	30	Udio (AIME)
mom_diffrythm	AI	200	100	DiffRhythm
mom_riffusion	AI	200	100	Riffusion (MoM)
mom_udio	AI	200	100	Udio (MoM)
mom_yue	AI	200	100	Yue
sonics_chirp-v2-xxl-alpha	AI	200	80	Chirp v2
sonics_chirp-v3	AI	200	80	Chirp v3
sonics_chirp-v3.5	AI	200	80	Chirp v3.5
sonics_udio-120s	AI	200	80	Udio 120s
sonics_udio-30s	AI	200	80	Udio 30s
suno_cdn_latest	AI	200	100	Suno CDN (post-freeze)
suno_extra	AI	200	80	Suno extras
udio_cdn_latest	AI	200	35	Udio CDN (post-freeze) — v1.0.1 balanced
udio_extra	AI	200	80	Udio extras
sonics_real	Real	500	300	SONICS real partition
mom_real	Real	400	200	MoM real (mp3 + wav)
fma_hardneg	Real	300	150	FMA mp3 hard-negatives
mom_extra_real	Real	200	110	MoM extra real
mom_real_wav	Real	200	42	MoM real WAV variants
youtube_hardneg	Real	200	73	YouTube curated hard-negatives
TOTAL	—	6,200	2,280	28 sources, 22 AI generators

Real sources are intentionally over-represented (1,800 total) to enable rigorous FPR estimation across diverse codec and production conditions.

Files

• artifactbench_v1_manifest.json — Track manifest with bench_origin tags
• metadata.json — Dataset statistics and generator list

Citation

@article{oh2026artifactnet,
  title        = {ArtifactNet: Detecting AI-Generated Music via Forensic Residual Physics},
  author       = {Oh, Heewon},
  journal      = {arXiv preprint arXiv:2604.16254},
  year         = {2026},
  eprint       = {2604.16254},
  archivePrefix= {arXiv},
  primaryClass = {cs.SD},
  doi          = {10.48550/arXiv.2604.16254},
  url          = {https://arxiv.org/abs/2604.16254}
}

arXiv: 2604.16254 · DOI: 10.48550/arXiv.2604.16254

License

CC BY-NC 4.0