Datasets:

intrect
/

artifactbench

	---
	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

	```python
	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

	```bibtex
	@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](https://arxiv.org/abs/2604.16254) · DOI: [10.48550/arXiv.2604.16254](https://doi.org/10.48550/arXiv.2604.16254)

	## License

	CC BY-NC 4.0