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README.md

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+---
+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
+```
+
+## 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},
+  year={2026}
+}
+```
+
+## License
+
+CC BY-NC 4.0