Hugging Face's logo

Datasets:
intrect
/
artifactbench

Tasks:
Audio Classification
Modalities:
Audio
Languages:
English
Size:
1K<n<10K
ArXiv:
Tags:
ai-music-detection
benchmark
forensic
audio
License:
Dataset card Files
xet
 Community
1
Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 101, in _split_generators
                  pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
                             ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
                  batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
                  examples = [ujson_loads(line) for line in original_batch.splitlines()]
                              ~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
                  return pd.io.json.ujson_loads(*args, **kwargs)
                         ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

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.

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%

Public Model Comparison — 8-way (v1.1 test partition, 2026-07-04)

Eight publicly-available detectors scored on identical files with the same runner (adapters in artifactbench/models/), τ = 0.5. n = 2,104 (1,388 AI / 716 real — the locally-restored real set; see the provenance caveat in v1.1/RESULTS_8WAY.md).

Rank Model Params F1 Precision Recall (TPR) FPR Sanity FAIL
1 ArtifactNet v9.4 (public ONNX) 4.2M 0.952 0.932 97.3% 13.8% 8/28
2 AI-Music-Detection AST-60s 90.8M 0.840 0.848 83.1% 28.9% 16/28
3 CLAM (MoM) 194.3M 0.787 0.711 88.3% 69.7% 14/28
4 SpecTTTra α-120s 18.7M 0.777 0.880 69.5% 18.4% 22/28
5 Deezer ISMIR fakeprint LR 3.6K 0.754 0.906 64.6% 13.0% 18/28
6 FST (Mippia, arXiv:2601.13647) 174.4M 0.735 0.984 58.7% 1.8% 17/28
7 DeepFense EAT+Nes2Net — 0.650 0.589 72.4% 97.8% 11/28
8 SpecTTTra β-5s 18.7M 0.563 0.884 41.3% 10.5% 24/28

The ArtifactNet production pipeline (v9.7 / cnn_v95, PyTorch — not the public ONNX export above) measured on the same partition: F1 0.984 / TPR 98.9% / FPR 4.2% (1/28 FAIL). Per-source TPR/FPR tables, ROC summaries, and notes: v1.1/RESULTS_8WAY.md.

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

v1.1 (2026-07-03) — integrity-audit purged test partition

An audit of the test partition against the ArtifactNet v9.4 training manifest found 34 overlapping real tracks (all YouTube-derived); 5 further real tracks became unrecoverable. v1.1/ ships the purged partition (n = 2,224), a per-track status CSV, and official v1.1 result bounds. v1 files are unchanged — results computed on v1 remain reproducible. See v1.1/RESULTS_v1.1.md.

8-way public model comparison (2026-07-04)

Extends the v1.1 evaluation with five more publicly-available detectors. The summary table is in Public Model Comparison above; per-source breakdowns and notes are in v1.1/RESULTS_8WAY.md.
Downloads last month
666
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)
Total file size:
16 GB

Papers for intrect/artifactbench