Architecture
How it works
ArtifactNet reframes AI music detection as forensic residual physics. Instead of learning generator-specific stylistic fingerprints, it isolates the physical residue that neural audio codecs imprint on generated audio. A bounded-mask UNet extracts those codec residuals from the magnitude spectrogram, HPSS decomposes them into multi-channel forensic features, and a compact CNN classifies real vs. AI-generated.
STFT
Magnitude
spectrogram
ArtifactUNet
Bounded-mask UNet
codec residual extractor
HPSS decomp.
Median-filter HPSS →
7-channel forensic features
CNN classifier
Compact CNN
residual structure scoring
Verdict
Real vs. AI
+ calibrated confidence
The mechanism: commercial AI music generators (Suno, Udio, Stable Audio, MusicGen, Riffusion) all rely on neural audio codecs that use Residual Vector Quantization (RVQ), mapping continuous audio onto discrete codebook vectors. When a separation model trained only on human music meets AI-generated audio, the reconstruction residuals have measurably different structure — a phenomenon the paper terms forensic residual amplification. ArtifactNet detects that structural difference, so it generalizes to generators never seen in training. Read the paper (arXiv:2604.16254) →
RVQ residue
Residual Vector Quantization leaves structured codebook-quantization residue across the spectrum. ArtifactNet keys on this rather than on audible style cues.
Codec reconstruction error
Neural-codec decoders produce reconstruction residuals whose structure diverges from human recordings — amplified by a separation model trained only on real music.
Unseen-generator robustness
Because the signal is generation-mechanism physics, not generator-specific patterns, detection holds up on generators absent from the training set.
Use cases
Who uses it
Distributors
Catalog-scale artifact detection
Batch-process thousands of tracks via REST API. Flag AI-generated content with RVQ signatures before ingestion into your catalog. JSONL / CSV / XLSX results export.
Mastering
Pre-master cleanup
Insert de-artifact on the 2-bus before limiting. Seven factory presets tuned for different generator profiles — AI aggressive, lossy conservative, HPSS harmonic focus.
Platforms
Ingestion pipeline integration
REST API with batch create / append / commit flow. Async processing via worker queue. Results polled by job ID. Works within standard 10 MiB request limits via R2 presigned URLs.
Producers
Real-time DAW plug-in
VST3 / CLAP for macOS and Windows. 186 ms ECO latency for live monitoring, 500 ms Standard for final print. No cloud dependency — all inference on-device.
Benchmark
ArtifactBench results
ArtifactBench is a held-out evaluation set of 6,183 tracks spanning 22 AI music generators (Suno, Udio, Stable Audio, MusicGen, Riffusion, EnCodec, and 16 others) and 6 real music sources. Results are from the paper: Oh, H. (2026). ArtifactNet: Detecting AI-Generated Music via Forensic Residual Physics. arXiv:2604.16254.
0.9829
F1 score
vs. CLAM 0.7576 · SpecTTTra 0.7713
(identical eval conditions)
1.49%
False positive rate
Real music incorrectly flagged as AI
5–10 s
Inference per track
4-minute track on RTX 4090
OOD
Out-of-distribution robust
Generalizes to unseen generators not in training set
FAQ
Common questions
What AI music generators does ArtifactNet detect?
ArtifactNet detects AI-generated music from any generator that uses neural audio codecs with residual vector quantization (RVQ), including Suno, Udio, Stable Audio, MusicGen, Riffusion, and EnCodec-based systems. Because it targets the physics of RVQ residuals rather than generator-specific patterns, it also generalizes to unseen generators not present in training.
Is ArtifactNet a watermark detector?
No. ArtifactNet is not a watermark detector and does not require any cooperation from the generator. It analyzes forensic residual patterns — the physical artifacts that neural audio codecs inevitably leave behind — without any planted signal.
Is ArtifactNet open source?
Model weights and inference code are available on the Hugging Face repository under a CC BY-NC 4.0 license (non-commercial, personal, and research use). The paper is freely available at arXiv:2604.16254.
How accurate is ArtifactNet?
On ArtifactBench — a held-out set of 6,183 tracks across 22 AI generators and 6 real music sources — ArtifactNet achieves an F1 score of 0.9829 with a false positive rate of 1.49%, outperforming prior models including CLAM (F1 0.7576, FPR 69.26%) and SpecTTTra (F1 0.7713, FPR 19.43%) evaluated under identical conditions with published checkpoints.
Can I try ArtifactNet without signing up?
Yes. The free web demo at demo.intrect.io lets you upload any WAV, MP3, or FLAC file (or paste a YouTube URL) and get an AI-vs-human verdict instantly. No account required.
How do I use ArtifactNet at scale?
The ArtifactNet REST API (app.intrect.io) supports batch jobs — create a batch, append audio files, commit, and poll for results in JSONL, CSV, or XLSX format. Free tier includes 10 tracks/month with no credit card required.
API
Quick start
# 1. Upload tracks and start a batch job
curl -X POST https://api.intrect.io/v1/batch/create \
-H "Authorization: Bearer $TOKEN"
# 2. Append audio files
curl -X POST https://api.intrect.io/v1/batch/$BATCH_ID/files \
-H "Authorization: Bearer $TOKEN" \
-F "files=@track.wav"
# 3. Commit to queue
curl -X POST https://api.intrect.io/v1/batch/$BATCH_ID/commit \
-H "Authorization: Bearer $TOKEN"
# 4. Poll for results (JSONL / CSV / XLSX)
curl https://api.intrect.io/v1/batch/$BATCH_ID/results?format=jsonl \
-H "Authorization: Bearer $TOKEN"
Pricing
Simple, usage-based plans
Free
$0
10 tracks / month
- Batch API access
- JSONL / CSV export
- No credit card required
Open dashboard →
Artist
$9.99/mo
20 tracks / month
- Everything in Free
- API key access
- Priority support
Upgrade →
Most popular
Creator
$49/mo
150 tracks / month
- Everything in Artist
- DDEX / Apple export
- Batch history 90 days
Get Creator →
Pro
$149/mo
500 tracks / month
- Everything in Creator
- Multiple API keys
- SLA support
Get Pro →
REST API
Batch processing
Upload hundreds of tracks, process asynchronously, download structured results. Free tier included — no credit card required to start.
Get API access →
DAW Plug-in
Real-time, on-device
VST3 / CLAP for macOS and Windows. 14-day full trial, no account required. Same ArtifactNet model, running entirely on your hardware.
Download trial →