Artifactnet

🛡️ Patent Pending (KR + PCT) The bounded-mask residual extraction and codec-invariant training methods used in ArtifactNet are covered by pending patent applications. Use of the ONNX build under CC BY-NC 4.0 grants no patent license; commercial deployment requires both a commercial license and a patent license (contact above for both).

i️ What is released A pre-compiled, end-to-end ONNX inference build of the full pipeline (STFT → UNet → HPSS → 7-channel CNN → sigmoid). Raw PyTorch weights, training code, and training data are not publicly released. This is a deliberate scope limitation — the released binary is sufficient to reproduce inference numbers reported in our paper, but does not enable fine-tuning or weight extraction.

Model Description

• Architecture: ArtifactUNet (3.6M) + 7ch HPSS Forensic CNN (424K) = 4.2M total
• Input: 44.1kHz mono audio, 4-second segments
• Output: P(AI) ∈ [0, 1] per segment, song-level median verdict
• Format: Single ONNX file (entire pipeline: STFT → UNet → HPSS → 7ch → CNN → sigmoid)

Performance — ArtifactBench v0.9 (test-only fair eval, all models unseen)

Evaluated on 2,263 tracks (bench_origin=test, unseen by all three models), threshold τ=0.5, identical preprocessing.

Usage

import onnxruntime as ort
import numpy as np
import soundfile as sf

# Load ONNX inference build
sess = ort.InferenceSession("artifactnet_v94_full.onnx")

# Load audio (44.1kHz mono, 4-second chunk)
audio, sr = sf.read("track.wav", dtype="float32")
if audio.ndim > 1:
    audio = audio.mean(axis=1)
chunk = audio[:4 * 44100].reshape(1, -1).astype(np.float32)

# Inference
prob = sess.run(None, {"audio": chunk})[0][0]
print(f"P(AI) = {prob:.4f}")  # > 0.5 → AI-generated

For song-level verdict, compute median over multiple chunks.

Benchmark

Evaluate with ArtifactBench v1.

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 — Free for academic, research, and personal use. Commercial use is prohibited without prior written permission. This includes (but is not limited to):

• Selling access to the ONNX build or its outputs
• Integrating into commercial products, SaaS, or APIs
• Using the model to generate revenue, directly or indirectly
• Attempting to extract weights for derivative commercial models

For commercial licensing inquiries: [email protected]

Patent Notice

Patent applications covering the core methods of ArtifactNet are pending in Korea (KR) and via the Patent Cooperation Treaty (PCT). The CC BY-NC 4.0 license on this ONNX inference build does not convey any patent rights. Commercial use, even under a commercial copyright license, requires a separate patent license. Academic and research use within the scope of CC BY-NC 4.0 is permitted without separate patent license, consistent with standard research-use exemptions.