ArtifactNet: Detecting AI-Generated Music via Forensic Residual Physics
Detecting artificially generated music requires distinguishing synthesis-related traces from musical content and artifacts introduced by audio distribution. We present ArtifactNet, a compact framework based on learned forensic residuals. A pretrained music source separator supplies residual targets during training, but the final system replaces it with ArtifactUNet, a task-directed bounded-mask extractor. Seven channels describe harmonic and percussive residual structure, their balance, and temporal variation, and a lightweight convolutional classifier produces recording scores. The extractor and classifier contain 4.03 million parameters. We evaluate the retained checkpoints on a frozen, recording-level test containing 534 generated and 479 real recordings after conservative source-family and identity exclusions from the reconstructed training lineage. At the pre-established threshold of 0.225, ArtifactNet obtains F1=0.9671, 93.63% recall, and no observed false positive; AUROC and average precision are 0.9982 and 0.9988. Released SpecTTTra and CLAM checkpoints are evaluated on the same files under their model-specific frontends. A diagnostic further shows that the released CLAM head changes substantially with execution batch composition. In a separate paired four-codec study on 100 real and 100 generated recordings, codec-aware extraction reduces the mean score range from 0.276 to 0.033 for real music but increases it from 0.035 to 0.249 for generated music; Opus recall falls from 96% to 74%. The results support compact residual detection while exposing class-dependent codec and implementation sensitivities.