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ArtifactNet is a lightweight neural network framework that detects AI-generated music by analyzing codec-specific artifacts in audio signals, achieving F1=0.9829 on a new 6,183-track benchmark (ArtifactBench) with 49x fewer parameters than competing methods. The approach uses forensic physics principles to extract codec residuals through a bounded-mask UNet and compact CNN, with codec-aware training reducing cross-codec drift by 83%.