Improved Robustness in AI-Generated Music Detection
arXiv:2607.27454
The paper introduces a detection pipeline for AI-generated music that remains robust to simple audio manipulations such as speed or pitch changes by mapping audio to a log-frequency axis and using a shift‑invariant cross‑correlation filter.
Abstract
AI music generators leave predictable spectral artifacts determined by their architecture. Existing detectors exploit these artifacts with near-perfect accuracy on raw generated tracks, but their performance collapses under simple audio manipulations, such as speed modification or pitch shifting. We address this open robustness problem by introducing a frequency-scaling-invariant detection pipeline that aims to prevent this kind of attack by design. Our method maps audio onto a log-frequency axis via a log-STFT remapping. A single learned cross-correlation filter, combined with max-pooling, provides shift invariance at inference time. Training uses a hybrid loss that jointly supervises binary detection and artifact-peak localization, regularizing boundary weights. Because robustness to speed change is built in by design, the detector is also interpretable: it outputs both a binary decision and an estimate of the applied speed-change factor.
Proceedings of the 27th ISMIR Conference, Abu Dhabi, UAE, November 08-12, 2026