Who Is AI Singing As? Singer-Timbre Misuse Detection in AI Music

AI-generated music can imitate the vocal identity of real singers, raising a question beyond DeepFake detection: who is AI singing as? We study this problem as real–synthetic singer verification and identify a substantial identity gap: pretrained speaker-verification models that perform well on real singing can fail severely on synthetic vocals. To study this gap, we develop TimbreTrace, which adapts pretrained speaker-verification models using mixed real–synthetic supervision. Our analysis shows that pretrained models retain source-singer characteristics even when converted vocals are perceived as the intended target singer, while post-training shifts representations toward the target singer and improves real–synthetic verification. We further evaluate how these gains transfer to Zero-day AI Songs. These results establish the real–synthetic singer-identity gap as a challenge for singer verification and show that real–synthetic supervision can markedly reduce it.