MUSICSHIELD: Protection for Musicians in the Era of Generative AI

Recent advancements in music-generative AI systems pose a growing threat to professionals in the music production industry. These models learn from large datasets, often scraping publicly available music, and can edit, remix, and replicate an artist's signature style without their consent. In this paper, we introduce MusicShield, a tool that enables musicians to apply “music shields” to their work before public release. These shields introduce subtle imperceptible perturbations to the audio signal, preventing generative models from learning or generating new music based on the artist's work. While recent work (e.g., HarmonyCloak) focuses primarily on making music unlearnable to disrupt AI training, MusicShield not only prevents AI from training on music but also thwarts editing and manipulation, with improved scalability, lower computational cost, and better cross-model transferability. To evaluate MusicShield, we conducted user studies with 470 music professionals and enthusiasts to assess its effectiveness, usability, and perceptual tolerability, as well as their views on AI-driven music editing. Additionally, our quantitative evaluations across four state-of-the-art generative models (i.e., MusicLM, MusicGen, Jasco, and Riffusion) demonstrate its robustness and broad applicability. Our analysis shows that MusicShield withstands varied conditions and adaptive countermeasures while remaining lightweight and cost-efficient. User study results, together with quantitative metrics, confirm that MusicShield11Music samples from different settings and scenarios are available for listening at https://artyshield.ai/musicshield/. provides a practical and reliable solution for blocking unauthorized AI learning and editing in the generative AI era.