Harmonycloak: Making Music Unlearnable for Generative AI

Recent advances in generative AI have significantly expanded into the realms of art and music. This development has opened up a vast realm of possibilities, pushing the boundaries of human creativity into unexplored frontiers. However, as generative AI advances, it can replicate artistic styles and produce new artwork, posing significant concerns for the perceived rarity and value of artists' creations. In response to these challenges, it is becoming increasingly crucial to establish and enforce protective measures that safeguard artists' copyrighted work from unauthorized exploitation by generative AI models. In this paper, we introduce the first defensive mechanism, HARMONYCLOAK, to prevent the exploitative use of artwork, specifically in the context of instrumental music, by generative AI models. Particularly, HARMONYCLOAK employs imperceptible error-minimizing noise to make the model's generative loss approach zero for these perturbed music data, tricking the model into believing nothing can be learned so as to disrupt their attempts to replicate musical structures and styles. By using a set of intra-track and inter-track objective metrics and a subjective user study, extensive experiments on three state-of-the-art music generative AI models (i.e., MuseGAN, SymphonyNet, and MusicLM) validate the effectiveness and applicability of Harmonycloak^1.1.Audio examples of the unlearnable music examples are available for listening at https://mosis.eecs.utk.edu/harmonycloak.html. in both white-box and black-box settings.