Preliminary Study Shows Membership Inference Attacks Limited Against Generative Music Models

Global: Preliminary Study Shows Membership Inference Attacks Limited Against Generative Music Models

A team of researchers released a new arXiv paper in December 2025 that investigates whether membership inference attacks can expose user data or copyrighted works used to train generative music systems. The study focuses on MuseGAN, a widely cited model for creating multi-instrument music, and evaluates several established attack techniques. By supplying a set of music records alongside the trained model, the researchers aim to determine if any of those records were part of the training data.

Background

Generative artificial intelligence has rapidly expanded across image, text, and audio modalities, prompting heightened scrutiny of privacy risks and the use of copyrighted material during model training. Membership inference attacks sit at the intersection of these concerns, offering a method to infer whether specific data points contributed to a model’s learning process. In domains such as healthcare, such attacks could reveal sensitive patient information, while rights‑holders view them as potential evidence of unauthorized use of protected works.

Research Gap in Music

Although prior investigations have examined membership inference in image and speech models, the literature lacks an assessment of these attacks on generative music. Given the multi‑billion‑dollar size of the music industry and the artistic stakes involved, the authors argue that understanding the vulnerability of music models is a pressing research priority.

Methodology

The authors selected MuseGAN, a benchmark generative adversarial network for music synthesis, as the target model. They applied several known membership inference techniques that have been successful on other audio and visual generators. The experiments involved constructing candidate record sets, querying the model, and measuring the attacks’ ability to correctly label training versus non‑training samples.

Findings

Results indicate that the music data used to train MuseGAN exhibits a notable degree of resilience. Across the evaluated attacks, the success rates did not significantly exceed random guessing, suggesting that current membership inference methods are less effective for generative music than for other media types.

Implications

For privacy advocates, the findings provide tentative reassurance that generative music models may pose a lower risk of exposing individual contributors’ data. Conversely, copyright owners seeking technical proof of unauthorized training may find existing attacks insufficient in the music domain, highlighting a gap between legal expectations and technical capabilities.

Future Directions

The authors recommend extending the analysis to additional music generation architectures, larger datasets, and emerging attack strategies. They also suggest exploring defensive mechanisms that could further harden music models against inference attempts while preserving creative quality.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.