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[2512.09654] Membership and Dataset Inference Attacks on Large Audio Generative Models
Summary
This paper explores membership and dataset inference attacks on large audio generative models, assessing their implications for copyright protection and dataset accountability.
Why It Matters
As audio generative models become more prevalent, understanding how to protect artists' rights and verify the use of their works in training datasets is crucial. This research highlights the effectiveness of dataset inference attacks as a potential solution for copyright concerns, making it relevant for artists, developers, and policymakers in the AI and creative sectors.
Key Takeaways
- Membership inference attacks are limited in effectiveness for large datasets.
- Dataset inference attacks can successfully verify if an artist's works were used in model training.
- The findings suggest a new approach for copyright protection in the era of AI-generated content.
- Combining evidence from multiple samples enhances the accuracy of dataset inference.
- This research contributes to ongoing discussions about accountability in AI and copyright laws.
Computer Science > Machine Learning arXiv:2512.09654 (cs) [Submitted on 10 Dec 2025 (v1), last revised 13 Feb 2026 (this version, v2)] Title:Membership and Dataset Inference Attacks on Large Audio Generative Models Authors:Jakub Proboszcz, Paweł Kochanski, Karol Korszun, Donato Crisostomi, Giorgio Strano, Emanuele Rodolà, Kamil Deja, Jan Dubinski View a PDF of the paper titled Membership and Dataset Inference Attacks on Large Audio Generative Models, by Jakub Proboszcz and 7 other authors View PDF HTML (experimental) Abstract:Generative audio models, based on diffusion and autoregressive architectures, have advanced rapidly in both quality and expressiveness. This progress, however, raises pressing copyright concerns, as such models are often trained on vast corpora of artistic and commercial works. A central question is whether one can reliably verify if an artist's material was included in training, thereby providing a means for copyright holders to protect their content. In this work, we investigate the feasibility of such verification through membership inference attacks (MIA) on open-source generative audio models, which attempt to determine whether a specific audio sample was part of the training set. Our empirical results show that membership inference alone is of limited effectiveness at scale, as the per-sample membership signal is weak for models trained on large and diverse datasets. However, artists and media owners typically hold collections of works rather th...
