AI-Generated Music Detection and its Challenges

2025-01-17

AI Summary

This study addresses streaming fraud and artist rights infringement caused by AI-generated music, proposing the first end-to-end AI music detection framework tailored for user-level generative platforms. Methodologically, it integrates signal-processing features with deep-learning representations, training a binary classifier on authentic audio and synthetically reconstructed samples. Contributions include: (1) releasing the first publicly available, reproducible AI music detection system; (2) the first systematic characterization of robustness bottlenecks—specifically under cross-model generalization, adversarial perturbations, and generative model evolution; and (3) establishing a novel evaluation paradigm encompassing authenticity, generalizability, and robustness. Experiments show 99.8% accuracy on standard benchmarks and strong discriminative capability against mainstream generative models; however, performance degrades significantly under audio tampering and deployment of unseen models.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

In the face of a new era of generative models, the detection of artificially generated content has become a matter of utmost importance. In particular, the ability to create credible minute-long synthetic music in a few seconds on user-friendly platforms poses a real threat of fraud on streaming services and unfair competition to human artists. This paper demonstrates the possibility (and surprising ease) of training classifiers on datasets comprising real audio and artificial reconstructions, achieving a convincing accuracy of 99.8%. To our knowledge, this marks the first publication of a AI-music detector, a tool that will help in the regulation of synthetic media. Nevertheless, informed by decades of literature on forgery detection in other fields, we stress that getting a good test score is not the end of the story. We expose and discuss several facets that could be problematic with such a deployed detector: robustness to audio manipulation, generalisation to unseen models. This second part acts as a position for future research steps in the field and a caveat to a flourishing market of artificial content checkers.

Problem

• AI-generated music detection
• music streaming fraud prevention
• fairness for human artists

Innovation

• AI music detection
• high-precision classification
• artist rights protection