Towards AI-Generated Music Plagiarism Detection as a Version Identification Problem
The rapid expansion of text-to-music generative models challenges traditional paradigms of music creation and intellectual property. Plagiarism in this context is rarely an absolute mathematical binary, but an ambiguous threshold negotiated over harmonic structure, melodic contours, or overall perceived stylistic character. In this work, we test the transferability of state-of-the-art music version identification architectures from the human-to-human cover domain to the human-to-AI plagiarism setting. To evaluate this task, we introduce COPYCAT, a benchmark derived from real-world plagiarism cases and extended through generative re-synthesis and digital signal processing obfuscations, yie...
| Authors | Fotis Koutsikos, Ioannis Prokopiou, Spyridon Kantarelis et al. |
|---|---|
| Primary category | cs.SD |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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