# FakeMusicCaps: a Dataset for Detection and Attribution of Synthetic Music Generated via Text-to-Music Models

arXiv: https://arxiv.org/abs/2409.10684 · HTML: https://mlsanity.com/paper/2409.10684/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2024.

## Built on by (most cited first, 13 of 13)

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- [AT-ADD: All-Type Audio Deepfake Detection Challenge Evaluation Plan](https://mlsanity.com/paper/2604.08184/) (2026)
- [AI-Generated Music Detection in Broadcast Monitoring](https://mlsanity.com/paper/2602.06823/) (2026)
- [A Practical Synthesis of Detecting AI-Generated Textual, Visual, and Audio Content](https://mlsanity.com/paper/2504.02898/) (2025)
- [Segment Transformer: AI-Generated Music Detection via Music Structural Analysis](https://mlsanity.com/paper/2509.08283/) (2025)
- [Bias beyond Borders: Global Inequalities in AI-Generated Music](https://mlsanity.com/paper/2510.01963/) (2025)
- [Detecting Musical Deepfakes](https://mlsanity.com/paper/2505.09633/) (2025)
- [Echoes: A semantically-aligned music deepfake detection dataset](https://mlsanity.com/paper/2603.23667/) (2026)
- [Distinguishing AI-Generated Music from Edited Audio as a Hard-Negative Robustness Task](https://mlsanity.com/paper/2608.14916/) (2026)
- [ToolDF: Tool-Integrated Reasoning for Mixed-Authenticity Audio Deepfake Detection](https://mlsanity.com/paper/2609.03620/) (2026)
- [MUUNRiver-Bench: Diagnosing Relation-Dependent Music Retrieval with Multimodal Instructions](https://mlsanity.com/paper/2609.16090/) (2026)

## Abstract (arXiv)

Text-To-Music (TTM) models have recently revolutionized the automatic music generation research field. Specifically, by reaching superior performances to all previous state-of-the-art models and by lowering the technical proficiency needed to use them. Due to these reasons, they have readily started to be adopted for commercial uses and music production practices. This widespread diffusion of TTMs poses several concerns regarding copyright violation and rightful attribution, posing the need of serious consideration of them by the audio forensics community. In this paper, we tackle the problem of detection and attribution of TTM-generated data. We propose a dataset, FakeMusicCaps that contains several versions of the music-caption pairs dataset MusicCaps re-generated via several state-of-the-art TTM techniques. We evaluate the proposed dataset by performing initial experiments regarding the detection and attribution of TTM-generated audio.