# From Audio Deepfake Detection to AI-Generated Music Detection -- A Pathway and Overview

arXiv: https://arxiv.org/abs/2412.00571 · HTML: https://mlsanity.com/paper/2412.00571/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2024.

## Built on by (most cited first, 11 of 11)

- [Detect All-Type Deepfake Audio: Wavelet Prompt Tuning for Enhanced Auditory Perception](https://mlsanity.com/paper/2504.06753/) (2025)
- [Interpretable All-Type Audio Deepfake Detection with Audio LLMs via Frequency-Time Reinforcement Learning](https://mlsanity.com/paper/2601.02983/) (2026)
- [Watermarking for AI Content Detection: A Review on Text, Visual, and Audio Modalities](https://mlsanity.com/paper/2504.03765/) (2025)
- [Double Entendre: Robust Audio-Based AI-Generated Lyrics Detection via Multi-View Fusion](https://mlsanity.com/paper/2506.15981/) (2025)
- [AT-ADD: All-Type Audio Deepfake Detection Challenge Evaluation Plan](https://mlsanity.com/paper/2604.08184/) (2026)
- [A Practical Synthesis of Detecting AI-Generated Textual, Visual, and Audio Content](https://mlsanity.com/paper/2504.02898/) (2025)
- [Fusion Segment Transformer: Bi-Directional Attention Guided Fusion Network for AI-Generated Music Detection](https://mlsanity.com/paper/2601.13647/) (2026)
- [MixAssist: An Audio-Language Dataset for Co-Creative AI Assistance in Music Mixing](https://mlsanity.com/paper/2507.06329/) (2025)
- [Echoes: A semantically-aligned music deepfake detection dataset](https://mlsanity.com/paper/2603.23667/) (2026)
- [Enhancing Efficiency and Performance in Deepfake Audio Detection through Neuron-level Dropin & Neuroplasticity Mechanisms](https://mlsanity.com/paper/2603.24343/) (2026)
- [Assessing AI-generated music detection in real-world broadcast monitoring](https://mlsanity.com/paper/2608.07359/) (2026)

## Abstract (arXiv)

As Artificial Intelligence (AI) technologies continue to evolve, their use in generating realistic, contextually appropriate content has expanded into various domains. Music, an art form and medium for entertainment deeply rooted in human culture, is seeing an increased involvement of AI into its production. However, the unregulated use of AI music generation (AIGM) tools raises concerns about potential negative impacts on the music industry, copyright, and artistic integrity, underscoring the importance of effective AIGM detection. This paper provides a systematic overview of existing AIGM detection methods. We first establish a four-level detection taxonomy: signal-level, feature-level, watermark, and semantic consistency, organising methods according to the type of trace they exploit. Drawing on the more mature field of audio deepfake detection, we then present a stratified transferability analysis that examines which components may or may not transfer to AIGM detection, and under what conditions. A multi-dimensional classification further organises representative methods along input modality, detection granularity, feature type, model type, detection target, robustness setting, and interpretability. We conclude by discussing implications and proposing directions for future research to address ongoing challenges in the field.