arXiv:2603.23667v2 Announce Type: replace-cross Abstract: We introduce Echoes, a new dataset for music deepfake detection designed for training and benchmarking detectors under realistic and provider-diverse conditions. Echoes comprises 4,468 tracks (131 hours of audio) spanning multiple genres (pop, rock, electronic), and includes content generated by ten popular AI music generation systems. To prevent shortcut learning and promote robust generalization, the dataset is deliberately constructed to be challenging, enforcing semantic-level alignment between spoofed audio and bona fide references
The proliferation of sophisticated AI music generation systems necessitates advanced detection methods to ensure authenticity and intellectual property rights in the rapidly evolving audio landscape.
This development is crucial for establishing trust in digital media, mitigating the risks of synthetic content, and supporting the integrity of creative industries as AI capabilities advance.
The availability of a robust dataset like Echoes will significantly improve the accuracy and generalizability of music deepfake detection, moving from theoretical understanding to practical application.
- · Audio forensics researchers
- · Music industry rightsholders
- · AI safety and ethics organizations
- · Content authentication platforms
- · Creators of malicious deepfakes
- · Platforms without robust detection
- · Ephemeral deepfake detection methods
Improved deepfake detection capabilities will be integrated into music platforms and content distribution channels.
This will lead to a 'cat and mouse' game where deepfake generation and detection continuously evolve in sophistication.
The development could influence intellectual property law regarding AI-generated content and the standards for authentic digital media.
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Read at arXiv cs.AI