{
  "id": 6795,
  "url": "https://arxiv.org/abs/2603.23667v2",
  "title": "Echoes: A semantically-aligned music deepfake detection dataset",
  "summary": "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 betw",
  "authors": "Octavian Pascu, Dan Oneata, Horia Cucu, Nicolas M. Muller",
  "category": "research",
  "topics": "safety-alignment,misinformation",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-24T19:10:42.000Z",
  "fetched_at": "2026-07-14T16:32:45.893Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6795",
  "original_url": "https://arxiv.org/abs/2603.23667v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}