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Robust Workflow Generation via Adversarial Learning for Audio Deepfake Detection — arXiv:2609.20063

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.20063
License (as archived): CC0-1.0
Archived: 2026-10-10T03:24:02.088Z
SHA-256 of the archived text: fc621f999d2364de7b6ac88cd1b23f792b223939a1edde2917da970eaf837307
Stable URL of this page: https://metraipolis.com/documento/dc_f22d87f09dbf

How to cite

Robust Workflow Generation via Adversarial Learning for Audio Deepfake Detection — arXiv:2609.20063 — arXiv (en), https://arxiv.org/abs/2609.20063 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f22d87f09dbf (Metraipolis archive dc_f22d87f09dbf, sha256 fc621f999d2364de7b6ac88cd1b23f792b223939a1edde2917da970eaf837307)

BibTeX

@misc{dcf22d87f09dbf,
  title = {Robust Workflow Generation via Adversarial Learning for Audio Deepfake Detection — arXiv:2609.20063},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.20063},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f22d87f09dbf; sha256 fc621f999d2364de7b6ac88cd1b23f792b223939a1edde2917da970eaf837307},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.20063. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The rapid advancement of speech synthesis and voice conversion technologies has made audio deepfakes increasingly realistic, posing serious security risks in practical applications. While existing detection methods achieve strong performance under controlled conditions, they often fail to generalize under real-world perturbations and corruptions. In this paper, we propose ROGUE, a framework that dynamically constructs robust detection workflows by orchestrating multiple detection tools. ROGUE formulates workflow generation as a sequential decision-making problem and introduces a dual-agent paradigm, where a perturbation agent generates audio perturbations and a policy agent learns to select and execute detection tools under perturbed conditions. Through adversarial learning, ROGUE enables perturbation-aware tool selection, adaptive execution strategies, and improved robustness to distribution shifts. Extensive experiments across multiple datasets and real-world corruptions demonstrate that ROGUE consistently outperforms strong baselines in both robustness and generalization. Our results highlight the effectiveness of adversarially optimized workflow generation for building reliable audio deepfake detection systems in real-world deployment settings.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-18 in cs.AI).
Categorie arXiv: Sound (cs.SD) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.20063 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-10T03:04:13.896Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).