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arXiv AI By Xiang Li, Pin-Yu Chen, Wenqi Wei

Robust Workflow Generation via Adversarial Learning for Audio Deepfake Detection

Read the original on arXiv AI →

The paper introduces ROGUE, a framework that builds robust audio deepfake detection workflows by combining multiple detection tools. ROGUE treats workflow creation as a sequential decision problem and uses a dual-agent system: a perturbation agent generates audio distortions while a policy agent selects and executes detection tools that are resilient to those perturbations. Experiments on several datasets and real-world corruptions show that ROGUE consistently outperforms strong baselines in robustness and generalization, demonstrating the value of adversarially optimized workflow generation for reliable deployment.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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