发表机构
Fordham University; IBM Research（福特汉姆大学; IBM研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对音频深度伪造检测在真实扰动下泛化性差的问题，提出ROGUE框架，通过双智能体对抗学习动态生成检测工作流，显著提升鲁棒性和泛化能力。
AI 中文摘要
语音合成和语音转换技术的快速发展使得音频深度伪造越来越逼真，在实际应用中构成了严重的安全风险。虽然现有的检测方法在受控条件下表现出较强的性能，但它们在真实世界的扰动和损坏下往往难以泛化。在本文中，我们提出了ROGUE，一个通过编排多个检测工具动态构建鲁棒检测工作流的框架。ROGUE将工作流生成表述为一个序列决策问题，并引入了一种双智能体范式，其中扰动智能体生成音频扰动，策略智能体学习在扰动条件下选择和执行检测工具。通过对抗学习，ROGUE实现了扰动感知的工具选择、自适应执行策略，并提高了对分布偏移的鲁棒性。在多个数据集和真实世界损坏上的大量实验表明，ROGUE在鲁棒性和泛化性方面始终优于强基线。我们的结果凸显了对抗优化的工作流生成在真实部署环境中构建可靠音频深度伪造检测系统的有效性。
英文摘要
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.
