Abstract
As advances in synthetic voice generation accelerate, an increasing variety of fake voice generators have emerged, producing audio that is often indistinguishable from real human speech. This evolution poses new and serious threats across sectors where audio recordings serve as critical evidence. Although fake voice detectors are also advancing, the arms race between fake voice generation and detection has become more intense and complex. In this work, we present the first large-scale, cross-domain evaluation of fake voice detectors, benchmarking 8 state-of-the-art models against datasets synthesized by 20 different fake voice generation systems. To the best of our knowledge, this is the most comprehensive cross-domain assessment conducted to date. Our study reveals substantial security vulnerabilities in current fake voice detection systems, underscoring critical gaps in their real-world robustness. To advance the field, we propose a unified and effective metric that consolidates the diverse and often inconsistent evaluation criteria previously used across different studies. This metric enables standardized, straightforward comparisons of the robustness of fake voice detectors. We conclude by offering actionable recommendations for building more resilient fake voice detection technologies, with the broader goal of reinforcing the foundations of AI security and trustworthiness.
Abstract (translated)
随着合成语音生成技术的快速发展，越来越多的伪造语音生成工具出现，它们产生的音频往往难以与真实的人类语音区分。这种演变在依赖录音作为关键证据的各个行业中带来了新的和严重的威胁。尽管伪造语音检测器也在进步，但伪造语音生成和检测之间的军备竞赛变得更加激烈且复杂。在这项工作中，我们首次进行了大规模、跨领域的伪造语音检测器评估，将8种最先进的模型与20个不同伪造语音生成系统产生的数据集进行基准测试。据我们所知，这是迄今为止最全面的跨领域评估。 我们的研究揭示了当前伪造语音检测系统的重大安全漏洞，强调了它们在实际应用中的鲁棒性存在关键差距。为了推动该领域的进步，我们提出了一种统一且有效的度量标准，将不同研究中常用的多样化和经常不一致的评价标准整合起来。这一指标使得伪造语音检测器的鲁棒性对比变得更加标准化和直观。 最后，我们提供了一系列可操作的建议，旨在构建更具弹性的伪造语音检测技术，并以此为目标加强人工智能的安全性和可信度基础。
URL
https://arxiv.org/abs/2510.06544
