检测不可检测之物：评估当前欺骗检测方法对抗无缝语音编辑的有效性
Detecting the Undetectable: Assessing the Efficacy of Current Spoof Detection Methods Against Seamless Speech Edits
AI总结：
针对 Voicebox 等无缝神经语音编辑带来的欺骗风险，本文构建 SINE 数据集并证明自监督检测器在检测、定位和跨方法泛化上表现优异。
摘要：
神经语音编辑的发展引发了人们对其被滥用于欺骗攻击的担忧。传统的部分编辑语音语料库主要关注剪切粘贴式编辑，这类编辑虽然保持了说话人一致性，但往往会引入可检测的不连续性。近期方法（如 A³T 和 Voicebox）通过利用上下文信息改善了过渡效果。为推动欺骗检测研究，我们推出了使用 Voicebox 创建的 Speech INfilling Edit（SINE）数据集。我们详细介绍了重新实现 Voicebox 训练和数据集创建的过程。主观评估证实，与传统剪切粘贴方法相比，使用这种新技术编辑的语音更难检测。尽管人类难以辨别，但实验结果表明，基于自监督的检测器在检测、定位以及跨不同编辑方法的泛化方面能够取得显著性能。该数据集和相关模型将公开发布。
英文摘要：
Neural speech editing advancements have raised concerns about their misuse in spoofing attacks. Traditional partially edited speech corpora primarily focus on cut-and-paste edits, which, while maintaining speaker consistency, often introduce detectable discontinuities. Recent methods, like A\textsuperscript{3}T and Voicebox, improve transitions by leveraging contextual information. To foster spoofing detection research, we introduce the Speech INfilling Edit (SINE) dataset, created with Voicebox. We detailed the process of re-implementing Voicebox training and dataset creation. Subjective evaluations confirm that speech edited using this novel technique is more challenging to detect than conventional cut-and-paste methods. Despite human difficulty, experimental results demonstrate that self-supervised-based detectors can achieve remarkable performance in detection, localization, and generalization across different edit methods. The dataset and related models will be made publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
