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.

本研究针对使用神经语音编辑技术进行的欺骗攻击问题，提出了新的“语音填充编辑(SINE)”数据集，以应对现有欺骗检测方法的不足。通过引入更复杂的语音编辑技术，研究显示相较于传统的剪切和粘贴编辑，利用该数据集训练的检测器在识别和定位方面表现卓越，对欺骗攻击的防御具有重要意义。

检测不可检测的：评估当前欺骗检测方法对无缝语音编辑的有效性