指向隐藏之处：通过信号指针网络揭露语音音频拼接
Point to the Hidden: Exposing Speech Audio Splicing via Signal Pointer Nets
AI总结：
本文提出SigPointer指针网络框架，用于在无约束场景下高效检测语音音频拼接，实验显示性能提升6到10个百分点。
摘要：
验证用于刑事调查的录音证据完整性是音频取证分析师工作的一个组成部分。其中一个重点是检测删除或插入操作，即所谓的音频拼接。虽然这是一种相对容易的改变口头陈述的方法，但仔细的编辑可以产生相当令人信服的结果。对于困难案例或大量数据，自动化工具可以支持检测潜在的编辑位置。为此，目前已提出了多种分析方法和深度学习方法。然而，很少有方法解决实践中预期的无约束拼接场景。通过SigPointer，我们提出了一种用于连续输入的指针网络框架，它比现有工作更自然、更高效地揭示拼接位置。在具有取证挑战性的数据（如强压缩和含噪信号）上进行的大量实验量化了指针机制的优势，性能提升了约6到10个百分点。
英文摘要：
Verifying the integrity of voice recording evidence for criminal investigations is an integral part of an audio forensic analyst's work. Here, one focus is on detecting deletion or insertion operations, so called audio splicing. While this is a rather easy approach to alter spoken statements, careful editing can yield quite convincing results. For difficult cases or big amounts of data, automated tools can support in detecting potential editing locations. To this end, several analytical and deep learning methods have been proposed by now. Still, few address unconstrained splicing scenarios as expected in practice. With SigPointer, we propose a pointer network framework for continuous input that uncovers splice locations naturally and more efficiently than existing works. Extensive experiments on forensically challenging data like strongly compressed and noisy signals quantify the benefit of the pointer mechanism with performance increases between about 6 to 10 percentage points.
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。
