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.

翻译：验证刑事调查中录音证据的完整性，是音频取证分析师工作的核心组成部分。其中，一个重点任务是检测删除或插入操作，即所谓的音频拼接。尽管这是一种相当容易篡改口头陈述的手段，但精心编辑仍能产生颇为令人信服的效果。对于疑难案件或海量数据，自动化工具可以辅助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，很少有方法能解决实践中预期的无约束拼接场景。通过SigPointer，我们提出了一种适用于连续输入的指针网络框架，它能够比现有工作更自然、更高效地揭示拼接位置。针对强压缩和噪声干扰等具有取证挑战性的数据所进行的大量实验，量化了指针机制的好处，性能提升了约6到10个百分点。