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：验证刑事调查中语音记录证据的完整性是音频取证分析师工作的核心环节。其中，重点在于检测删除或插入操作——即所谓的音频拼接。尽管这是一种相对简单的篡改口头陈述的方式，但经过精心编辑后可能产生颇具说服力的结果。对于疑难案件或海量数据，自动化工具可协助定位潜在的编辑位置。目前虽已有多种分析及深度学习方法被提出，但鲜有方法能应对实际中预期的无约束拼接场景。我们提出的SigPointer是一种面向连续输入的指针网络框架，能够比现有方法更自然地揭示拼接位置，且效率更高。通过对强压缩和噪声信号等具有取证挑战性的数据进行广泛实验，我们量化了指针机制的效能优势，其性能提升幅度约为6至10个百分点。