The increasing availability of audio editing software altering digital audios and their ease of use allows create forgeries at low cost. A copy-move forgery (CMF) is one of easiest and popular audio forgeries, which created by copying and pasting audio segments within the same audio, and potentially post-processing it. Three main approaches to audio copy-move detection exist nowadays: samples/frames comparison, acoustic features coherence searching and dynamic time warping. But these approaches will suffer from computational complexity and/or sensitive to noise and post-processing. In this paper, we propose a new local feature tensors-based copy-move detection algorithm that can be applied to transformed duplicates detection and localization problem to a special locality sensitive hash like procedure. The experimental results with massive online real-time audios datasets reveal that the proposed technique effectively determines and locating copy-move forgeries even on a forged speech segment are as short as fractional second. This method is also computational efficient and robust against the audios processed with severe nonlinear transformation, such as resampling, filtering, jsittering, compression and cropping, even contaminated with background noise and music. Hence, the proposed technique provides an efficient and reliable way of copy-move forgery detection that increases the credibility of audio in practical forensics applications

翻译：随着音频编辑软件日益普及且操作简便，数字音频可被低成本篡改。复制-移动伪造（CMF）是最简单且常见的音频篡改手段之一，其通过在同一音频内复制粘贴片段并可能进行后处理实现。现有音频复制-移动检测方法主要有三类：样本/帧比较、声学特征一致性搜索及动态时间规整。但这些方法存在计算复杂度高和/或对噪声及后处理敏感等问题。本文提出一种基于局部特征张量的新型复制-移动检测算法，可将变换副本检测与定位问题转化为类局部敏感哈希流程。基于海量在线实时音频数据集的实验结果表明，即便伪造语音片段短至亚秒级，该技术也能有效识别并定位复制-移动伪造。该方法计算高效且对严重非线性变换（如重采样、滤波、抖动、压缩、裁剪）处理后的音频具有鲁棒性，甚至能抵抗背景噪声与音乐干扰。因此，该技术为复制-移动伪造检测提供了高效可靠的解决方案，增强了音频在实用取证中的可信度。