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:修改数字音频的音频编辑软件越来越多,而且易于使用,因此可以低成本地创造伪造材料。复制式仿冒(CMF)是一种最容易和流行的音频伪造工具,它通过在同一音频内复制和粘贴音频段而制作,并有可能加以后处理。现在存在着三种主要的音频复制式探测方法:样本/框架比较、声频特征一致性搜索和动态时间扭曲。但这些方法将受到计算复杂性和/或对噪音和后处理敏感的影响。在本文中,我们提议一种新的本地地貌特征高压复印移动探测算法,可以用来改变重复的探测和本地化问题,将其转化为特殊地点敏感程序。大规模在线实时音频数据集的实验结果表明,拟议的技术有效确定和定位影印在伪造的语音段上的影音动伪造伪造假音的伪造器,甚至只是几分数第二。这种方法也具有计算效率和稳健性,与经过严重非线性转换处理的音频音频探测方法相比,例如重新勘查勘、过滤、缩、压缩和裁剪辑和裁剪辑等方法,提供了一种有效的实际取证技术,从而提供了可靠的背景和复制。