Copy-move forgery on speech (CMF), coupled with post-processing techniques, presents a great challenge to the forensic detection and localization of tampered areas. Most of the existing CMF detection approaches necessitate pre-segmentation of speech to facilitate similarity calculations among these segments. However, these approaches usually suffer from the problems of uncontrollable computational complexity and sensitivity to the presence of a word that is read multiple times within a speech recording. To address these issues, we propose a local feature tensors-based CMF detection algorithm that can transform duplicate detection and localization problems into a special tensor-matching procedure, accompanied by complete theoretical analysis as support. Through extensive experimentation, we have demonstrated that our method exhibits computational efficiency and robustness against post-processing techniques. Notably, it can effectively and blindly detect tampered segments, even those as short as a fractional second. These advantages highlight the promising potential of our approach for practical applications.

翻译：复制-移动伪造（CMF）结合后处理技术，对篡改区域的取证检测与定位构成了巨大挑战。现有大多数CMF检测方法需预先对语音进行分割，以便于计算各段之间的相似性。然而，这些方法通常面临计算复杂度不可控以及对语音录音中重复朗读同一词语的敏感性等问题。为解决上述问题，我们提出一种基于局部特征张量的CMF检测算法，该算法能将重复检测与定位问题转化为特殊的张量匹配过程，并辅以完整理论分析作为支撑。通过广泛的实验，我们证明了该方法具备计算高效性以及对后处理技术的鲁棒性。尤为重要的是，它能有效且盲检地识别甚至短至亚秒级的篡改段。这些优势突显了我们的方法在实际应用中的巨大潜力。