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：语音拷贝-移动篡改（CMF）结合后处理技术，对篡改区域的取证检测与定位构成了重大挑战。现有CMF检测方法大多需要对语音进行预分割，以便计算各片段间的相似度。然而，这些方法通常存在计算复杂度不可控以及对语音录音中重复朗读的单词敏感等问题。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，该算法可将重复检测与定位问题转化为特殊的张量匹配过程，并辅以完整的理论分析作为支撑。通过大量实验，我们证明了该方法具有计算高效性以及对后处理技术的鲁棒性。值得注意的是，该方法能够有效且盲地检测被篡改的片段，即使短至几分之一秒的片段也不例外。这些优势彰显了该方法在实际应用中的巨大潜力。