噪声中的快速盲语音复制-移动检测与定位
Fast and Blind Speech Copy-Move Detection and Localization in Noise
AI总结：
针对语音复制-移动伪造检测中计算复杂且对重复单词敏感的问题，提出基于局部特征张量的检测算法，将问题转化为张量匹配过程，实现了高效、鲁棒的盲检测与定位。
摘要：
语音上的复制-移动伪造（CMF）结合后处理技术，给篡改区域的取证检测和定位带来了巨大挑战。大多数现有的CMF检测方法需要对语音进行预分割，以便在这些片段之间进行相似性计算。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多次朗读某个单词的存在敏感等问题。为了解决这些问题，我们提出了一种基于局部特征张量的CMF检测算法，该算法能够将重复检测和定位问题转化为一种特殊的张量匹配过程，并伴有完整的理论分析作为支撑。通过大量实验，我们证明了我们的方法展现出计算效率和对后处理技术的鲁棒性。值得注意的是，它能够有效且盲目地检测篡改片段，甚至是短至几分之一秒的片段。这些优势突显了我们的方法在实际应用中的巨大潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
