  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.
