1 research outputs found
Fast Blind Audio Copy-Move Detection and Localization Using Local Feature Tensors in Noise
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
application