Fast and Effective Copy-Move Detection of Digital Audio Based on Auto Segment

Abstract

Detecting digital audio forgeries is a significant research focus in the field of audio forensics. In this article, the authors focus on a special form of digital audio forgery—copy-move—and propose a fast and effective method to detect doctored audios. First, the article segments the input audio data into syllables by voice activity detection and syllable detection. Second, the authors select the points in the frequency domain as feature by applying discrete Fourier transform (DFT) to each audio segment. Furthermore, this article sorts every segment according to the features and gets a sorted list of audio segments. In the end, the article merely compares one segment with some adjacent segments in the sorted list so that the time complexity is decreased. After comparisons with other state of the art methods, the results show that the proposed method can identify the authentication of the input audio and locate the forged position fast and effectively.

Blackman–Tukey spectral estimation and electric network frequency matching from power mains and speech recordings

“…Audio forensics has been intensively studied [19][20][21][22]. Audio reverberation significantly affects the quality of an audio recording and small differences can indicate alterations in the original recordings.…”

Spectrogram Dataset of Korean Smartphone Audio Files Forged Using the “Mix Paste” Command

“…Among these, copy-moves are difficult to detect because the forged segments originate from the same audio file [5]. Recently, with the popularity of audio editing software such as Adobe Audition CC, audio file content can be easily edited in various ways [4,6]. In addition to basic editing functions, this software provides the "Mix Paste" command, which selects the desired part of a current audio file, copies it, and combines it by pasting.…”