- Title
- Blind detection of copy-move forgery in digital audio forensics
- Creator
- Imran, Muhammad; Ali, Zulfiqar; Bakhsh, Sheikh; Akram, Sheeraz
- Date
- 2017
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/181260
- Identifier
- vital:15896
- Identifier
-
https://doi.org/10.1109/ACCESS.2017.2717842
- Identifier
- ISBN:2169-3536 (ISSN)
- Abstract
- Although copy-move forgery is one of the most common fabrication techniques, blind detection of such tampering in digital audio is mostly unexplored. Unlike active techniques, blind forgery detection is challenging, because it does not embed a watermark or signature in an audio that is unknown in most of the real-life scenarios. Therefore, forgery localization becomes more challenging, especially when using blind methods. In this paper, we propose a novel method for blind detection and localization of copy-move forgery. One of the most crucial steps in the proposed method is a voice activity detection (VAD) module for investigating audio recordings to detect and localize the forgery. The VAD module is equally vital for the development of the copy-move forgery database, wherein audio samples are generated by using the recordings of various types of microphones. We employ a chaotic theory to copy and move the text in generated forged recordings to ensure forgery localization at any place in a recording. The VAD module is responsible for the extraction of words in a forged audio, these words are analyzed by applying a 1-D local binary pattern operator. This operator provides the patterns of extracted words in the form of histograms. The forged parts (copy and move text) have similar histograms. An accuracy of 96.59% is achieved, the proposed method is deemed robust against noise. © 2013 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Access Vol. 5, no. (2017), p. 12843-12855
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright 2017 IEEE
- Rights
- Open Access
- Subject
- 40 Engineering; 46 Information and Computing SciencesAudio forgery; Authentication; Blind detection; Copy-move forgery; Digital multimedia forensics
- Full Text
- Reviewed
- Funder
- This work was supported by the Deanship of Scientific Research of King Saud University, Riyadh Saudi Arabia through the Research Group, under Project RG-1435-051.
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