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Detecting and locating digital audio forgeries based on singularity analysis with wavelet packet

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Abstract

Audio watermarking and signature are widely used for authentication. However, these techniques will become powerless in many actual situations because of their requirement of additional information. Audio forensic techniques are necessary for digital audio. In this paper, we propose an audio forensics scheme to detect and locate speech audio forged operations in time domain (including deletion, insertion, substitution and splicing) by performing discrete wavelet packet decomposition and analyzing singularity points of audio signals. We first analyze the forged operations and find that the audio signals will often generate new singular points because of the decrease or breaking of the correlation property of those samples close to the tampering position. Then we utilize the singularity analysis based on wavelet packet and design five parameters (which is different for the sample rate of digital audio file) to propose an approach which can detect and locate audio forgeries in time domain. Finally, extensive experimental results have demonstrated that the proposed method can better achieve the goals that identify whether a given speech file has been tampered (e.g., part of the content deleted or replaced) previously and further locate the forged positions in time domain.

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Authors and Affiliations

  1. School of Information Science and Technology, Jinan University, Guangzhou, 510632, China

    Jiaorong Chen, Shijun Xiang, Hongbin Huang & Weiping Liu

Authors
  1. Jiaorong Chen
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  2. Shijun Xiang
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  3. Hongbin Huang
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  4. Weiping Liu
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Corresponding author

Correspondence to Weiping Liu.

Additional information

This work was supported in part by NSFC (No.61272414), and in part by The University-Industry-Science Partnership Project of Guangdong Province and National Education Ministry (2012B091000155).

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Chen, J., Xiang, S., Huang, H. et al. Detecting and locating digital audio forgeries based on singularity analysis with wavelet packet. Multimed Tools Appl 75, 2303–2325 (2016). https://doi.org/10.1007/s11042-014-2406-3

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  • DOI: https://doi.org/10.1007/s11042-014-2406-3

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