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Audio splicing detection and localization using environmental signature

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Abstract

Audio splicing is one of the most common manipulation techniques in the area of audio forensics. In this paper, the magnitudes of acoustic channel impulse response and ambient noise are proposed as the environmental signature. Specifically, the spliced audio segments are detected according to the magnitude correlation between the query frames and reference frames via a statically optimal threshold. The detection accuracy is further refined by comparing the adjacent frames. The effectiveness of the proposed method is tested on two data sets. One is generated from TIMIT database, the second is made in four acoustic environments using a commercial grade microphones. Experimental results show that the proposed method not only detects the presence of spliced frames, but also localizes the forgery segments with near perfect accuracy. Comparison results illustrate that the identification accuracy of the proposed scheme is higher than the previous schemes. In addition, experimental results also show that the proposed scheme is also superior to the previous works. A real-world meeting recording database (AMI corpus) is also used to verify the effectiveness of the proposed method for practical applications.

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Notes

  1. 1 In [3], the average false alarm rate is near 30 % for the sampling rate of 16kHz.

  2. 2 In our implementation, the recommended parameters in [3] do not work on our data sets. The optimal parameters used in this paper are manually selected by trials and errors.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant No. 61402219), 2013 Guangdong Natural Science Funds for Distinguished Young Scholar (S2013050014223), the NPST program by the King Saud University under grant number 12-INF2634-02 and a grant from the National Science Foundation (CNS-1440929).

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, South University of Science and Technology of China, Shenzhen, Guangdong, China, 518055

    Hong Zhao, Yifan Chen & Rui Wang

  2. Department of Electrical and Computer Engineering, University of Michigan – Dearborn, Dearborn, MI, 48128, USA

    Hafiz Malik

Authors
  1. Hong Zhao
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  2. Yifan Chen
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  3. Rui Wang
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  4. Hafiz Malik
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Correspondence to Hong Zhao.

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Zhao, H., Chen, Y., Wang, R. et al. Audio splicing detection and localization using environmental signature. Multimed Tools Appl 76, 13897–13927 (2017). https://doi.org/10.1007/s11042-016-3758-7

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  • DOI: https://doi.org/10.1007/s11042-016-3758-7

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