Cell-phone origin identification based on spectral features of device self-noise

Anshan PEI; Rangding WANG; Diqun YAN

doi:10.11959/j.issn.1000-0801.2017019

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Cell-phone origin identification based on spectral features of device self-noise

research and development | 更新时间：2024-06-05

- Cell-phone origin identification based on spectral features of device self-noise
- Telecommunications Science Vol. 33, Issue 1, Pages: 85-94(2017)
- 作者机构：
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- DOI：10.11959/j.issn.1000-0801.2017019
  CLC： TP391
- Published Online：2017-01，
  
  Published：15 January 2017
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Anshan PEI, Rangding WANG, Diqun YAN. Cell-phone origin identification based on spectral features of device self-noise[J]. Telecommunications science, 2017, 33(1): 85-94.
DOI：

Anshan PEI, Rangding WANG, Diqun YAN. Cell-phone origin identification based on spectral features of device self-noise[J]. Telecommunications science, 2017, 33(1): 85-94. DOI： 10.11959/j.issn.1000-0801.2017019.

摘要

随着手机录音设备的普及以及各种功能强大且易于操作的数字媒体编辑软件的出现，手机来源识别已成为多媒体取证领域的热点问题。将本底噪声作为手机的“指纹”，提出了一种基于本底噪声的手机来源识别方法。该方法先通过静音段录音的估计得到本底噪声；然后计算本底噪声的频谱特征在时间轴方向上的均值，将其作为手机来源识别的分类特征；最后采用主成分分析（PCA）法对特征进行降维，并采用支持向量机（SVM）进行分类。实验部分对24款主流型号的手机进行了分类，结果表明本文方法的平均识别准确率（accuracy）和平均召回率（recall）达到了99.24%，同时也验证了相比MFCC，本底噪声有更加优越的性能。

Abstract

With the widespread availability of cell-phone recording devices and the availability of various powerful and easy-to-use digital media editing software

source cell-phone identification has become a hot topic in multimedia forensics.A novel cell-phone identification method was proposed based on the recorded speech.Firstly

device self-noise (DSN) was considered as the fingerprint of the cell-phone and estimated from the silent segments of the speech.Then

the mean of the noise's spectrum was extracted as the identification.Principal components analysis (PCA) was applied to reduce the feature dimension.Support vector machine (SVM) was adopted as the classifier to determine the source of the detecting speech.Twenty-four popular models of the cell-phones were evaluated in the experiment.The experimental results show that the average identification accuracy and recall of the method can reach up to 99.24% and demonstrate that the self-noise feature has more superior performance than the MFCC feature.

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references

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