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Source Cell-Phone Identification Using Spectral Features of Device Self-noise

  • Chao Jin
  • Rangding WangEmail author
  • Diqun Yan
  • Biaoli Tao
  • Yanan Chen
  • Anshan Pei
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10082)

Abstract

Source cell-phone identification has become a hot topic in multimedia forensics recently. In this paper, we propose a novel cell-phone identification method based on the recorded speech files. Device self-noise is considered as the fingerprint of the cell-phone, and the self-noise is estimated from the near-silent segments of recording. Moreover, two categories of spectral features of self-noise, i.e., spectral shape features (SN-SSF) and spectral distribution features (SN-SDF), are extracted for closed-set classification using SVM classifier. Experimental results show that the self-noise has the ability to identify the cell-phones of 24 different models, and identification accuracies of 89.23% and 94.53% have been obtained for SN-SSF and SN-SDF, respectively. To the best of our knowledge, it is the first attempt to comprehensively define the self-noise of cell-phone and furthermore apply it to source identification issue of audio forensics.

Keywords

Audio forensics Source cell-phone identification Self-noise Noise estimation Spectral features 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61672302, 61300055), Zhejiang Natural Science Foundation (Grant No. LZ15F020010, Y17F020051), Ningbo University Fund (Grant No. XKXL1405, XKXL1420, XKXL1509, XKXL1503) and K.C. Wong Magna Fund in Ningbo University.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Chao Jin
    • 1
  • Rangding Wang
    • 1
    Email author
  • Diqun Yan
    • 1
  • Biaoli Tao
    • 1
  • Yanan Chen
    • 1
  • Anshan Pei
    • 1
  1. 1.College of Information Science and EngineeringNingbo UniversityNingboChina

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