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Analysis of Denoising Filters for Source Identification Using PRNU Features

  • Nadia SiddiquiEmail author
  • Syeda Shira Moin
  • Saiful Islam
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 900)

Abstract

In Digital Image Forensics, one of the important techniques is Source Camera Identification (SCI) that attempts to identify the source camera of captured images. The sensor patterns of captured images are used for identification. The most regular pattern noise is photo response non-uniformity (PRNU) that can be generated by sensor defects during manufacturing process. These noises are distinguishable due to different sensor vendors of devices. In this work, identification is based on mobile camera that is from which mobile model a given image is captured. Here the analysis of three different denoising filters (Wiener, Total Variation and Gaussian) are done, to get the best result in our dataset. For classification, support vector machine (SVM) classifier is used and validation is done using 10-fold cross-validation technique.

Keywords

Photo Response non-Uniformity Wiener Total variation Gaussian 

References

  1. 1.
    Z. Geradts, J. Bijhold, M. Kieft, K. Kurosawa, K. Kuroki, N. Saitoh, Methods for identification of images acquired with digital cameras, in Enabling Technologies for Law Enforcement and Security (2001)Google Scholar
  2. 2.
    X. Kang, J. Chen, K. Lin, P. Anjie, A context-adaptive SPN predictor for trustworthy source camera identification. EURASIP J. Image Video Proc. 2014(1), 19 (2014)CrossRefGoogle Scholar
  3. 3.
    R. Lukac, K. Plataniotis, Secure single-sensor digital camera. Electron. Lett. 42, 627 (2006)CrossRefGoogle Scholar
  4. 4.
    J. Zhao, Q. Wang, J. Guo, L. Gao, F. Yang, An overview on passive image forensics technology for automatic computer forgery. Int. J. Digit. Crime Forensics 8, 14–25 (2016)CrossRefGoogle Scholar
  5. 5.
    H. Farid, Digital doctoring: how to tell the real from the fake. Significance 3, 162–166 (2006)MathSciNetCrossRefGoogle Scholar
  6. 6.
    K. Choi, E. Lam, K. Wong, Automatic source camera identification using the intrinsic lens radial distortion. Opt. Express 14, 11551 (2006)CrossRefGoogle Scholar
  7. 7.
    J. Luka, J. Fridrich, M. Goljan, Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 1, 205–214 (2006)CrossRefGoogle Scholar
  8. 8.
    Y. Hong, Analysis and comparison of several pattern recognition methods in source camera identification. J. Electron. Meas. Instrum. 26, 367–371 (2013)CrossRefGoogle Scholar
  9. 9.
    P. Wighton, T. Lee, H. Lui, D. McLean, M. Atkins, Chromatic aberration correction: an enhancement to the calibration of low-cost digital dermoscopes. Skin Res. Technology 17, 339–347 (2011)CrossRefGoogle Scholar
  10. 10.
    L. Xiaolin, Based on wavelet transform plane principal component inspection application research of image denoising algorithm. Int. J. Signal Proc. Image Proc. Pattern Recogn. 8, 19–28 (2015)Google Scholar
  11. 11.
    M. Chen, J. Fridrich, M. Goljan, J. Lukas, Determining image origin and integrity using sensor noise. IEEE Trans. Inf. Forensics Secur. 3, 74–90 (2008)CrossRefGoogle Scholar
  12. 12.
    F. Gisolf, A. Malgoezar, T. Baar, Z. Geradts, Improving source camera identification using a simplified total variation based noise removal algorithm. Digit. Invest. 10, 207–214 (2013)CrossRefGoogle Scholar
  13. 13.
    J. Janesick, M. Blouke, Scientific charge-coupled devices: past, present, & future. Opt. Photonics News 6, 16 (1995)CrossRefGoogle Scholar
  14. 14.
    X. Kang, Y. Li, Z. Qu, J. Huang, Enhancing source camera identification performance with a camera reference phase sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 7, 393–402 (2012)CrossRefGoogle Scholar
  15. 15.
    C.-T. Li, Source camera identification using enhanced sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 5, 280–287 (2010)CrossRefGoogle Scholar
  16. 16.
    U. Venkata, S. Sugumaran, R. Naskar, K-unknown models detection through clustering in blind source camera identification. IET Image Proc. 12(7), 1204–1213 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Nadia Siddiqui
    • 1
    Email author
  • Syeda Shira Moin
    • 1
  • Saiful Islam
    • 1
  1. 1.Department of Computer EngineeringZakir Husain College of Engineering and Technology, Aligarh Muslim UniversityAligarhIndia

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