Mobile Camera Source Identification with SVD

  • A. -R. Soobhany
  • K. P. LamEmail author
  • P. Fletcher
  • D. J. Collins
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 313)


A novel method for extracting the characterising sensor pattern noise (SPN) from digital images is presented. Based on the spectral decomposition technique of Singular Value Decomposition, the method estimates the SPN of each image in terms of its energy level by first transforming the image/signals into a linear additive noise model that separates the photo response non-uniformity (PRNU) of the associated camera from the signal subspace. The camera reference signatures of the individual cameras are computed from a sample of their respective images and compared with a mixture of image signatures from a set of known camera devices. The statistical properties of the method were studied using the Student’s t-test constructed under the null hypothesis formalism. Our studies show that it is possible to determine the source device of digital images from camera phones using such method of signature extraction, with encouraging results.


Source identification Singular value decomposition Digital image forensics Sensor pattern noise PRNU Mobile camera phone 



We are indebted to the generous support of Willy’s World, UK for providing some equipment. Special thanks also to Sofia Shah for providing her personal phones as well as participating in the image acquisition process.


  1. 1.
    A. R. Soobhany, R. Leary and K. P. Lam, “On the Performance of Li’s Unsupervised Image Classifier and the Optimal Cropping Position of Images for Forensic Investigations,” International Journal of Digital Crime and Forensics (IJDCF), vol. 3, pp. 1–13, 2011.Google Scholar
  2. 2.
    J. Lukas, J. Fridrich and M. Goljan, “Digital camera identification from sensor pattern noise,” IEEE Transactions on Information Forensics and Security, vol. 1, pp. 205–214, 2006.Google Scholar
  3. 3.
    C. -T. Li, “Source Camera Identification Using Enhanced Sensor Pattern Noise,” IEEE Transactions on Information Forensics and Security, vol. 5, pp. 280–287, 2010.Google Scholar
  4. 4.
    R. C. Gonzalez and R. E. Woods, “Homomorphic filtering,” in Digital Image Processing, Second ed. New Jersey: Prentice-Hall, 2002, pp. 191.Google Scholar
  5. 5.
    G. Gul and I. Avcibas, “Source cell phone camera identification based on singular value decomposition,” in First IEEE International Workshop on Information Forensics and Security, 2009. WIFS 2009. 2009, pp. 171–175.Google Scholar
  6. 6.
    T. Gloe, M. Kirchner, A. Winkler and R. Böhme, “Can we trust digital image forensics?” in Proceedings of the 15th International Conference on Multimedia, Augsburg, Germany, 2007, pp. 78–86.Google Scholar
  7. 7.
    A. Swaminathan, M. Wu and K. J. R. Liu, “Nonintrusive component forensics of visual sensors using output images,” IEEE Transactions on Information Forensics and Security, vol. 2, pp. 91–106, 2007.Google Scholar
  8. 8.
    K. San Choi, E. Y. Lam and K. K. Y. Wong, “Source camera identification using footprints from lens aberration,” Digital Photography II SPIE, vol. 6069, pp. 172–179, 2006.Google Scholar
  9. 9.
    H. Farid, “Digital image ballistics from JPEG quantization,” Dept. Comput. Sci., Dartmouth College, Hanover, NH, Tech. Rep. TR2006–583, 2006.Google Scholar
  10. 10.
    M. J. Sorell, “Conditions for effective detection and identification of primary quantization of re-quantized JPEG images,” in E-Forensics ‘08: Proceedings of the 1st International Conference on Forensic Applications and Techniques in Telecommunications, Information, and Multimedia and Workshop, Adelaide, Australia, 2008, pp. 1–6.Google Scholar
  11. 11.
    S. Lin, Jinwei Gu, S. Yamazaki and Heung-Yeung Shum, “Radiometric calibration from a single image,” in Proceedings of the 2004 IEEE Computer Society Conference On Computer Vision and Pattern Recognition, 2004. CVPR 2004. 2004, pp. II-938-II-945 Vol.2.Google Scholar
  12. 12.
    O. S. Celiktutan and B. Avcibas, “Blind identification of source cell-phone model,” IEEE Trans on Info Forensics & Security, vol. 3, pp. 553–566, 2008.Google Scholar
  13. 13.
    M. Chen, J. Fridrich, M. Goljan and J. Lukas, “Determining image origin and integrity using sensor noise,” IEEE Trans on Info Forensics & Security, vol. 3, pp. 74–90, 2008.Google Scholar
  14. 14.
    J. Fridrich, “Digital Image Forensic Using Sensor Noise,” IEEE Signal Process. Mag., vol. 26, pp. 26–37, 2009.Google Scholar
  15. 15.
    E. J. Alles, Z. J. M. H. Geradts and C. J. Veenman, “Source camera identification for low resolution heavily compressed images,” in Int’l Conf on Comp Sciences and its Applications, 2008. ICCSA ‘08. 2008, pp. 557–567.Google Scholar
  16. 16.
    T. Berger, Rate Distortion Theory: A Mathematical Basis for Data Compression. NJ: Prentice-Hall, 1971.Google Scholar
  17. 17.
    C. Moler, “Eigenvalues and singular values,” in Numerical Computing with MATLAB The MathWorks, Inc, 2004.
  18. 18.
    H. Andrews and C. Patterson, “Singular value decompositions and digital image processing,” IEEE Transactions on Acoustics, Speech and Signal Processing,, vol. 24, pp. 26–53, 1976.Google Scholar
  19. 19.
    H. Xie, L. E. Pierce and F. T. Ulaby, “Statistical properties of logarithmically transformed speckle,” IEEE Transactions on Geoscience and Remote Sensing, vol. 40, pp. 721–727, 2002.Google Scholar
  20. 20.
    A. El Gamal and H. Eltoukhy, “CMOS image sensors,” Circuits and Devices Magazine, IEEE, vol. 21, pp. 6–20, 2005.Google Scholar
  21. 21.
    K. Irie, A. E. McKinnon, K. Unsworth and I. M. Woodhead, “A model for measurement of noise in CCD digital-video cameras,” Measurement Science and Technology, vol. 19, pp. 045207, 2008.Google Scholar
  22. 22.
    A. R. Soobhany, “Image Source Identification and Characterisation for Forensic Analysis,” Ph.D. Thesis, submitted in October 2012.Google Scholar
  23. 23.
    Chang CI, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer Publishers, 2003.Google Scholar
  24. 24.
    Lam KP et al, A Coarse-Grained Spectral Signature Generator, SPIE Procs, vol 6356, pp. 62560S1–12, 2007.Google Scholar
  25. 25.
    Lam KP and Emery R, Image pixel guided tours – A software platform for non-destructive x-ray imaging, SPIE/IS&T Procs. vol 724, 2009.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • A. -R. Soobhany
    • 1
  • K. P. Lam
    • 1
    Email author
  • P. Fletcher
    • 1
  • D. J. Collins
    • 1
  1. 1.Research Institute for the Environment, Physical Sciences and Applied MathematicsKeele UniversityKeeleUnited Kingdom

Personalised recommendations