Source Camera Model Identification Using Features from Contaminated Sensor Noise

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9569)

Abstract

This paper presents a new approach of camera model identification. It is based on using the noise residual extracted from an image by applying a wavelet-based denoising filter in a machine learning framework. We refer to this noise residual as the polluted noise (POL-PRNU), because it contains a PRNU signal contaminated with other types of noise such as the image content. Our proposition consists of extracting high order statistics from POL-PRNU by computing co-occurrences matrix. Additionally, we enrich the set of features with those related to CFA demosaicing artifacts. These two sets of features feed a classifier to perform a camera model identification. The experimental results illustrate the fact that machine learning techniques with discriminant features are efficient for camera model identification purposes.

Keywords

Camera model identification POL-PRNU CFA Co-occurrences matrix Feature extraction Rich model 

References

  1. 1.
    Lukas, J., Fridrich, J., Goljan, M.: Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 1(2), 205–214 (2006)CrossRefGoogle Scholar
  2. 2.
    Choi, S., Lam, E.Y., Wong, K.K.Y.: Source camera identification using footprints from lens aberration. In: Proceedings of the SPIE, vol. 6069, San Jose, CA, p. 60690J60690J8 (2006)Google Scholar
  3. 3.
    Dirik, A.E., Sencar, H.T., Memon, N.: Source camera identification based on sensor dust characteristics. In: IEEE Workshop on Signal Processing Applications for Public Security and Forensics, SAFE 2007, Washington, USA, 11–13 April 2007Google Scholar
  4. 4.
    Bayram, S., Sencar, H.T., Memon, N.: Improvements on source camera model identification based on CFA interpolation. In: International Conference on Digital Forensics, Orlando, FL (2006)Google Scholar
  5. 5.
    Kharrazi, M., Sencar, H.T., Memon, N.: Blind source camera identification. In: 2004 International Conference on Image Processing, ICIP 2004, vol. 1, pp. 709–712, October 2004Google Scholar
  6. 6.
    Celiktutan, O., Sankur, B., Avcibas, I.: Blind identification of source cell-phone model. IEEE Trans. Inf. Forensics Secur. 3(3), 553–566 (2008)CrossRefGoogle Scholar
  7. 7.
    Fridrich, J.: Digital image forensic using sensor noise. IEEE Signal Process. Mag. 26(2), 26–37 (2009)CrossRefGoogle Scholar
  8. 8.
    Goljan, M., Fridrich, J.: Estimation of lens distortion correction from single images. In: Proceedings of the SPIE, Electronic Imaging, MediaWatermarking, Security, and Forensics, San Francisco, CA, 26 February 2014Google Scholar
  9. 9.
    Goljan, M., Fridrich, J.: Camera identification from cropped and scaled images. In: Proceedings of the SPIE, Electronic Imaging, Forensics, Security, Steganography, and Watermarking of Multimedia Contents X, San Jose, CA, 26–31 January 2008Google Scholar
  10. 10.
    Li, C.-T., Satta, R.: Empirical investigation into the correlation between vignetting effect and the quality of sensor pattern noise. IET Comput. Vis. 6, 560–566 (2012)CrossRefGoogle Scholar
  11. 11.
    Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)CrossRefGoogle Scholar
  12. 12.
    Qiu, X., Li, H., Luo, W., Huang, J.: A universal image forensic strategy based on steganalytic model. In: Proceedings of the 2nd ACM Workshop on Information Hiding and Multimedia Security IHMMSec, Salzburg, Austria, pp. 165–170 (2014)Google Scholar
  13. 13.
    Cozzolino, D., Gragnaniello, D., Verdoliva, L.: Image forgery detection through residual-based local descriptors and block-matching. In: IEEE International Conference on Image Processing (ICIP), Paris, France, pp. 5297–5301, October 2014Google Scholar
  14. 14.
    Filler, T., Fridrich, J., Goljan, M.: Using sensor pattern noise for camera model identification. In: Proceedings of the 15th IEEE International Conference on Image Processing ICIP, San Diego, California, 12–15 October, pp. 1296–1299 (2008)Google Scholar
  15. 15.
    Bengio, Y., Delalleau, O., Le Roux, N.: The curse of dimensionality for local kernel machines, Technical report 1258, Département d’informatique et recherche opérationnelle, Université de Montréal (2005)Google Scholar
  16. 16.
    Gloe, T., Böhme, R.: The dresden image database for benchmarking digital image forensics. In: Proceedings of the ACM Symposium on Applied Computing, SAC 2010, New York, NY, USA, pp. 1584–1590 (2010)Google Scholar
  17. 17.
    Chang, C.-C., Lin, C.-J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27:127:27 (2011). Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm
  18. 18.
    Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A practical guide to support vector classification, Technical report, Department of Computer Science, National Taiwan University (2003). http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Nîmes UniversityNîmes Cedex 1France
  2. 2.Montpellier University, UMR 5506-LIRMMMontpellierFrance
  3. 3.CNRS, UMR 5506-LIRMMMontpellier Cedex 5France

Personalised recommendations