Feature-Based Camera Model Identification Works in Practice

Results of a Comprehensive Evaluation Study
  • Thomas Gloe
  • Karsten Borowka
  • Antje Winkler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5806)

Abstract

Feature-based camera model identification plays an important role in the toolbox for image source identification. It enables the forensic investigator to discover the probable source model employed to acquire an image under investigation. However, little is known about the performance on large sets of cameras that include multiple devices of the same model. Following the process of a forensic investigation, this paper tackles important questions for the application of feature-based camera model identification in real world scenarios. More than 9,000 images were acquired under controlled conditions using 44 digital cameras of 12 different models. This forms the basis for an in-depth analysis of a) intra-camera model similarity, b) the number of required devices and images for training the identification method, and c) the influence of camera settings. All experiments in this paper suggest: feature-based camera model identification works in practice and provides reliable results even if only one device for each camera model under investigation is available to the forensic investigator.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lyu, S., Farid, H.: How realistic is photorealistic? IEEE Transactions on Signal Processing 53(2), 845–850 (2005)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Dehnie, S., Sencar, H.T., Memon, N.: Digital image forensics for identifying computer generated and digital camera images. In: Proceedings of the 2006 IEEE International Conference on Image Processing (ICIP 2006), pp. 2313–2316 (2006)Google Scholar
  3. 3.
    Khanna, N., Mikkikineni, A.K., Chiu, G.T.C., Allebach, J.P., Delp, E.J.: Forensic classification of imaging sensor types. In: Delp, E.J., Wong, P.W. (eds.) Proceedings of SPIE: Security and Watermarking of Multimedia Content IX, vol. 6505 (2007), 65050U Google Scholar
  4. 4.
    Kharrazi, M., Sencar, H.T., Memon, N.: Blind source camera identification. In: Proceedings of the 2004 IEEE International Conference on Image Processing (ICIP 2004), pp. 709–712 (2004)Google Scholar
  5. 5.
    Bayram, S., Sencar, H.T., Memon, N.: Improvements on source camera-model identification based on CFA. In: Proceedings of the WG 11.9 International Conference on Digital Forensics (2006)Google Scholar
  6. 6.
    Çeliktutan, O., Avcibas, İ., Sankur, B.: Blind identification of cellular phone cameras. In: Delp, E.J., Wong, P.W. (eds.) Proceedings of SPIE: Security and Watermarking of Multimedia Content IX, vol. 6505 (2007), 65051H Google Scholar
  7. 7.
    Swaminathan, A., Wu, M., Liu, K.J.R.: Nonintrusive component forensics of visual sensors using output images. IEEE Transactions on Information Forensics and Security 2(1), 91–106 (2007)CrossRefGoogle Scholar
  8. 8.
    Filler, T., Fridrich, J., Goljan, M.: Using sensor pattern noise for camera model identification. In: Proceedings of the 2008 IEEE International Conference on Image Processing (ICIP 2008), pp. 1296–1299 (2008)Google Scholar
  9. 9.
    Lukáš, J., Fridrich, J., Goljan, M.: Determining digital image origin using sensor imperfections. In: Said, A., Apostolopoulus, J.G. (eds.) Proceedings of SPIE: Image and Video Communications and Processing, vol. 5685, pp. 249–260 (2005)Google Scholar
  10. 10.
    Chen, M., Fridrich, J., Goljan, M.: Digital imaging sensor identification (further study). In: Delp, E.J., Wong, P.W. (eds.) Proceedings of SPIE: Security and Watermarking of Multimedia Content IX, vol. 6505 (2007), 65050P Google Scholar
  11. 11.
    Gloe, T., Franz, E., Winkler, A.: Forensics for flatbed scanners. In: Delp, E.J., Wong, P.W. (eds.) Proceedings of SPIE: Security, Steganography, and Watermarking of Multimedia Contents IX, vol. 6505 (2007), 65051I Google Scholar
  12. 12.
    Gou, H., Swaminathan, A., Wu, M.: Robust scanner identification based on noise features. In: Delp, E.J., Wong, P.W. (eds.) Proceedings of SPIE: Security and Watermarking of Multimedia Content IX, vol. 6505 (2007), 65050S Google Scholar
  13. 13.
    Khanna, N., Mikkikineni, A.K., Chiu, G.T.C., Allebach, J.P., Delp, E.J.: Scanner identification using sensor pattern noise. In: Delp, E.J., Wong, P.W. (eds.) Proceedings of SPIE: Security and Watermarking of Multimedia Content IX, vol. 6505 (2007), 65051KGoogle Scholar
  14. 14.
    Goljan, M., Fridrich, J., Filler, T.: Large scale test of sensor fingerprint camera identification. In: Delp, E.J., Dittmann, J., Memon, N., Wong, P.W. (eds.) Proceedings of SPIE: Media Forensics and Security XI, vol. 7254, 7254–18 (2009)Google Scholar
  15. 15.
    Çeliktutan, O., Sankur, B., Avcibas, İ.: Blind identification of source cell-phone model. IEEE Transactions on Information Forensics and Security 3(3), 553–566 (2008)CrossRefGoogle Scholar
  16. 16.
    Farid, H., Lyu, S.: Higher-order wavelet statistics and their application to digital forensics. In: IEEE Workshop on Statistical Analysis in Computer Vision (in conjunction with CVPR) (2003)Google Scholar
  17. 17.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
  18. 18.
    Gloe, T., Böhme, R.: The ‘Dresden’ image database for benchmarking digital image forensics. Submitted to the 25th Symposium on Applied Computing, ACM SAC 2010 (2010)Google Scholar
  19. 19.
    Adams, J., Parulski, K., Spaulding, K.: Color processing in digital cameras. IEEE Micro. 18(6), 20–30 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Thomas Gloe
    • 1
  • Karsten Borowka
    • 1
  • Antje Winkler
    • 1
  1. 1.Institute of Systems ArchitectureTechnische Universität DresdenDresdenGermany

Personalised recommendations