Advertisement

Scanner Model Identification of Official Documents Using Noise Parameters Estimation in the Wavelet Domain

  • Chaima Ben RabahEmail author
  • Gouenou Coatrieux
  • Riadh Abdelfattah
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11182)

Abstract

In this article, we propose a novel approach for discerning which scanner has been used to scan a particular document. Its originality relates to a signature extracted in the wavelet domain of the digitized documents where the acquisition noise specific to a scanner is located in the first subbands of details. This signature is an estimate of the statistical noise model which is modeled by a General Gaussian distribution (GGD) and whose parameters are estimated in the HH subband by maximizing the likelihood function. These parameters constitute a unique identifier for a scanner. For a given image, we propose to identify its origin by minimizing the Kullback-Leibler divergence between its signature and those of known scanners. Experiments conducted on a real scanned-image database, developed for the validation of the work presented in this paper, show that the proposed approach achieves high detection performance. Total of 1000 images were used in experiments.

Keywords

Digitized documents Scanner identification Image forensics Authenticity Wavelet transform 

Notes

Acknowledgements

This work was financially supported by the “PHC Utique” program of the French Ministry of Foreign Affairs and Ministry of higher education and research and the Tunisian Ministry of higher education and scientific research in the CMCU project number 17G1405.

References

  1. 1.
  2. 2.
    Ferguson, N., Schneier, B., Kohno, T.: Cryptography Engineering: Design Principles and Practical Applications. Wiley, Hoboken (2011)Google Scholar
  3. 3.
    Qadir, M.A., Ahmad, I.: Digital text watermarking: secure content delivery and data hiding in digital documents. IEEE Aerosp. Electron. Syst. Mag. 21(11) (2006)CrossRefGoogle Scholar
  4. 4.
    Swaminathan, A., Min, W., Ray Liu, K.J.: Digital image forensics via intrinsic fingerprints. IEEE Trans. Inf. Forensics Secur. 3(1), 101–117 (2008)CrossRefGoogle Scholar
  5. 5.
    Gloe, T., Franz, E., Winkler, A.: Forensics for flatbed scanners, in security, steganography, and watermarking of multimedia contents IX. Int. Soc. Opt. Photonics 6505, 65051I (2007)Google Scholar
  6. 6.
    Gou, H., Swaminathan, A., Min, W.: Robust scanner identification based on noise features. In: Security, Steganography, and Watermarking of Multimedia Contents IX. International Society for Optics and Photonics, vol. 6505, p. 65050S (2007)Google Scholar
  7. 7.
    Gou, H., Swaminathan, A., Min, W.: Intrinsic sensor noise features for forensic analysis on scanners and scanned images. IEEE Trans. Inf. Forensics Secur. 4(3), 476–491 (2009)CrossRefGoogle Scholar
  8. 8.
    Khanna, N., Mikkilineni, A.K., Delp, E.J.: Scanner identification using feature-based processing and analysis. IEEE Trans. Inf. Forensics Secur. 4(1), 123–139 (2009)CrossRefGoogle Scholar
  9. 9.
    Khanna, N., Mikkilineni, A.K., Chiu, G.T.C., Allebach, J.P., Delp, E.J.: Scanner identification using sensor pattern noise. In: Security, Steganography, and Watermarking of Multimedia Contents IX. International Society for Optics and Photonics, vol. 6505, p. 65051K (2007)Google Scholar
  10. 10.
    Khanna, N., Delp, E.J.: Source scanner identification for scanned documents. In: First IEEE International Workshop on Information Forensics and Security, WIFS 2009, pp. 166–170. IEEE (2009)Google Scholar
  11. 11.
    Joshi, S., Gupta, G., Khanna, N.: Source classification using document images from smartphones and flatbed scanners. In: Rameshan, R., Arora, C., Dutta Roy, S. (eds.) NCVPRIPG 2017. CCIS, vol. 841, pp. 281–292. Springer, Singapore (2018).  https://doi.org/10.1007/978-981-13-0020-2_25CrossRefGoogle Scholar
  12. 12.
    Dirik, A.E., Sencar, H.T., Memon, N.: Flatbed scanner identification based on dust and scratches over scanner platen. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2009, pp. 1385–1388. IEEE (2009)Google Scholar
  13. 13.
    Elsharkawy, Z.F., Abdelwahab, S.A., Dessouky, M.I., Elaraby, S.M., Abd El-Samie, F.E.: Identifying unique flatbed scanner characteristics for matching a scanned image to its source. Digit. Image Process. 5(9), 397–403 (2013)Google Scholar
  14. 14.
    Sugawara, S.: Identification of scanner models by comparison of scanned hologram images. Forensic Sci. Int. 241, 69–83 (2014)CrossRefGoogle Scholar
  15. 15.
    Choi, C.-H., Lee, M.-J., Lee, H.-K.: Scanner identification using spectral noise in the frequency domain. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 2121–2124. IEEE (2010)Google Scholar
  16. 16.
    Findlater, K.M., et al.: A CMOS image sensor with a double-junction active pixel. IEEE Trans. Electron Devices 50(1), 32–42 (2003)CrossRefGoogle Scholar
  17. 17.
    Khanna, N., et al.: A survey of forensic characterization methods for physical devices. Digit. Investig. 3, 17–28 (2006)CrossRefGoogle Scholar
  18. 18.
    Daubechies, I.: Ten Lectures on Wavelets, vol. 61. SIAM (1992)Google Scholar
  19. 19.
    Chang, S.G., Yu, B., Vetterli, M.: Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process. 9(9), 1532–1546 (2000)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Verbeke, J., Cools, R.: The newton-raphson method. Int. J. Math. Educ. Sci. Technol. 26(2), 177–193 (1995)CrossRefGoogle Scholar
  21. 21.
    Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79–86 (1951)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Cochran, M.: A proposed standard procedure to define minimum scanning attribute levels for hard copy documents. In: 2014 47th Hawaii International Conference on System Sciences (HICSS), pp. 2036–2043. IEEE (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Chaima Ben Rabah
    • 1
    • 2
    Email author
  • Gouenou Coatrieux
    • 2
  • Riadh Abdelfattah
    • 1
    • 2
  1. 1.COSIM LabUniversity of Carthage, Higher School of Communications of TunisArianaTunisia
  2. 2.LaTIM Inserm UMR1101, IMT Atlantique, Technopôle Brest-Iroise, CS 83818Brest Cedex 3France

Personalised recommendations