Source Classification Using Document Images from Smartphones and Flatbed Scanners

  • Sharad Joshi
  • Gaurav Gupta
  • Nitin KhannaEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 841)


With technological advancements, digital scans of printed documents are increasingly used in many systems in place of the original hard copy documents. This convenience to use digital scans comes at increased risk of potentially fraudulent and criminal activities due to their easy manipulation. To curb such activities, identification of source corresponding to a scanned document can provide important clues to investigating agencies and also help build a secure communication system. This work utilizes local tetra patterns to capture unique device-specific signatures from images of printed documents. In this first of its kind work for scanner identification, the method uses all characters to train a single classifier thereby, reducing the amount of training data required. The proposed method depicts font size independence when tested on an existing scanner dataset and a novel step towards font shape independence when tested on a smart phone dataset of comparable size (Supplementary material and code is available at


Scanner forensics Source scanner identification Smartphone identification Printed documents Local tetra patterns 



This material is based upon work supported by the Board of Research in Nuclear Sciences (BRNS), Department of Atomic Energy (DAE), Government of India under the project DAE-BRNS-ATC-34/14/45/2014-BRNS and Visvesvaraya Ph.D. Scheme, Ministry of the Electronics & Information Technology (MeitY), Government of India. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.


  1. 1.
    Abramova, S., Bohme, R.: Detecting copy-move forgeries in scanned text documents. Electron. Imag. 8, 1–9 (2016)Google Scholar
  2. 2.
    Amerini, I., Caldelli, R., Del Bimbo, A., Di Fuccia, A., Saravo, L., Rizzo, A.P.: Copy-move forgery detection from printed images. In: IS&T/SPIE Electronic Imaging, p. 90280Y (2014)Google Scholar
  3. 3.
    Chiang, P.J., Khanna, N., Mikkilineni, A.K., Segovia, M.V.O., Suh, S., Allebach, J.P., Chiu, G.T.C., Delp, E.J.: Printer and scanner forensics. IEEE Signal Process. Mag. 26(2), 72–83 (2009)CrossRefGoogle Scholar
  4. 4.
    Choi, C.H., Lee, M.J., Lee, H.K.: Scanner identification using spectral noise in the frequency domain. In: 17th IEEE International Conference on Image Processing, ICIP, pp. 2121–2124 (2010)Google Scholar
  5. 5.
    Elsharkawy, Z., Abdelwahab, S., Dessouky, M., Elaraby, S., El-Samie, F.: Identifying unique flatbed scanner characteristics for matching a scanned image to its source. In: 30th IEEE National Radio Science Conference, NRSC, pp. 298–305 (2013)Google Scholar
  6. 6.
    Gloe, T., Franz, E., Winkler, A.: Forensics for flatbed scanners. In: Proceedings of SPIE Security, Steganography, and Watermarking of Multimedia Contents IX, p. 65051I (2007)Google Scholar
  7. 7.
    Gou, H., Swaminathan, A., Wu, M.: 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.
    Joshi, S., Khanna, N.: Single classifier-based passive system for source printer classification using local texture features. arXiv preprint arXiv:1706.07422 (2017)
  9. 9.
    Khanna, N., Delp, E.J.: Intrinsic signatures for scanned documents forensics: effect of font shape and size. In: Proceedings of IEEE International Symposium on Circuits and Systems, ISCAS, pp. 3060–3063 (2010)Google Scholar
  10. 10.
    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
  11. 11.
    Murala, S., Maheshwari, R., Balasubramanian, R.: Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans. Image Process. 21(5), 2874–2886 (2012)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Ojala, T., Pietikäinen, M., Mäenpää, M.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  13. 13.
    Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns. Springer, London (2011). 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.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Xu, G., Shi, Y.Q.: Camera model identification using local binary patterns. In: IEEE International Conference on Multimedia and Expo, ICME, pp. 392–397 (2012)Google Scholar
  17. 17.
    Zhang, B., Gao, Y., Zhao, S., Liu, J.: Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans. Image Process. 19(2), 533–544 (2010)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Multimedia Analysis and Security (MANAS) Lab, Electrical EngineeringIndian Institute of Technology Gandhinagar (IITGN)GandhinagarIndia

Personalised recommendations