Advertisement

Using Benford’s Law Divergence and Neural Networks for Classification and Source Identification of Biometric Images

  • Aamo IorliamEmail author
  • Anthony T. S. Ho
  • Adrian Waller
  • Xi Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10082)

Abstract

It is obvious that tampering of raw biometric samples is becoming an important security concern. The Benford’s law, which is also called the first digit law has been reported in the forensic literature to be very effective in detecting forged or tampered data. In this paper, the divergence values of Benford’s law are used as input features for a Neural Network for the classification and source identification of biometric images. Experimental analysis shows that the classification and identification of the source of the biometric images can achieve good accuracies between the range of 90.02% and 100%.

Keywords

Benford’s law Neural network Biometric images 

References

  1. 1.
    Harper, W.W.: Fingerprint forgery transferred latent fingerprints. J. Crim. Law Criminol. 28(4), 573–580 (1937)Google Scholar
  2. 2.
    Iorliam, A., Ho, A.T.S., Poh, N.: Using Benford’s Law to detect JPEG biometric data tampering. Biometrics 2014, London (2014)Google Scholar
  3. 3.
    Hildebrandt, M., Dittmann, J.: Benford’s Law based detection of latent fingerprint forgeries on the example of artificial sweat printed fingerprints captured by confocal laser scanning microscopes. In: IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, p. 94090A (2015)Google Scholar
  4. 4.
    Yan, Y., Osadciw, L.A.: Bridging biometrics and forensics. In: Electronic Imaging, International Society for Optics and Photonics, p. 68190Q (2008)Google Scholar
  5. 5.
    Jain, A.K., Hong, L., Pankanti, S., Bolle, R.: An identity-authentication system using fingerprints. Proc. IEEE 85(9), 1365–1388 (1997)CrossRefGoogle Scholar
  6. 6.
    Note on CASIA-IrisV1. Biometric Ideal Test. http://biometrics.idealtest.org/dbDetailForUser.do?id=1
  7. 7.
    Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Synthetic fingerprint generation. In: Handbook of fingerprint recognition, pp. 271–302. Springer, London (2009)Google Scholar
  8. 8.
    Vein Dataset. PUT Vein Database Description. http://biometrics.put.poznan.pl/vein-dataset/
  9. 9.
    Hildebrandt, M., Sturm, J., Dittmann, J., Vielhauer, C.: Creation of a Public Corpus of contact-less acquired latent fingerprints without privacy implications. In: Decker, B., Dittmann, J., Kraetzer, C., Vielhauer, C. (eds.) CMS 2013. LNCS, vol. 8099, pp. 204–206. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40779-6_19 CrossRefGoogle Scholar
  10. 10.
    Bartlow, N., Kalka, N., Cukic, B., Ross, A.: Identifying sensors from fingerprint images. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 78–84 (2009)Google Scholar
  11. 11.
    FVC2000. Fingerprint Verification Competition Databases. http://bias.csr.unibo.it/fvc2000/databases.asp
  12. 12.
    CASIA-FACEV5. Biometric Ideal Test. http://www.idealtest.org/dbDetailForUser.do?id=9
  13. 13.
    Fu, D., Shi, Y.Q., Su, W.: A generalized Benford’s law for JPEG coefficients and its applications in image forensics. In: Electronic Imaging 2007, International Society for Optics and Photonics, p. 65051L (2007)Google Scholar
  14. 14.
    Li, X.H., Zhao, Y.Q., Liao, M., Shih, F.Y., Shi, Y.Q.: Detection of tampered region for JPEG images by using mode-based first digit features. EURASIP J. Adv. Sig. Process. 2012(1), 1–10 (2012)CrossRefGoogle Scholar
  15. 15.
    Xu, B., Wang, J., Liu, G., Dai, Y.: Photorealistic computer graphics forensics based on leading digit law. J. Electron. (China) 28(1), 95–100 (2011)CrossRefGoogle Scholar
  16. 16.
    Benford, F.: The law of anomalous numbers. Proc. Am. Philosophical Soc. 78(4), 551–572 (1938)zbMATHGoogle Scholar
  17. 17.
    Pérez-Gonález, F., Heileman, G.L., Abdallah, C.T.: Benford’s law in image processing. In: 2007 IEEE International Conference on Image Processing, ICIP 2007, vol. 1, pp. 1-405 (2007)Google Scholar
  18. 18.
    Hill, T.P.: A statistical derivation of the significant-digit law. Stat. Sci. 10(4), 354–363 (1995)MathSciNetzbMATHGoogle Scholar
  19. 19.
    Durtschi, C., Hillison, W., Pacini, C.: The effective use of Benford’s law to assist in detecting fraud in accounting data. J. Forensic Account. 5(1), 17–34 (2004)Google Scholar
  20. 20.
    Acebo, E., Sbert, M.: Benford’s law for natural and synthetic images. In: Proceedings of the First Eurographics Conference on Computational Aesthetics in Graphics, Visualization and Imaging, Eurographics Association, pp. 169–176 (2005)Google Scholar
  21. 21.
    Jolion, J.M.: Images and Benford’s law. J. Math. Imaging Vis. 14(1), 73–81 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Qadir, G., Zhao, X., Ho, A.T.: Estimating JPEG2000 compression for image forensics using Benford’s law. In: SPIE Photonics Europe, International Society for Optics and Photonics, p. 77230J (2010)Google Scholar
  23. 23.
    Li, B., Shi, Y.Q., Huang, J.: Detecting doubly compressed JPEG images by using mode based first digit features. In: 2008 IEEE 10th Workshop on Multimedia Signal Processing, pp. 730–735 (2008)Google Scholar
  24. 24.
    Golik, P., Doetsch, P., Ney, H.: Cross-entropy vs. squared error training: a theoretical and experimental comparison. In: Interspeech, pp. 1756–1760 (2013)Google Scholar
  25. 25.
    Panchal, G., Ganatra, A., Kosta, Y.P., Panchal, D.: Behaviour analysis of multilayer perceptrons with multiple hidden neurons and hidden layers. Int. J. Comput. Theory Eng. 3(2), 332 (2011)CrossRefGoogle Scholar
  26. 26.
    Othman, A.A.: Mixing Biometric Data For Generating Joint Identities and Preserving Privacy. Ph.D. Thesis, West Virginia University (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Aamo Iorliam
    • 1
    Email author
  • Anthony T. S. Ho
    • 1
    • 3
    • 4
  • Adrian Waller
    • 2
  • Xi Zhao
    • 3
  1. 1.Department of Computer ScienceUniversity of SurreyGuildfordUK
  2. 2.Thales UK Research and TechnologyReadingUK
  3. 3.School of Computer Science and Information EngineeringTianjin University of Science and TechnologyTianjinChina
  4. 4.Wuhan University of TechnologyWuhanChina

Personalised recommendations