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%.
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References
Harper, W.W.: Fingerprint forgery transferred latent fingerprints. J. Crim. Law Criminol. 28(4), 573–580 (1937)
Iorliam, A., Ho, A.T.S., Poh, N.: Using Benford’s Law to detect JPEG biometric data tampering. Biometrics 2014, London (2014)
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)
Yan, Y., Osadciw, L.A.: Bridging biometrics and forensics. In: Electronic Imaging, International Society for Optics and Photonics, p. 68190Q (2008)
Jain, A.K., Hong, L., Pankanti, S., Bolle, R.: An identity-authentication system using fingerprints. Proc. IEEE 85(9), 1365–1388 (1997)
Note on CASIA-IrisV1. Biometric Ideal Test. http://biometrics.idealtest.org/dbDetailForUser.do?id=1
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Synthetic fingerprint generation. In: Handbook of fingerprint recognition, pp. 271–302. Springer, London (2009)
Vein Dataset. PUT Vein Database Description. http://biometrics.put.poznan.pl/vein-dataset/
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
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)
FVC2000. Fingerprint Verification Competition Databases. http://bias.csr.unibo.it/fvc2000/databases.asp
CASIA-FACEV5. Biometric Ideal Test. http://www.idealtest.org/dbDetailForUser.do?id=9
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)
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)
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)
Benford, F.: The law of anomalous numbers. Proc. Am. Philosophical Soc. 78(4), 551–572 (1938)
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)
Hill, T.P.: A statistical derivation of the significant-digit law. Stat. Sci. 10(4), 354–363 (1995)
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)
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)
Jolion, J.M.: Images and Benford’s law. J. Math. Imaging Vis. 14(1), 73–81 (2001)
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)
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)
Golik, P., Doetsch, P., Ney, H.: Cross-entropy vs. squared error training: a theoretical and experimental comparison. In: Interspeech, pp. 1756–1760 (2013)
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)
Othman, A.A.: Mixing Biometric Data For Generating Joint Identities and Preserving Privacy. Ph.D. Thesis, West Virginia University (2013)
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Iorliam, A., Ho, A.T.S., Waller, A., Zhao, X. (2017). Using Benford’s Law Divergence and Neural Networks for Classification and Source Identification of Biometric Images. In: Shi, Y., Kim, H., Perez-Gonzalez, F., Liu, F. (eds) Digital Forensics and Watermarking. IWDW 2016. Lecture Notes in Computer Science(), vol 10082. Springer, Cham. https://doi.org/10.1007/978-3-319-53465-7_7
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