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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10082)


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%.


Benford’s law Neural network Biometric images 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  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

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