In: User-Centric Privacy and Security in Biometrics, Claus Vielhauer (Ed.).
ISSN/ISBN: 9781785612077 DOI: 10.1049/PBSE004E_ch11
Abstract: It is obvious that tampering of raw biometric samples is becoming an important security and privacy 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 chapter, besides an introduction to the concept and state-ofthe-art reviews, the divergence values of Benford's law are used as input features for a neural network for the classification of biometric images. Experimental analysis shows that the classification of the biometric images can achieve good accuracies between the range of 90.02% and 100%.
Bibtex:
@CHAPTER{,
author = {Aamo Iorliam},
affiliation = {Department of Computer Science, University of Surrey},
author = {Anthony T. Ho},
affiliation = {Department of Computer Science, University of Surrey},
author = {Norman Poh},
affiliation = {Department of Computer Science, University of Surrey},
author = {Xi Zhao},
affiliation = {School of Computer Science and Information Engineering, Tianjin University of Science and Technology},
author = {Zhe Xia},
affiliation = {School of Computer Science and Technology, Wuhan University of Technology},
title = {Benford's law for classification of biometric images},
booktitle = User-Centric Privacy and Security in Biometrics,
publisher = {Institution of Engineering and Technology},
year = {2017},
pages = {237--256},
series = {Security},
doi = {10.1049/PBSE004E_ch11},
url = {https://digital-library.theiet.org/content/books/10.1049/pbse004e_ch11},
}
Reference Type: Book Chapter
Subject Area(s): Computer Science, Image Processing