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, pp. 88-105.
ISSN/ISBN: Not available at this time. DOI: 10.1007/978-3-319-53465-7_7
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%.
Bibtex:
@InProceedings{,
author="Iorliam, Aamo and Ho, Anthony Tung Shuen and Waller, Adrian and Zhao, Xi",
editor="Shi, Yun Qing
and Kim, Hyoung Joong
and Perez-Gonzalez, Fernando
and Liu, Feng",
title="Using Benford's Law Divergence and Neural Networks for Classification and Source Identification of Biometric Images",
booktitle="Digital Forensics and Watermarking",
year="2017",
publisher="Springer International Publishing",
address="Cham",
pages="88--105",
isbn="978-3-319-53465-7"
}
Reference Type: Conference Paper
Subject Area(s): Biology, Medical Sciences