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Iorliam, A, Ho, ATS, Waller, A and 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, 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 Benfordís 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 Benfordís 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