In: Proceedings SPIE 9409, Media Watermarking, Security, and Forensics 94090A (4 March 2015).
ISSN/ISBN: Not available at this time. DOI: 10.1117/12.2077531
Abstract: The possibility of forging latent fingerprints at crime scenes is known for a long time. Ever since it has been stated that an expert is capable of recognizing the presence of multiple identical latent prints as an indicator towards forgeries. With the possibility of printing fingerprint patterns to arbitrary surfaces using affordable ink- jet printers equipped with artificial sweat, it is rather simple to create a multitude of fingerprints with slight variations to avoid raising any suspicion. Such artificially printed fingerprints are often hard to detect during the analysis procedure. Moreover, the visibility of particular detection properties might be decreased depending on the utilized enhancement and acquisition technique. In previous work primarily such detection properties are used in combination with non-destructive high resolution sensory and pattern recognition techniques to detect fingerprint forgeries. In this paper we apply Benford's Law in the spatial domain to differentiate between real latent fingerprints and printed fingerprints. This technique has been successfully applied in media forensics to detect image manipulations. We use the differences between Benford's Law and the distribution of the most significant digit of the intensity and topography data from a confocal laser scanning microscope as features for a pattern recognition based detection of printed fingerprints. Our evaluation based on 3000 printed and 3000 latent print samples shows a very good detection performance of up to 98.85% using WEKA's Bagging classifier in a 10-fold stratified cross-validation.
Bibtex:
@inproceedings{
author = { Mario Hildebrandt and Jana Dittmann},
title = {Benford's Law based detection of latent fingerprint forgeries on the example of artificial sweat printed fingerprints captured by confocal laser scanning microscopes},
volume = {9409},
year = {2015},
doi = {10.1117/12.2077531},
URL = {https://doi.org/10.1117/12.2077531},
eprint = {}
}
Reference Type: Conference Paper
Subject Area(s): General Interest, Image Processing