Localized forgery detection in hyperspectral document images

Hyperspectral imaging is emerging as a promising technology to discover patterns that are otherwise hard to identify with regular cameras. Recent research has shown the potential of hyperspectral image analysis to automatically distinguish visually similar inks. However, a major limitation of prior work is that automatic distinction only works when the number of inks to be distinguished is known a priori and their relative proportions in the inspected image are roughly equal. This research work aims at addressing these two problems. We show how anomaly detection combined with unsupervised clustering can be used to handle cases where the proportions of pixels belonging to the two inks are highly unbalanced. We have performed experiments on the publicly available UWA Hyperspectral Documents dataset. Our results show that INFLO anomaly detection algorithm is able to best distinguish inks for highly unbalanced ink proportions.