Localized Forgery Detection In Hyperspectral Document Images

Zhipei Luo, Faisal Shafait, Ajmal Saeed Mian

Abstract:

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