A Spatio-Spectral Hybrid Convolutional Architecture for Hyperspectral Document Authentication

Abstract

Hyperspectral Document Image (HSDI) analysis allows for efficient and accurate differentiation of inks with visually similar color but unique spectral response, which is a crucial step in authentication of documents. Various HSDI based ink discrimination methods are available in the current literature, however, more accurate and robust methods are required to empower document authentication. Contrary to the former ink mismatch detection methods based on spectral features only, we present a novel method based on deep learning that exploits the spectral correlation as well as the spatial context to enhance ink mismatch detection. Spectral responses of the target pixel and its neighboring pixels are organized in an image format and fed to a Convolutional Neural Network (CNN) for classification. The proposed method achieves the highest accuracy among the other ink mismatch detection methods on the UWA Writing Ink Hyperspectral Images database (WIHSI), which demonstrates the effectiveness of deep learning models employing spatio-spectral hybrid features for document authentication. Detailed experimental analysis for selection of appropriate CNN architecture, spatio-spectral data format and training ratio is presented along with a comparison with the previous methods on this subject.