A New Fingerprinting Technique for Engraved Binary Matrix Authentication

This paper introduces a new method for authenticating engravings, crucial to fight counterfeiting and related issues in industries like luxury brands. The approach is based on extracting from the engravings natural minutiae, inspired by human biometrics, called (n, n)-modules. These modules, representing small sub-matrices of the printed pattern, that vary in appearance due to factors like material characteristics and engraving procedure. To create a reliable fingerprint, we proposed a voting system, with each vote being a comparison between two (n, n)-modules. This method leverages of the fact that the defects of an engraving or printing are difficult to reproduce and are observable on the scale of a printed or engraved point. We evaluate two ways of comparing the minutiae: a standard one using Euclidean distance and a deep learning-based one employing a convolutional neural network. The process delivers high accuracy and recall in authenticating engravings and shows robustness under various lighting conditions and levels of image blur.