Mobile hologram verification with deep learning

Abstract

Holograms are security features applied to security documents like banknotes, passports, and ID cards in order to protect them from counterfeiting. Checking the authenticity of holograms is an important but difficult task, as holograms comprise different appearances for varying observation and/or illumination directions. Multi-view and photometric image acquisition and analysis procedures have been proposed to capture that variable appearance. We have developed a portable ring-light illumination module used to acquire photometric image stacks of holograms with mobile devices. By the application of Convolutional Neural Networks (CNN), we developed a vector representation that captures the essential appearance properties of hologram types in only a few values extracted from the photometric hologram stack. We present results based on Euro banknote holograms of genuine and counterfeited Euro banknotes. When compared to a model-based hologram descriptor, we show that our new learned CNN representation enables hologram authentication on the basis of our mobile acquisition method more reliably.