Development of a smartphone authentication system using individual characteristics of anti-counterfeit paper

Abstract

Anti-counterfeiting measures that use individual product differences to discriminate are effective against mass counterfeiting. However, there are problems in that unique ID assignment such as code information has low duplication resistance, and that special discrimination equipment is required to use existing artifact metrics. In this study, we focused on colored fibers used in anti-counterfeiting paper. By combining individual judgment based on fiber distribution and authenticity judgment of the paper itself by machine learning, it is possible to judge authenticity with a single smartphone. We aimed to achieve both simple judgment and duplication resistance.