Philippine Banknote Counterfeit Detection through Domain Adaptive Deep Learning Model of Convolutional Neural Network

Abstract

Money counterfeiting is the illegal duplication of any currency for the use of deceiving any entity in exchange for a real-world value. Due to the advancements in computer vision in digital computing and the ill-effects of money counterfeiting, it had become one of the most prevalent issues in the fiscal system of any country that needs to be progressively solved. This paper investigated the use of ResNet18 through transfer learning for the task of Philippine banknote counterfeit detection. The used dataset of this study consisted of 391 counterfeited and 391 authentic images of 500 and 1000 Philippine peso bills. The trained model achieved a testing accuracy of 99.59%. Despite achieving a lower training accuracy, the trained model of this study achieved a validation accuracy, specificity, precision, sensitivity, and F1-score of 100% on live testing with the developed web-based money counterfeit detection system.