Evaluation of Deep Learning models on UV ink : a Fake Money detection scheme with RPN
Anjir Ahmed Chowdhury, Argho Das, Debajyoti Karmaker, Khadija Kubra Shahjalal Hoque · 2022
As soon as coins or money were invented, people were trying to make counterfeits. Counterfeit money is fake money that is produced without the permission of the state or government, usually to imitate the currency and deceive the intended recipient. In Bangladesh, this is a significant problem that is becoming an increased phenomenon as the days pass by. Today’s modern banknotes have several security features that make it easier to identify fake notes. One of the security features is the use of UV ink. Banknotes deliberately put random flecks of color scattered all over the surface of the money - which acts as an extra layer of protection against counterfeiters. We proposed an automatic authentication model for identifying counterfeit money based on these random flecks of color which are visible under UV light. To obtain a benchmark result, existing object detection pre-trained models were used, followed by MobileNet, Inception, ResNet50, ResNet101, and Inception-ResNet architectures. After that, using the Region Proposal Network (RPN) method with Convolutional Neural Network (CNN) based classification the optimal model was proposed. The proposed model had a 96.3% accuracy. It is critical to reduce the circulation of counterfeit money in a country’s economy to stop inflation. This study will aid in the detection of counterfeit money and, hopefully, reduce its spread.