Banknote counterfeiting is a common practice worldwide. Due to the recent developments in technology, banknote imitation has become easier than before. There are different kinds of algorithms developed for the detection of counterfeit banknotes for different countries in the literature. The earlier algorithms utilized classical image processing techniques where the implementations of machine learning and deep learning algorithms appeared with the developments in the artificial intelligence field as well as the computer hardware. In this study, a novel convolutional neural networks-based deep learning algorithm has been developed that detects counterfeit Turkish Lira banknotes and their denominations using the banknote images taken under UV light. The results obtained with the proposed algorithm have been compared with the results obtained with state-of-the-art machine learning and deep learning algorithms. It is seen that the results obtained with the proposed algorithm are superior to the results obtained with the state-of-the-art machine learning and deep learning algorithms. The proposed algorithm achieved 100% accuracy on the training set and 99.95% accuracy on the test set while yielding low inference time with relatively few parameters and small file size.
Convolutional neural networks, counterfeit banknote detection, deep learning, machine learning
İYİKESİCİ, BURAK and ERÇELEBİ, ERGUN
"An efficient deep learning architecture for Turkish Lira recognition and counterfeit detection,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 31:
3, Article 13.
Available at: https://journals.tubitak.gov.tr/elektrik/vol31/iss3/13