Development of counterfeit banknote recognition system using deep learning

Banknote counterfeiting undermines financial stability and public trust on the currency of a nation. Moreover, it also has various economic implications on financial institutions and businesses. The counterfeiters employ increasingly sophisticated techniques to mimic the genuine banknotes with the advancement of technology, which necessitates the development of fast and robust counterfeit banknote detection systems in order to ensure the integrity of financial systems as well as maintaining the public trust on currency transactions. In this thesis, a Turkish lira authentication system has been developed which locates the banknotes on images and classify the located banknotes as genuine or counterfeit as well as identifying their denominations. The localization part is developed using the classical and well-known digital image processing techniques, where a novel convolutional neural network architecture is developed for the classification part as the deep learning, which is a subset of artificial intelligence, has shown remarkable success in automated feature extraction and pattern recognition tasks. The conducted experiments shows that the proposed architecture outperforms some state-of-the-art deep learning algorithms in terms of accuracy, loss, inference speed, and file size.