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The proliferation of advanced printing and scanning technologies has worsened the challenge of counterfeit currency, posing a significant threat to national economies. Effective detection of counterfeit banknotes is crucial for maintaining the monetary system's integrity. This study aims to evaluate the effectiveness of two prominent Python libraries, Keras and PyTorch, in counterfeit detection using Convolutional Neural Network (CNN) image classification. We repeat our experiments over 2 data sets, one dataset depicting the 1000 denomination of the Colombian peso under UV light and the second dataset of Bangladeshi Taka notes. The comparative analysis focuses on the libraries' performance in terms of accuracy, training time, computational efficiency, and the model behavior towards datasets. The findings reveal distinct differences between Keras and PyTorch in handling CNN-based image classification, with notable implications for accuracy and training efficiency. The study underscores the importance of choosing an appropriate Python library for counterfeit detection applications, contributing to the broader field of financial security and fraud prevention.