TY - GEN
T1 - Comparative Analysis of Convolutional Neural Network-Based Counterfeit Detection
T2 - International IoT, Electronics and Mechatronics Conference, IEMTRONICS 2024
AU - Balogun, Emmanuel
AU - Wimmer, Hayden
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - 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 two datasets, 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 model behavior toward 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.
AB - 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 two datasets, 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 model behavior toward 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.
KW - Activation function
KW - Convolutional Neural Network
KW - Counterfeit detection
KW - Image classification
KW - Keras library
KW - MaxPooling
KW - PyTorch library
UR - http://www.scopus.com/inward/record.url?scp=85218462776&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-4784-9_2
DO - 10.1007/978-981-97-4784-9_2
M3 - Conference article
AN - SCOPUS:85218462776
SN - 9789819747832
T3 - Lecture Notes in Electrical Engineering
SP - 11
EP - 27
BT - Proceedings of IEMTRONICS 2024 - International IoT, Electronics and Mechatronics Conference
A2 - Bradford, Phillip G.
A2 - Gadsden, S. Andrew
A2 - Koul, Shiban K.
A2 - Ghatak, Kamakhya Prasad
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 3 April 2024 through 5 April 2024
ER -