Citation

Jan, Salman and Gul, Fara and Belgaum, Mohammad Riyaz and Kamal, Shahid and Ahmad, Afaq and Bilal, Muhammad and Khan, Atif (2026) Counterfeit Currency Detection Through Deep Convolutional Generative Adversarial Network. International Journal of Advanced Computer Science and Applications, 17 (7). ISSN 2158107X

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Abstract

Worldwide, individuals, businesses, and economies are being affected by counterfeit currency significantly. Numerous models generate content that mimics the original, and existing solutions fall short in detecting the difference between real and fake. This study presents a detection system using three deep learning models: Deep Convolutional Generative Adversarial Network (DCGAN), Convolutional Neural Network (CNN), and Fully Connected Neural Network (FCNN). The proposed study also identifies patterns that contribute to real and fake currency when the models are trained on the data. After training the models, the proposed solution receives an accuracy of 95 per cent, an F1 Score of 0.957, 0.95 as precision, and 0.954 as recall. This study further carries out a comprehensive analysis of existing models and compares them with the proposed solution to determine the effectiveness of the solution and further recommend its implementation in real-world applications. The proposed solution contributes to the widespread adoption of the application across smart devices and further ensures a robust solution for the detection of counterfeit money.

Item Type:	Article
Uncontrolled Keywords:	Counterfeit, DCGAN, Fake Money
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	01 Oct 2026 07:26
Last Modified:	01 Oct 2026 07:26
URII:	http://shdl.mmu.edu.my/id/eprint/16793

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