• Dr.K.N.S. Lakshmi
• M. George E.W.
• M. Yamini
• S. Keerthana
• A. Venkata Prasad

Currency counterfeiting causes major economic losses. This paper develops machine learning models to detect fake banknotes. Experiments were conducted using a dataset of genuine and counterfeit Indian currency images. Models were implemented with random forest, logistic regression, and k-nearest neighbors algorithms. The random forest model achieved the highest accuracy of 99.9% in classifying real versus fake notes, demonstrating these techniques can effectively identify counterfeits. Automated detection through machine learning provides advanced tools to combat counterfeiting and prevent financial fraud. Further research can refine these models for real-world usage in law enforcement applications.

Machine Learning, Random Forests, Logistic Regression, K-Nearest neighbors, Counterfeit Detection, Fake Currency.

"Fake Currency Detection using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.d238-d246, February-2024, Available :http://www.jetir.org/papers/JETIR2402329.pdf

"Fake Currency Detection using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppd238-d246, February-2024, Available at : http://www.jetir.org/papers/JETIR2402329.pdf