Ensuring Effective Detection of Currency Counterfeits Across Different Touchpoints

The circulation of counterfeit currency presents a significant threat to the stability of financial systems, public trust, and overall economic integrity. Addressing this challenge necessitates advanced solutions capable of accurate and reliable detection. This study introduces a counterfeit detection framework that employs Convolutional Neural Networks (CNNs) for the high-precision identification of forged banknotes. The performance of the CNN model is rigorously compared against traditional machine learning algorithms, including Support Vector Classifier (SVC) and Random Forest Classifier, to ensure robust validation. Furthermore, a mobile-based, real-time detection system is developed, leveraging machine learning techniques to enable users at various points of interaction to verify currency effortlessly. By integrating cutting-edge algorithms and emphasizing practical usability, this research contributes to the development of secure and accessible currency authentication solutions.