Enhancing Fraud Detection with Deep Learning: A Robust Automated Counterfeit System

Counterfeit currency is a serious hazard to individuals, financial institutions, and enterprises in the current global economy. Maintaining the integrity of financial transactions and protecting against potential losses require the correct identification of counterfeit banknotes. Using machine learning, and pattern recognition approaches, significant progress has been made in developing automated counterfeit detection systems. In order to protect transaction security and the public’s faith in their currency, these technologies are designed to precisely recognise and differentiate real banknotes from counterfeit ones. The application examines banknote security features like microprinting, security thread, holograms, and watermarks using sophisticated image recognition and machine learning techniques. Additionally, it uses a variety of verification techniques, such as infrared (IR) scanning, magnetic ink detection, and ultraviolet (UV) light detection, to confirm the legitimacy of currency. By using to Brute Force Matching and K-Nearest Neighbour algorithms photos of currency are classified as real or fake