A Systematic Literature Review of Counterfeit Currency Detection using Modern Methods

The escalating issue of counterfeit currency, fuelled by sophisticated printing and scanning technologies, poses a substantial threat to global economies, undermining financial systems and eroding public trust. Effective counterfeit detection has become imperative for mitigating this growing problem and safeguarding monetary integrity. This paper provides a comprehensive review of current and emerging methodologies in counterfeit currency detection, with a primary focus on image processing techniques. Key approaches, including Ultra Violet (UV) detection, polarization analysis, advanced edge detection, and machine learning algorithms, are explored in depth. Specific image processing techniques, such as the canny edge detection algorithm, image segmentation, and feature extraction, are analyzed for their effectiveness in identifying subtle differences between genuine and forged currency features. The paper also emphasizes the integration of these techniques into automated systems, which enhance accuracy, speed, and scalability in real-world applications. Leveraging both traditional and advanced technologies, this study proposes a robust framework for cost-effective, reliable, and scalable solutions in counterfeit detection, aiming to strengthen defences against increasingly complex forgeries.