Fake Currency Detection Using Convolutional Neural Network
Dr. V. Kavitha · International Journal for Research in Applied Science and Engineering Technology · 2025
This literature survey examines existing research on counterfeit currency detection systems, focusing on the use of Convolutional Neural Networks (CNNs) for visual data analysis. Many studies highlight the effectiveness of CNNs in recognizing patterns and anomalies in currency images. However, a significant limitation in current systems is the use of small and limited datasets that predominantly feature older Indian banknotes. This lack of diversity in datasets, including variations in currency types, denominations, and environmental factors, restricts the generalization capabilities of detection models. Moreover, much of the existing work emphasizes detecting counterfeit versions of outdated banknotes, leaving a gap in the detection of newer notes with updated security features. Through this survey, we aim to identify the challenges faced by current detection systems and explore strategies to enhance dataset diversity for improving model accuracy and adaptability to evolving counterfeit scenarios.