Enhanced Counterfeit Currency Detection Through Multi-Fusion Techniques

The “Enhanced Counterfeit Currency Detection through Multi-Fusion Techniques” title focuses on addressing the growing threat of counterfeit currency by employing advanced detection methods. This research combines image processing, deep learning, and pattern recognition to improve the accuracy and robustness of counterfeit currency detection systems. By using image processing, the project extracts crucial features from currency notes, while deep learning models, particularly Convolutional Neural Networks (CNNs), are utilized to adapt to new and evolving counterfeit strategies. Additionally, pattern recognition techniques are applied to spot inconsistencies in the currency that signal counterfeiting. This integrated approach aims to support financial institutions, businesses, and individuals by reinforcing the reliability of transactions, minimizing financial losses, and enhancing confidence in the use of currency in daily operations.