Forgery Fighters: The CNN-SVM Edge in Currency Security

In this research, a combination of a hybrid Convolutional Neural Network (CNN) and Support Vector Machine (SVM) model is developed for enhanced issuance and detection of counterfeit cash notes. The model uses the trendy CNNs' feature extraction capabilities and SVMs' specific classification ability to relate to 5 different classes of counterfeit strategies and, in that way, completes the drawbacks of traditional ways of detection. We (myself and my group) put money pictures into a large pool and conducted a strict check on random classes. There was a very high level of precision, accuracy, recall and F1-score classification for all the classes. Our model produced pretty good results in its entirety. The accuracies ranged from 94% to 97%. The accuracy and sensitivity levels were between 85.68% to 96.33% and 87.83% to 97.72%, finally calculating the F1-scores at 88.42% to 95.47%. The figures illustrate that the model can not be seen solely as a potential solution; it also functions effectively and ensures counterfeit coins' recognition. The model has predictive ability, which can be appreciated by the precise confusion matrix analysis showing how it can recognize more difficult counterfeit methods with no or just very few misclassifications. With this research outcome, ascertaining the hybridized CNN-SVM strategy being considerably more effective than the traditional tests suggests a versatile and functional approach that unquestionably can be applied to different real-world utilizations. Concerning the development of future international secure financial systems, other investigations are expected to be carried out to improve the model and to research its application to more various forms of currencies. This research lays out how a next-generation learning algorithm that can fight fraud or other financial crimes may be used to achieve significant progress in the security and robustness of economic systems worldwide.