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This article suggests employing deep learning methodologies to automatically identify counterfeit banknotes. Convolutional neural networks (CNNs) are employed to extract distinctive characteristics of Indian currency notes. These attributes are subsequently inputted into another CNN to determine if the money is authentic or counterfeit. Various techniques have been utilized to identify counterfeit objects; however, they often depend on machinery and equipment, which can be less effective and time-consuming. This research presents a hybrid strategy utilizing the Convolutional Neural Network (CNN) and Vgg16 model to accurately detect counterfeit cash. This system uses convolutional neural networks (CNN) and Vgg16 to identify counterfeit cash by analyzing its width, colors, and serial numbers. The proposed methodology is evaluated using a dataset comprising authentic and forged cash notes. This technology surpasses conventional detection methods in terms of accuracy and precision. The result will determine whether the Indian rupee note is genuine or counterfeit. The suggested model effectively identifies counterfeit Indian rupee notes by utilizing Convolutional Neural Network and Vgg16 algorithms, resulting in accuracies of 98.3% and 98.8% respectively. The integration of our proposed technology into current systems will bolster the security of banknotes and effectively safeguard against counterfeiting. |
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