Abstract: Currency forgery is a significant crime that has a negative impact on a country's finances. In banking systems, the proposed technique will be useful in detecting counterfeit money. Due to a rise in the number of counterfeit notes on the market, India is experiencing more serious issues. Various false note detecting solutions are available globally to combat this problem, however the most of them are hardware-based and expensive.
This focuses on obtaining public access in order to detect counterfeit currencies. The suggested method can determine a banknote's legality by looking for certain security features including watermarks, latent pictures, security threads, and so on. Machine learning algorithms can be used to identify counterfeit banknotes. These security aspects are extracted and encoded as part of the approach. A support vector machine is used to extract security features from the input image, as well as to identify and classify them.
Keywords: Counterfeit currency, Convolutional Neural Network (CNN), Support Vector Machine (SVM), Android Application, Region of Interest (ROI), Edge Detection, Artificial Intelligence (AI), Image Processing, Machine Learning (ML), Deep Learning(DL).
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DOI:
10.17148/IJARCCE.2022.116116
