Identification of Fake Indian Currency Using Deep Learning

The accumulation of fake currency poses significant difficulties to financial systems and providence worldwide. The one significant asset of our country is bank currency, and in order to produce diversity of money, criminals introduce phony notes that imitate the original note in the financial market. Fake Indian currency notes have become a major source of concern in India due to their impact on the country's economy and security. During the demonetization period, it was discovered that a large amount of fake currency was in high demand. In general, it is extremely difficult for a human to identify a forged note from a genuine note without the use of many characteristics and parameters designed for identification, as numerous features of a forged note are identical to those of the original. It is difficult to tell the difference between counterfeit bank notes and genuine ones. As a result, an automated system that can be accessed by banks, ATMs as well as common people is required. To construct such an automated system, an effective algorithm must be developed that can predict whether or not the given note is real. Forged bank currency, or false notes, are meticulously designed. In this study, we offer a new approach for detecting counterfeit Indian currency using deep learning algorithms. The design intends to create an automated system that can differentiate between real and counterfeit Indian currency notes. To accomplish this, we use deep learning, a type of machine learning known for its ability to understand complex patterns and representations from massive datasets. Datasets are acquired by processing legitimate currency images and detecting security parameters and characteristics. We employ convolutional neural networks (CNNs), a common deep learning architecture, for feature extraction and image classification from currency images. The characteristics are then compared with the presented currency note to determine whether it is legitimate or not.