Identification of Counterfeit Indian Currency Using CNN
Dasa Hemanth Kumar1, Lopelly Nikhila2, Kotha Shiva Sai3, Bandi Komal4
hemanthkumarece@nmrec.edu.in
lopellynikhila2002@gmail.com,kothashivasai89@gmail.com,komalbandi2001@gmail.com
1Assistant Professor, Department of Electronics and Communication Engineering
2-4Student, Department of Electronics and Communication Engineering
Nalla Malla Reddy Engineering College , Hyderabad , Telangana , India
ABSTRACT: The advancement of colour printing technology has increased the production of counterfeit money notes on a vast scale. Printing used to be done in a printing company, but now everyone has a printer at home and can print the false notes with optimum accuracy. As a result, instead of real currency, we have false currency in our market. Daily reports of fake cash fraud highlight the issue of false currency. The advancement of technology, such as computers, scanners, and copies, has made it easier to produce fake currency, and there is no programme to check whether the currency is phoney or real. India is plagued by numerous issues, including corruption, black money, and counterfeit currency.
Because of this issue, phoney currency is created in less time and more efficiently. To address this issue, we created a technology that can distinguish between real and phoney cash. The numerous characteristics of Indian currency are described in this system. There are equipment in banks and other markets that may verify financial legitimacy. However, the average person does not verify each note to see if it is fake or real. The suggested system employs a CNN model to detect whether the cash is genuine or counterfeit. The programme is entirely written in the Python programming language. Steps such as grey scale conversion, feature extraction by hog edge recognition, splitting, and so on are performed using appropriate software.
By using Xception Architecture from CNN model we can achieve higher accuracy than any other model. The proposed system has advantages such as simplicity and high performance. The result will predict whether the Indian currency note is Real or Fake.
Keywords: Indian currency, Convolutional Neural Network , Image Processing , Xception Architecture , Deep Learning
