Fake Currency Detection using Deep Learning and Image Processing

Fake Currency Detection using Deep Learning and Image Processing

Dr. Madhuri Borawake, Umesh ManePatil,

Prasad Suryawanshi, Cinthiya Salve,

 Abhishek Karale

Computer Engineering Department,

PDEA’S College Of Engineering Manjari(BK), India

ABSTRACT – This project focuses on the detection of fake currency using Machine Learning. The aim is to develop a mobile application that automates this process. The methodology involves image acquisition, image segmentation, feature extraction, and comparison. A magnified image of the genuine currency is used to create a dataset for machine learning. The features of the currency note being tested are compared with the dataset to determine its authenticity. The key challenge is to systematically repeat the analysis process to minimize errors and reduce time.

The Automatic Fake Currency Recognition System is designed to identify counterfeit paper money and distinguish it from genuine currency. The issue of fake currency has had a significant impact on the financial system and various sectors, particularly after demonetization. This approach explores a new method using Convolutional Neural Networks (CNNs) to identify fake money through image analysis, which is found to be superior to previous image processing techniques. Deep Learning, which has shown remarkable results in image classification tasks, forms the basis of this technique. It enables real-time identification of counterfeit currency through image recognition, benefiting both individuals and machines. The proposed system's accuracy is evaluated using precision.

Keywords: Convolutional Neural Networks, Counterfeit, Demonetization, Image Processing, Deep Learning, Vgg16, Automatic detection.