Fake Currency Detection Using Convolutional Neural Networks

Year : 2024 | Volume :14 | Issue : 01 | Page : 1-9
By

D.Jahnavi

Dr.P.M.K.Prasad

Dr.Guntu Nooka Raju

Dr. BPV Dileep

K.Lohitha

  1. Associate Professor, Department of Electronics and Communication Engineering, GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  2. Associate Professor, Department of Electronics and Communication Engineering, GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  3. Assistant Professor, Department of Electronics and Communication Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh, India
  4. Associate Professor, Department of Electronics and Communication Engineering, GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  5. Associate Professor, Department of Electronics and Communication Engineering, GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India

Abstract

In today’s world, due to increasing technology like scanning, color printing, and duplicating, the identification of bogus notes by the human eye is almost becoming impossible. Knowingly or unknowingly, due to the usage of bogus currency notes, the Indian economy is also being impacted badly. Hence, the identification of bogus currency notes is important. This paper deals with identifying whether the given sample of the currency note is real or bogus. Previously, there were many methods for the identification of bogus notes eliminating the need for manual feature extraction. Also, the results are not very accurate and hence deep neural networks can be used for the recognition of bogus currency notes. In this paper, the Convolution Neural Network technique is employed for the detection of bogus currency bills. Convolution Neural Network is an architecture that directly learns from the input data and classifies the note, if the note is found to be a bogus note, then immediately a message will be sent indicating the specified location where the bogus notes are being circulated. Hence, the problem of bogus note circulation can be reduced and help society not being deceived by fraudsters. The results show that the training accuracy and validation accuracy of the proposed method are 99.6% and 99.7% respectively

Keywords: Convolutional neural networks, Deep Learning, VGG16, Fake currency, accuracy

[This article belongs to Current Trends in Signal Processing(ctsp)]

How to cite this article: D.Jahnavi, Dr.P.M.K.Prasad, Dr.Guntu Nooka Raju, Dr. BPV Dileep, K.Lohitha. Fake Currency Detection Using Convolutional Neural Networks. Current Trends in Signal Processing. 2024; 14(01):1-9.
How to cite this URL: D.Jahnavi, Dr.P.M.K.Prasad, Dr.Guntu Nooka Raju, Dr. BPV Dileep, K.Lohitha. Fake Currency Detection Using Convolutional Neural Networks. Current Trends in Signal Processing. 2024; 14(01):1-9. Available from: https://journals.stmjournals.com/ctsp/article=2024/view=150757

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Regular Issue Subscription Original Research
Volume 14
Issue 01
Received May 1, 2024
Accepted May 28, 2024
Published June 14, 2024
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