FAKE CURRENCY DETECTION USING MACHINE LEARNING
AND
IMAGE PROCESSING
A Project Report submitted to
JNTUA, Ananthapuramu
In partial fulfillment of the requirements for the award of the degree of
Bachelor of Technology
(ARTIFICIAL INTELIGENCE & DATA SCIENCE)
By
N.Sireesha (21KB1A3056)
N.Muni Prakash (21KB1A3055) SK.Imran (21KB1A3082)
T.Chandana (21KB1A3090) S.Pavithra (21KB1A3086)
Under the esteemed Guidance of
Sri. P.Chandrakala (B.Tech.,M.Tech.,[Ph.D])
Designation,
Department of IT and AI&DS
Department of IT and AI&DS
N.B.K.R INSTITUTE OF SCIENCE & TECHNOLOGY
VIDYANAGAR – 524 413, TIRUPATHI DIST, AP
(Autonomous)
APRIL 2025
Website: www.nbkrist.org. Ph: 08624-228 247
Email :ist@nbkrist.org. Fax :08624-228 257
N.B.K.R. INSTITUTE OF SCIENCE & TECHNOLOGY
(Autonomous)
(Approved by AICTE: Accredited by NBA: Affiliated to JNTUA, Ananthapuramu)
An ISO 9001-2000 Certified Institution
Vidyanagar -524 413, Tirupathi District, Andhra Pradesh, India
BONAFIDE CERTIFICATE
This is to certify that the project work entitled “ FAKE CURRENCY DETECTION USING MACHINE
LEARNING AND IMAGE PROCESSING ” is a bonafide work done by N.Sireesha (21KB1A3056),
N.MuniPrakash(21KB1A3055), SK.Imran(21KB1A3082), T.Chandana(21KB1A3090), S.Pavithra
(21KB1A3086).in the Department of Information Technology and Artificial Intelligence & Data Science,
N.B.K.R.Institute of Science & Technology, Vidyanagar and is submitted to JNTUA, Ananthapuramu in
the partial fulfillment for the award of B.Tech degree in Artificial Intelligence & Data Science . This work has
been carried out under my supervision.
P.Chandrakala Dr. A.Narayana Rao
Assistant Professor Professor & Head
Department of IT and AI&DS Department of IT and AI&DS
NBKRIST, Vidyanagar NBKRIST, Vidyanagar
Submitted for the Viva-Voce Examination held on ____________
Examiner-1 Examiner-2
ACKNOWLEDGEMENT
The satisfaction that accompanies the successful completion of a project would be
incomplete without the people who made it possible of their constant guidance and
encouragement crowned our efforts with success.
We would like to express our profound sense of gratitude to our project guide
Sri.P.Chandrakala, Assistant Professor, Department of IT and AI&DS, N.B.K.R.I.S.T
(affiliated to JNTUA, Ananthapuramu), vidyanagar, for his/her masterful guidance and
the constant encouragement throughout the project. Our sincere appreciations for his/her
suggestions and unmatched services without, which this work would have been an
unfulfilled dream.
We convey our special thanks to Dr. Y.VENKATA RAMI REDDY respectable
chairman of N.B.K.R. Institute of Science and Technology, for providing excellent
infrastructure in our campus for the completion of the project.
We convey our special thanks to Sri N.RAM KUMAR REDDY respectable
correspondent of N.B.K.R. Institute of Science and Technology, for providing excellent
infrastructure in our campus for the completion of the project.
We are grateful to Dr. V. Vijaya Kumar Reddy, Director, N.B.K.R Institute of
Science and Technology for allowing us to avail all the facilities in the college.
We express our sincere gratitude to Dr. A. Narayana Rao, Professor, Head of Department,
Department of IT and AI&DS for providing exceptional facilities for successful completion
of our project work.
We would like to convey our heart full thanks to Staff members, Lab technicians,
and our friends, who extended their cooperation in making this project as a successful one.
We would like to thank one and all who have helped us directly and indirectly to
complete this project successfully.
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ABSTRACT
The proliferation of counterfeit currency poses a significant threat to economic stability,
necessitating advanced methods for effective detection. This project, titled "Fake Currency
Detection Using Machine Learning And Image Processing," proposes a novel approach to
counterfeit detection by leveraging deep learning techniques. The study explores three primary
models: MobileNet, ResNet, and a hybrid model combining MobileNet with Support Vector
Machines (SVM), along with a variant that integrates MobileNet with both SVM and Random
Forest. MobileNet and ResNet, known for their efficiency and accuracy in image classification
tasks, are evaluated for their performance in distinguishing genuine Indian currency from
counterfeit notes. The hybrid model aims to enhance detection capabilities by combining the
strengths of MobileNet with SVM, offering a robust solution for handling complex counterfeit
patterns. Additionally, the integration of SVM and Random Forest with MobileNet seeks to further
improve classification performance by leveraging ensemble learning techniques. The effectiveness
of these models is assessed based on their accuracy, precision, recall, and overall robustness in
real-world scenarios. The results highlight the potential of convolutional neural networks in
enhancing counterfeit currency detection systems, providing valuable insights for improving
financial security measures. Keywords: Counterfeit Detection, Convolutional Neural Networks,
MobileNet, ResNet, Support Vector Machines (SVM), Random Forest, Hybrid Model, Indian
Currency, Image Classification, Machine Learning.
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TABLE OF CONTENTS
CHAPTER NO CHAPTER NAME PAGE NO
ACKNOWLEDGEMENT
ABSTRACT
CHAPTER 1: INTRODUCTION
1.1 Introduction
1.2. Motivation
1.3 Aim and Objective
1.4 Scheme of chapterization
CHAPTER 2: SURVEY OF THE LITERATURE
2.1 Introduction
2.2 Literature Survey
2.3 Existing System
2.4 Proposed System
2.5 Feasibility analysis
CHAPTER 3: DESIGN ISSUES
3.1. System Design
3.1.1. Description about Modules
3.2. Detailed Design of the Project
CHPTER 4 IMPLEMENTATION ISSUES
4.1 Introduction
4.2 Requirements
4.2.1 H/W requirements
4.2.2 S/W requirements
4.3 Test plan
4.3.1 Test Procedure
4.3.2 Test Cases
4.4 Input / Output Windows
CHAPTER 5: CONCLUSION AND FUTURE ENHANCEMENTS
5.1 Conclusion
5.2 Future enhancements
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BIBLIOGRAPHY
-Published papers
-References
-Text Books
-Websites
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