Fake Indian Currency Detection Using Camera Matlab Code

There have been reports that some small scale traders are refusing to accept the new banknotes, due to fear of receiving counterfeit money.

Produces 8-character code and that client enters as secret key. Fake Currency Note Detection Fake or fake notes are significant issue happening in trade exchanges out a nation like India. As indicated by the overview directed by RBI in 2013 demonstrates that there is trillions of cash in fake notes in entire India. Notes 1.In this project setup, note is placed in front of camera to check whether it is fake or genuine. Camera takes the pictures of notes and it is analyzed by MATLAB program installed on computer and checks Indian currency notes. The project is meant to check Indian currency notes of 10, 20, 50, 100, 500 and 1000 rupees.

Most of them, just like many Kenyans have expressed doubts and confusion on how to differentiate the genuine notes from the fake ones even as the circulation of the fake currencies continue to flood the market.

Automatic method for detection of fake currency note is very important in every country. In this project we have made fake currency note CAMERA detection technique using MATLAB and other applications of image processing. In the project setup, note is placed in front of camera to check whether it is fake. Credit Card Fraud Detection: Top ML Solutions in 2021. Our Guide to Machine Learning Fraud Detection Techniques and Implementation Requirements. Roman Chuprina Technical journalist, covering AI/ML, IoT and Blockchain topics with articles and interviews. February 25, 2021.

The Central Bank of Kenya (CBK) recently launched a new smartphone application that helps users familiarise themselves with the new currency features.

The app provides in-depth details on how to detect the fake new currency notes using four different ways:

1.) Using Ultraviolet (UV) light

2.) Using normal light

3.) Using feel/ sense of touch

4.) By tilting the note

The application, that is available on the google play store, can be downloaded and used on Android phones. Here is the download link: https://bit.ly/2Rrqz1i

Nonetheless, the awareness campaign by CBK appears not to be sufficient with some Kenyans stating that they have little time for the internet.

Kenyans are now asking for more education on how to identify the new banknotes.

Luckily for you, Kenyans.co.ke has written this article to help you detect the fake new currency notes, according to the CBK's app.

1. How to detect fake new currency notes using ultraviolet (UV) light

These are the noticeable indicators under UV light:

Ksh50 (Front)

The vertical serial numbers located at the left will glow yellow.

The arrow-shape alongside the dove will have an inward green glow and an outward yellow glow.

The value text 50 located at the bottom right will give off a green glow.

Ksh50 (Back)

There are 2 yellow stripes with the value 50 highlighted in green at the opposite ends overlapped by a green dove.

There are also shades of green and yellow above the dove’s image.

Ksh100 (Front)

The vertical serial numbers located at the left will glow yellow.

The arrow-shape alongside the dove will have an inward green glow and an outside red glow.

The value text 100 located at the top center will give off a green glow.

Ksh100 (Back)

There are 2 red stripes with the value 100 highlighted in green at the opposite ends overlapped by a green dove.

There are also shades of green and red above the dove’s image.

Ksh200 (Front)

The vertical serial numbers located at the left will glow yellow.

The dove located underneath the serial number will also glow.

The value 200 located at the bottom center will glow green.

Ksh200 (Back)

There are 2 yellow stripes with the value 200 highlighted in green at the opposite ends overlapped by a green dove.

There are also shades of green and yellow above the dove’s image.

Ksh500 (Front)

The vertical serial numbers located at the left will glow yellow.

The arrow-shape alongside the dove will have an inward green glow and an exterior yellow glow.

Their are 2 value text 500 located at the bottom center giving off a green glow.

Ksh500 (Back)

There are 2 green stripes with the value 500 highlighted in yellow at the opposite ends overlapped by a yellow dove.

There are shades of green and yellow next to the central lions image.

Ksh1000 (Front)

The vertical serial numbers located at the left will glow yellow.

The arrow-shape alongside the dove will have an inward red glow and an outside yellow glow.

The value text 1000 located at the top center and also bottom right will give off a green glow.

Ksh1000 (Back)

There are 2 green stripes with the value 1000 highlighted in red at the opposite ends overlapped by a red dove.

There are also shades of green and red above the dove’s image.

2. How to detect fake new currency notes using normal light

If you hold up the notes against the light, you’ll see a watermark of a perfect lion’s head, the text CBK, and the value of the banknote. You will also see a security thread which appears as a continuous line.

These are the noticeable differences observed through the sense of sight:

Note: The red eye symbol is the location of the watermark.

Ksh50 (Front)

Held up to the light, a watermark of a lion’s portrait is visible, the letters CBK as well as the value 50.

The security thread appears as a continuous line.


Ksh50 (Back)

Held up to the light, a lions portrait is visible, the letters CBK and also the value 50.


Ksh100 (Front)

Held up to the light, a lions portrait is visible, the letters CBK and also the value 100

The security thread appears as a continuous line.


Ksh100 (Back)

Held up to the light, a lions portrait is visible, the letters CBK and also the value 100


Ksh200 (Front)

Held up to the light, a lions portrait is visible, the letters CBK and also the value 100

The security thread appears as a continuous line.

Ksh200 (Back)

Held up to the light, a lions portrait is visible, the letters CBK and also the value 200.


Ksh500 (Front)

Held up to the light, a lions portrait is visible, the letters CBK and also the value 100.

The security thread appears as a continuous line.

Ksh500 (Back)

Held up to the light, a lions portrait is visible, the letters CBK and also the value 500.


Ksh1000 (Front)

Held up to the light, a lions portrait is visible, the letters CBK and also the value 100.

The security thread appears as a continuous line.

Ksh1000 (Back)

Held up to the light, a lions portrait is visible, the letters CBK and also the value 1000.


3. How to detect fake new currency notes using touch/feel

These are the noticeable differences felt through the sense of touch.

Note: The pointing hand symbol is the location of the feel.

The touch feature is only on the front side of the note.

Ksh50

Bars located on the left side of the note is ONE.

The printed text KENYA at the top is ELEVATED.

The printed value 50 located at the right is also ELEVATED.

Ksh100

Bars located at the left side of the note are TWO.

The printed text KENYA at the top is ELEVATED.

The printed value 100 located at the right is ELEVATED.

Ksh200

Bars located at the left side of the note are THREE.

The printed text KENYA at the top is ELEVATED.

The printed value 200 located at the tight is ELEVATED.

Ksh500

Bars located at the left side of the note are FOUR.

The printed text KENYA at the top is ELEVATED.

The printed value 500 located at the right is ELEVATED.

Ksh1000

Bars located at the left side of the note are FIVE.

The printed text KENYA at the top is ELEVATED.

The printed value 1000 located at the right is ELEVATED.


4. How to detect fake new currency notes by tilting notes

These are the indicators NOTICEABLE observed when tilting the note.

Ksh50 (Front)

The security thread changes color from red to green.

Ksh50 (Back)

The golden band shows the value of the note ie 50.

Ksh100 (Front)

The security thread changes color from red to green.

Ksh100 (Back)

The golden band shows the value of the note ie 100.

Ksh200 (Front)

The security thread changes color from red to green and also has additional rainbow colors.

Ksh200 (Back)

The golden band shows the value of the note ie 200.

Ksh500 (Front)

The security thread changes color from red to green and also has additional rainbow colors.

Ksh500 (Back)

The golden band shows the value of the note ie 500.

Ksh1000 (Front)

The security thread changes color from red to green and also has additional rainbow colors.

Ksh1000 (Back)

The golden band shows the value of the note ie 1000.


Guidelines on how old Ksh1000 notes will be returned to the bank

During the 2019 Madaraka Day celebrations, President Uhuru Kenyatta began the process of demonetizing the old currency and replacing them with new generation notes.

The Central Bank of Kenya has in the meantime released the new notes into the Kenyan market.

It has also maintained its word on ensuring there will be no extension of banning the old Ksh1000 currency notes from the market by 1st October 2019.

The new development is meant to ensure that no Kenyan currency has the image of a person as per what is stated in the Kenyan constitution.

Fake Indian Currency Detection Using Camera Matlab Code

At the same time, the government hopes to weed out graft through execution of some monies held by corrupt individuals.

Here are the guidlines on how to return the old Ksh1000 notes to the bank:

1. Persons exchanging currency notes for amounts not exceeding Ksh1 million of the withdrawn currency notes will exchange at their Commercial banks, CBK Branches and Currency Centres, or any nearest commercial bank.

2. Bank customers exchanging currency notes for amounts Ksh1 million to Ksh5 million of the withdrawn currency notes will exchange at their respective commercial banks, under the normal procedures and requirements.

Fake Indian Currency Detection Using Camera Matlab Code Free

3. Persons without bank accounts exchanging currency notes for amounts exceeding Ksh1 million will require an endorsement from CBK.

4. Persons exchanging currency notes for amounts exceeding Ksh5 million (bulk exchange) will require an endorsement from CBK. These persons should get in touch using the contacts shown below.

Ankush Singh , Ankur Pandey , Aman Tekriwal , Prashant Mankani, Prof. Ketaki Bhoyar, 2019, Detection of Fake Currency using Image Processing, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 08, Issue 12 (December 2019),

Fake Indian Currency Detection Using Camera Matlab Code Pdf

  • Open Access
  • Authors : Ankush Singh , Ankur Pandey , Aman Tekriwal , Prashant Mankani, Prof. Ketaki Bhoyar
  • Paper ID : IJERTV8IS120143
  • Volume & Issue : Volume 08, Issue 12 (December 2019)
  • Published (First Online): 25-12-2019
  • ISSN (Online) : 2278-0181
  • Publisher Name : IJERT
  • License: This work is licensed under a Creative Commons Attribution 4.0 International License
Text Only Version

Detection of Fake Currency using Image Processing

Ankush Singh

Dept of Computer Engineering DYPIEMR Akurdi, Pune (SPPU) Pune, India

Prashant Mankani

Fake Indian Currency Detection Using Camera Matlab Code Online

Dept of Computer Engineering DYPIEMR Akurdi, Pune (SPPU) Pune, India

Prof. Ketaki Bhoyar

Dept of Computer Engineering DYPIEMR Akurdi, Pune (SPPU) Pune, India

Ankur Pandey

Fake Indian Currency Detection Using Camera Matlab Code Download

Dept of Computer Engineering DYPIEMR Akurdi, Pune (SPPU) Pune, India

Aman Tekriwal

Dept of Computer Engineering DYPIEMR Akurdi, Pune (SPPU) Pune, India

Abstract In recent years a lot of fake currency note is being printed which have caused great loss and damage towards society. So, it has become a necessity to develop a tool to detect fake currency. This project proposes an approach that will detect fake currency note being circulated in our country by using their image. Our project will provide required mobility and compatibility to most peoples as well as credible accuracy for the fake currency detection. We are using image processing and cloud storage to make this app portable and efficient.

Keywords Machine Learning, Image Processing, SVM algorithm, Cloud Storage.

  1. INTRODUCTION

    Fake currency detection is a serious issue worldwide, affecting the economy of almost every country including India. Currency duplication also known as counterfeit currency is a vulnerable threat on economy. It is now a common phenomenon due to advanced printing and scanning technology. The possible solutions are to use either chemical properties of the currency or to use its physical appearance. The approach presented in this paper is based upon physical appearance of the Indian currency. Image processing algorithms have been adopted to extract the features such as security thread, intaglio printing (RBI logo) and identification mark, which have been adopted as security features of Indian currency. Hence, we propose a more user friendly and portable solution to this problem in form of an mobile app coupled with cloud storage.

  2. LITERATURE SURVEY

    1. Ms. Monali Patil, Prof. Jayant Adhikari, Prof. Rajesh Babu they proposed a system which uses image processing to distinguishes between features of a real note and a fake note. They used K-means algorithm for feature clustering and SVM algorithm to train their data model.[1].

    2. Mayadevi A.Gaikwad, Vaijinath V. Bhosle Vaibhav D Patil. In their research paper they have suggested a methodology of detecting fake currency from the real by comparing their visual features such as distance between Gandhijis portrait and other notations. This methodology can be useful for a system purely based on software processing.[2]

    3. Renuka Nagpure, Shreya Sheety, Trupti Ghotkar. They have proposed a system which uses the floral designs on the notes provided by RBI to distinguish between real and fake notes.[3]

    4. Neeru Rathee ,Arun Kadian, Rajat Sachdeva ,Vijul Dalel, Yatin Jaie. In their paper they have suggested image processing along with supervised machine learning to learn the distinguishing feature of a real note from fake one which will increase the precision of this method.[4]

    5. Akanksha Upadhyaya Research Scholar, Vinod Shokeen Associate Professor, Garima Srivastava. In their study they have proved that image processing along with logistic regression gives an accuracy of above 99%.[5]

  3. EXISTING SYSTEM

    In existing system, Image processing Is being used with legacy version of machine learning algorithm. Also, they are using local database which reduces the portability of system and because of their system is limited to PC device user friendliness is not good.

  4. PROPOSED APPROACH

    In proposed work, we will develop a system that would perfectly assess the features of fake note and real notes based on the paper by Ms. Monali Patil, Prof. Jayant Adhikari, Prof. Rajesh Babu. Our proposed system will be capable of performing real time detection of fake currency as we are using cloud storage for execution of our image processing logic simultaneously reducing the size of the smartphone app which plays crucial role in memory management of daily users.

    Also, our system will give live update of the identified currencies stock market values w.r.t other currencies around the globe.

    System Architecture

  5. ALGORITHM USED

  1. SVM Algorithm: In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analysis data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non- probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting).

CONCLUSION AND FUTURE SCOPE

Our System will be helpful for the regular peoples who are technically not involved in daily life with background processes. A smartphone app will provide its user an concise way to perform a very necessary task.

In forthcoming future, as discussed by Akanksha Upadhyaya Research Scholar, Vinod Shokeen Associate Professor, Garima Srivastava. In their study that precision of above 99% can be achieved with image processing and supervised learning. Our proposed system could replace the hardware system in some initial stages of currency verification process.

REFERENCES

  1. Fake Currency Detection using Image Processing. Ms. Monali Patil, Prof. Jayant Adhikari, Prof. Rajesh Babu Department of Computer Science and Engineering Tulsiramji Gaikwad Patil College of Engineering Mohgaon Nagpur Maharashtra India.

  2. Automatic Indian New Fake Currency Detection Technique Mayadevi A.Gaikwad, Vaijinath V. Bhosle Vaibhav D Patil College of Computer Science & Information Technology (COCSIT).

  3. Renuka Nagpure, Shreya Sheety, Trupti Ghotkar, 'Currency Recognition and Fake Note Detection', IJIRCCE, vol. 4, 2016.

  4. Neeru Rathee, Arun Kadian, Rajat Sachdeva, Vijul Dalel, Yatin Jaie Feature fusion for fake Indian currency detection. Maharaja Surajmal Institute of Technology, New Delhi, India.

  5. Akanksha Upadhyaya Research Scholar, Vinod Shokeen Associate Professor, Garima Srivastava Analysis of Counterfeit Currency Detection Techniques for Classification Model AIIT Amity University Noida, Noida, India

  6. Ms.Rumi Ghosh, Mr Rakesh Khare, A Study on Diverse Recognition Techniques for Indian Currency Note, IJESRT, Vol.2, Issue 6, June 2013.R. Nicole, Title of paper with only first word capitalized, J. Name Stand. Abbrev. In press.

  7. Amol A. Shirsath S. D. Bharkad, Survey of Currency Recognition System Using Image Processing, IJCER, Vol.3, Issue 7, pp 36-40, July 2013.

  8. M.Deborah and Soniya Prathap Detection of Fake currency using Image Processing. IJISET- International Journal of Innovative Science, Engineering & Technology, Vol. 1, Issue 10, 2014.

  9. Faiz M. Hasanuzzaman, Xiaodong Yang, and YingLi Tian, Senior Member, IEEE Robust and Effective Component-based Banknote Recognition for the Blind IEEE Trans Syst Man Cybern C Appl Rev. 2012 Nov; 42(6): 10211030.

  10. Mohammad H Alshayeji, Mohammad Al-Rousan and Dunya T. Hassoun, Detection Method for Counterfeit Currency Based on Bit- Plane Slicing Technique ,International Journal of Multimedia and Ubiquitous Engieering Vol.10, No.11 (2015).

  11. Nayana Susan Jose, Shermin Siby, Juby Mathew, Mrudula Das, Android Based Currency Recognition System for Blind, International Journal of Engineering Research in Computer Science and Engineering (IJERCSE) Vol 2, Issue 4, April 2015.

  12. Rubeena Mirza, Vinti Nanda, Characteristic Extraction Parameters for Genuine Paper Currency Verification Based on Image Processing, IFRSA International Journal of Computing, Volume 2, Issue 2, April 2012.

  13. Komal Vora, Ami Shah, Jay Mehta, A Review Paper on Currency Recognition System, International Journal of Computer Applications (0975 8887) Volume 115 No. 20, April 2015