A review of Fake Currency Recognition Methods

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 07 | July 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 2633
A review of Fake Currency Recognition Methods
Sruthy R
Guest. Lecturer, Dept. of Electronics & Communication Engineering, NSS Polytechnic College, Kerala, India
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Abstract –The global economy is vulnerable to counterfeit
currency. Advanced printing and scanning technologies have
made it a common occurrence. For both people and
corporations, fake currency recognition is a serious issue. The
creation of counterfeit banknotes, which are barely
distinguishable from legitimate currency, is a continuous
process for counterfeiters. To detect fake notes, several
traditional techniques and approaches are availablebased on
colors, widths, and serial numbers. This paper discusses
different methods of fake currency detection using image
processing.
Key Words: Fake Currency detection, Machine learning,
Support Vector Machine, Convolutional neural network,
AlexNet, K Nearest Neighbour (KNN), Support Vector
Classifier(SVC), Gradient Boosting Classifier(GBC),
ResNet50, DarkNet53, GoogleNet, Linear Discriminant
Analysis(LDA), Canny Edge Detector
1. INTRODUCTION
Digitalization is bringing about a rapid acceleration in
fraudulent activities, particularly in the financial
sector. Technology has greatly facilitatedtheproliferationof
fake money. Modern counterfeit money is very identical to
real money [1]. Even though identifying counterfeit cash
might be difficult, automated currency note recognition
systems have advanced significantly in recent years. It thus
attracts the interest of several modern research experts.
Despite their usefulness, today's fake money detectors are
too expensive to be used by the average person.
Recent studies are concentrating on fake currency
recognition based on image processing to resolve this
problem [1][2].
This paper reviews various methods of fake currency
detection based on image processing and machine learning.
The different methods are presented in Chapter 3.Chapter4
depicts the comparison of these methods.Chapter5includes
the conclusion of the study.
2. LITERATURE REVIEW
AlexNet-based fake currency detection has been proposedin
[3]. This transfer learned convolutional neural network is
trained using data sets consisting of 50,200,500,2000 Indian
rupee notes to obtain feature vectors. Here, the average
accuracy for identifying real currency and counterfeit
currency was 81.5percentand75%,respectively.AdeepCNN
model has been presented in [4] to detect counterfeit
currency. It is intended to detect counterfeit notes on
portable electronics like smartphones and tablets. A self-
generated dataset of 10,000 images including 500 actuals,
500 fake, and 2000 real and 2000 fake notes was used to
train and test the CNN model.Testing accuracy of 85.6%was
obtained through this method.
An ensemble of classifiers has been used for the fake
currency classification task in [5]. Moreover, this system is
based on more than one security feature. DT, SVM, LDA, and
KNN are the classifiers employed in this system. With an
accuracy of 82.7 percent for all features, the SVM classifier
outperforms all other classifiers. Four different CNN called
Alexnet,Resnet50,Darknet53,andGooglenethavebeenused
for Indian currency recognition in[6]. The findings
demonstrated that each of the four preconfigured networks
excels at one parameter while sacrificing the others.
KNN, SVC, and GBC have been used for fake money
recognition in [7]. KNN isa suitable candidate forapplication
in the computer vision job because of its excellent accuracy
forsmaller data sets. Machine learning algorithmsandimage
processing techniques are used to obtain the desired
outcome and accuracy. Here, KNN and GBC provide higher
accuracy in the recognition task.
A fake cash detection technique that takes advantage of edge
detection has been presented in [8]. A training dataset
identical to the one that will be tested later is used by the
detector. This edge detector-based system gives 90.45%
accuracy in the fake currency detection system. A unique
Optically Variable Device (OVD) patch was applied in [9] to
help identify fake Philippine notes. Here, the Canny Edge
algorithm identifiedcounterfeit currency usingOVDsecurity
features. It showed statistically significant detection rates
under a threshold of significance of 5 percent for all four
tests.
3. METHODOLOGY
3.1 AlexNet-based system
This system was implemented to classify fake Indian
Currency. Transfer learned Alex network with Adam
optimization has been used in the suggested technique [3].
Automatic feature extraction andfakecurrencyclassification
from the input image of note is done by Alex Net, which is
composed of 5 convolutional layers, 5 Max pooling layers, 2
Dropout layers, and 3 fully connected layers. The technique
is examined in real-time using a webcam. Following the

acquisition of the picture, the network learns how the input
currency note is constructed and compares it to its learned
features to produce a "Real Note" (or "Fake Note") result.
3.2 Deep CNN model-based system
An image of the ‘note’ must be captured or uploaded by the
user and is added to the real-time databaseFirebaseinorder
to receive results immediately [4]. After feeding the image
into the CNN model, the output appears on the screen
shortly afterward. The CNN model receives an image from
the database and predicts the outcomes, which are then
returned to the database. The picture is pre-processed and
converted to an 80 × 80-pixel size. CNN classifies whether
the note is fake or not by extracting features from the image.
The Deep CNN model has been implemented using 5
convolutional layers, 4 fully connected layers, and a single
flattened layer.
3.3 Feature Ensemble Approach based system
Here, an ensemble of six classifiers has been used for
currency recognition [5]. At first, the input image of the note
is converted to a grayscale image. This image is segmented
using ChanVese Segmentation which is a blend of the active
contour model and Mumford Shah model. DuringChanVese
segmentation, each pixel of the input image is assigned
either a true or false value. In this system, 6 securityfeatures
of notes are considered for fake currency recognition.
ROI can be calculated by partitioning the image ofa noteinto
16 blocks and merging these blocks to create images that
represent each security feature. Eachofthesecurityfeatures
is classified using different classifiers., SVM with linear
kernel, LDA, KNN, and Decision trees (DT) are the classifiers
employed in this system.
3.4 CNN-based System
Here, four different CNN architectures named AlexNet,
Darknet-53, GoogleNet, and ResNet-50 have been used for
detecting fake currency [6]. The dataset has been divided
into training and testing sets, which consist of the two
classes labeled Original and Fake Indian Note Currency.
Utilizing predetermined convolutional neural networks, the
training set and testing set attributes are retrieved. Support
Vector Machine (SVM) is used to classify the test picture of
the note as real or fake cash after characteristics have been
extracted using CNN.
3.5 Machine Learning Algorithm and Image
Processing-based system
In this system, due to the substantial variation in all the
feature values, the dataset has been normalized. Then data
has been divided utilizing the K-fold cross-validation
technique. The prediction model has been trained using K-
Nearest Neighbours, Support Vector Classifier, andGradient
Boosting Classifier. KNN categorizes a given data point by
examining its nearest neighbors and giving each one a score
based on the distance between them. The closestdata points
are given a greater weight based on the distance.
Determining the best-fitting hyper-plane for splitting the
categorization is how SVM classification is carried out.
Gradient boosting classifiers are built using a decision tree-
like paradigm, where layers of yes-or-no inquiriesareposed
to produce a prediction model. In this system, all three
classifiers provide more than 97% accuracy in the currency
classification task.
3.6 Edge detector-based system
Here, a camera or other device has been used to take a
picture of the currency [8]. Then the image is resized and
converted to the grayscale format. Then the edges of the
image have been detected using an edge detector. Then the
image is segmented using various machine learning and
clustering algorithms. Some dimensionality reduction
approaches are then utilized to highlight the key elements of
the image. The generated picture is then compared to the
data set already in place as the last step to determine if the
note is authentic or fraudulent (counterfeit).
3.7 Canny Edge detection Algorithm-based
system
In the first step, the security attributes of the reference
image are saved. The GUI software will evaluate the
note under test with the reference note after the user clicks
the "Compare" button [9]. The GUI clearly shows the
difference between these two notesonthecomparison.Here
Canny edge detection algorithm is employed for image
enhancement and sensing. It is a multiphase edge detection
algorithm that consists of an upper and lower threshold as
the parameters. Optically Variable Device (OVD) patches, a
unique security feature that outperforms the conventional
three-way recognition of notes, were added to some
Philippine banknotes in ordertorecognizecounterfeitnotes.
4. COMPARISON
The comparison of various fake currency recognition
techniques is shown in Table-1.

5. CONCLUSION
Finding the best approach to detect and recognize
counterfeit money is a problem that is becoming worse for
the scientific world. This review provides a summary of the
identification of fakecurrencyusingnon-conventional image
processing techniques. Majority of the reviewed fake
currency detection works are based on Indian Currency
Notes. The use of image processing algorithms, CNN models,
Machine learning algorithms make the counterfeit currency
recognition task quick and easy. All these techniques gives
more than eighty percentage accuracy in recognition task.
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No Title Method Dataset Accuracy
1
Real time fake currency note detection
using deep learning. [3]
AlexNet
Indian Currency
(50,200,500,2000 notes)
87% for real
2000 note,
82% for fake
note
2
Counterfeit Currency Detection using
Deep
Convolutional Neural Network [4]
Deep CNN
Indian Currency
(500, 2000 notes)
85.6%
3
Indian Currency Classification and Fake
Note Identification using Feature
Ensemble Approach [5]
Chanvese Segmentation
SVM, LDA, KNN, DT
Indian Currency
(100,200,500,2000 notes)
88.5%
4
Identification of Fake Indian Currency
using
Convolutional Neural Network [6]
Darknet53
AlexNet
ResNet-50
GoogleNet
Indian Currency
(10, 20,50, 100, 200 and
1000 notes)
72.04%
65.15%
80.94%
64.64%
5
Fake Currency Detection with Machine
Learning Algorithm and Image
Processing [7]
KNN
SVC
GBC
Indian Currency
99.9%
97.5%
99.4%
6
Fake currency detection using Image
processing [8]
Edge Detection,
Segmentation
Indian Currency
(500)
90.45%
7
Philippine currency paper bill
counterfeit detection through image
processing using Canny Edge
Technology [9]
Canny Edge detection and
OVD patch
Philippine currency Peso
500, 1000
95%
Table 1: Comparison

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