Fake Currency Detection Using Image Processing and Random Forest Algorithm

Abstract

It became relatively easy to make counterfeit
currency as technology advanced, such as
colour printing. Counterfeit notes are
produced so precisely that it's impossible to
tell the difference between a genuine and a
fake. We can define counterfeit currency
notes as copies of actual currency notes
produced without the approval of the state or
central authority and used to conduct
criminal activities. A parallel economy is run
with the help of this fake currency note. This
parallel economy causes a country's economy
to deteriorate, lowering the value of its
currency in the global market. As a result, it
became critical to build a comprehensive
technique to detect counterfeit notes to
decrease the flow of counterfeit notes into the
market. The fake currency detection system
for Indian cash using image processing and
machine learning is discussed in this research
study. In order to identify real currency notes
based on the distinctive features seen on the
Indian currency note of 100 rupees, this
research uses image processing and the
random forest approach. While omitting the
texture and colour of the note, banknote
identification is accomplished by comparing
received banknotes to a database of
previously learned images. On the sample size
of 400 notes, we found accuracy of 84.25%,
recall of 66.25%, and precision of 78.63%.
The recommended solution outperforms
convolutional neural networks in terms of
accuracy and training speed while using a
simpler methodology. To prevent inflation
and economic loss, this approach can also be
used to different types of currency notes.