This document discusses using the K-nearest neighbors (K-NN) algorithm to detect counterfeit currency. It first introduces the problem of currency counterfeiting and some existing techniques used for detection. It then focuses on analyzing the performance of different machine learning algorithms, finding that K-NN achieved the highest accuracy. The document proceeds to explain how K-NN works and how it can be applied specifically to currency detection by extracting image features and comparing new images to training data. Finally, it acknowledges some disadvantages of K-NN and proposes using convolutional neural networks for larger datasets in future work.

1. FAKE CURRENCY DETECTION
USING K-NN ALGORITHM
PRESENTED BY:-
ENJOY

2. TABLE OF CONTENT
• Introduction
• Techniques to detect currency counterfeit
• Performance analysis
• K-NN Algorithm
• How K-NN is used in detecting counterfeiting
• Disadvantages of using K-NN
• Conclusion
• Future Work

3. INTRODUCTION
• Currency counterfeiting is always been a challenging term for financial
system of any country. The problem of counterfeiting majorly affects the
economical as well as financial growth of a country, the race is going on
between the counterfeiters and the banks. To resolve the issue various
researchers came across with variety of techniques and proposed solutions
mostly focused on Machine learning and Image processing areas

4. TECHNIQUES TO DETECT COUNTERFEIT
Currency counterfeit can be detected by different techniques
• By checking watermarks , ultra violet waves , various image processing
techniques like canny edge detection , sobel operator, various
machine learning algorithms.
• Nowadays researchers mainly focus on Machine learning and Image
processing areas.

5. Here are performance analysis of some ML
algorithms:-
Out of these three ML algorithms K-NN performed with highest
accuracy rate .

6. K-NN ALGORITHM
• K-NN uses the concept of feature similarity to find out the new data point
values i.e., the value assigned to the new data point is based on the
matching of its value to the points of training set.
• K-NN groups the data into coherent clusters or subsets and classifies the
newly inputted data based on its similarity with previously trained data.
The input is assigned to the class with which it shares the most nearest
neighbors

7. Working of K-NN algorithm:-
• Select the number K of the neighbors
• Calculate the Euclidean distance of K number of neighbors with all training data
points using the formula
• Take the K nearest neighbors as per the calculated Euclidean distance
• Among these k neighbors, count the number of the data points
in each category.
• Assign the new data points to that category for which the number of the
neighbor is maximum.

9. KNN (K-NEAREST NEIGHBOR) METHOD IN
CURRENCY COUNTERFEIT:-

10. The input data set was made by collecting high-quality images of both
genuine and counterfeit currency using an industrial camera. On these
pictures, Wavelet Transform was used to extract features from the
gathered images. The attributes gathered after the Wavelet
Transformation were as follows-
• Variance
• Skewness
• Kurtosis
• Entropy
• Class of the currency

11. - Data set is obtained by plotting various attributes.
- Preprocess and normalize the data set.
data needs to be scaled so that the model is not biassed towards a
particular feature
- Split the data using K-fold cross validation technique.
- Training the model using the algorithm.
- Calculate the performance measures.

12. DIS ADVANTAGES OF USING K-NN ALGORITHM
• Does not work well with large dataset
• Does not work well with high dimensionality
• Sensitive to noisy and missing data
• Feature Scaling

13. CONCLUSION:-
• K-NN is one of the best machine learning algorithm for detection of
currency counterfeit.
• However it doesn’t work well for large datasets
• In this case the algorithm has to deal with image processing and it doesn’t
goes well with image processing because of its sensitivity to noisy and
missing data.

14. FUTURE WORK
• For large data set available, deep learning algorithms like Convolutional
Neural Networks or CNN can be applied which have high accuracy in
image processing scenarios. Furthermore, by using CNN the project can
directly analyze images as input, and wavelet transformation will not be
required. This can make the system more convenient and user friendly to
use.

15. Reference:-
• https://doi.org/10.1109/ICICCS51141.2021.9432274
• https://doi.org/10.1109/ICCS45141.2019.9065747
• https://doi:10.1088/1757-899X/1073/1/012029

16. THANK YOU!