Deep Learning for Indian Currency Classification and Fake Currency Detection

Abstract

The invention presents a deep learning-based system for the automated classification of Indian currency and detection of fake currency. By employing Convolutional Neural Networks (CNNs), the system can analyze high-resolution images of Indian currency notes, classifying them into their respective denominations (₹10, ₹20, ₹50, ₹100, ₹500, ₹2000) and identifying counterfeit notes by detecting anomalies in key security features such as watermarks, security threads, microtext, and holograms. This deep learning model is trained on a large dataset of genuine and counterfeit currency notes, enabling it to accurately detect even sophisticated forgeries. The system can operate in real-time, making it suitable for deployment in ATMs, banking kiosks, retail cash counters, and automated vending systems. It is highly adaptable, allowing it to incorporate updates to currency designs and security features issued by the Reserve Bank of India (RBI). The system offers a scalable and efficient solution for currency classification and counterfeit detection, reducing the need for manual inspection and minimizing human error. By providing real-time, accurate classification and detection of fake currency, the invention significantly enhances security and operational efficiency in cash-intensive environments, addressing the pressing issue of counterfeit circulation and ensuring reliable handling of Indian currency.

Specification

Description:Designing a deep learning model for Indian Currency Classification and Fake Currency
Detection involves multiple steps, including data collection, preprocessing, model architecture
selection, training, and evaluation. Fig.1 and Fig. 2 shows a proposed outline for such a model:

Data Collection and Preprocessing: Gather a diverse dataset of images containing genuine Indian
currency notes of various denominations (Rs. 10, Rs. 20, Rs. 50, Rs. 100, etc.). Obtain a separate
dataset of counterfeit/fake currency images, representing different types of counterfeit notes.
Resize and preprocess the images to a standardized resolution and apply data augmentation
techniques to increase the size and diversity of the dataset.
Model Architecture Selection: For the classification task (identifying different denominations),
consider using a Convolutional Neural Network (CNN) as the base model. CNNs are well-suited
for image classification tasks and can learn hierarchical features from images.
For fake currency detection, employ a separate CNN-based model that can distinguish between
genuine and counterfeit notes.
Feature Extraction: In the first part of the model, utilize several convolutional and pooling layers
to extract relevant features from the input images. These layers capture patterns, edges, and
textures present in the currency notes.
Classification Head: Add fully connected layers after the convolutional layers to flatten the
features and connect them to the classification head. The classification head consists of one or more dense layers that map the learned features to the output classes (denominations) using
SoftMax activation.
Fake Currency Detection Head: For the fake currency detection model, add another set of fully
connected layers after the feature extraction layers to predict whether the given currency note is
genuine or counterfeit. This head can also use SoftMax activation to produce a probability score
indicating the likelihood of the note being fake.
Model Compilation: Compile both models with appropriate loss functions (e.g., categorical
cross-entropy for classification and binary cross-entropy for fake currency detection) and an
optimizer (e.g., Adam or RMSprop).
Model Training: Split the dataset into training, validation, and testing sets. Train both models on
the training set using the compiled loss functions and optimizer.
Monitor performance on the validation set to prevent overfitting and adjust hyperparameters
accordingly.
Model Evaluation: Evaluate the models on the separate testing set to measure their performance
and accuracy. Utilize metrics such as accuracy, precision, recall, and F1-score to assess the
models' effectiveness in currency classification and fake currency detection.
Deployment: Once the models achieve satisfactory performance, deploy them in the desired
applications, such as ATMs, cash handling machines, or mobile apps for currency verification.
Continuous Improvement: To maintain the model & accuracy, periodically retrain it with new
data to adapt to any changes in genuine or counterfeit currency characteristics.
, C , C , Claims:
1. We claim that this method will detect the fake notes in real-time.
2. We claim that the system improves the accuracy and performance by differentiating the genuine and fake notes.
3. We claim that the invention enhances the security.
4. We claim that the system is reliable.

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