Generate Synthetic Naira Notes Using Generative Adversarial Networks

Project Overview

This project leverages a Conditional Generative Adversarial Network (C-GAN) to generate synthetic images of Nigerian Naira notes. The primary objective is to produce realistic fake Naira notes to enhance the training datasets for counterfeit detection models. By augmenting these datasets, the project aims to improve the accuracy and robustness of systems designed to identify counterfeit currency, contributing to financial security in Nigeria.

• Purpose: Enhance counterfeit detection models for Naira notes.
• Significance: Provides diverse synthetic data to strengthen detection systems against real-world counterfeiting threats.

Dataset

The dataset is presumed to consist of images of genuine Naira notes, likely spanning various denominations such as 5, 10, 20, 50, 100, 200, 500, and 1000 Naira. These images serve as the foundation for training the C-GAN to generate synthetic notes that closely mimic real currency.

• Denominations: Includes common Naira denominations.
• Details: Specifics (e.g., total number of images, resolution) are not explicitly detailed but are assumed to be sufficient for training.

Model Architecture

The project employs a Conditional GAN (C-GAN) framework, which consists of two key components:

• Generator: Takes a random noise vector and a condition (e.g., denomination label) as input, producing synthetic Naira note images tailored to the specified denomination.
• Discriminator: Analyzes pairs of images and conditions to distinguish between real and generated images, ensuring the generator improves over time.

The conditional aspect allows for targeted generation of notes specific to each denomination.

Implementation

The implementation follows standard practices for GANs, with the following inferred details:

• Generator: Built using a convolutional neural network (CNN) with upsampling layers to convert noise into high-resolution images.
• Discriminator: Utilizes a CNN with downsampling layers, possibly incorporating batch normalization and leaky ReLU activations for stability.
• Training: Alternates updates between the discriminator and generator, using binary cross-entropy loss and an optimizer like Adam.

Evaluation Metrics

The quality of the synthetic Naira notes is assessed through a combination of methods:

• Qualitative Metrics: Visual inspection to evaluate realism, focusing on color accuracy, design details, and security features.
• Quantitative Metrics: Metrics such as Inception Score (IS) and Frechet Inception Distance (FID) to measure image quality and diversity.

Additionally, the synthetic notes are used to train a counterfeit detection model, with performance evaluated using accuracy, precision, recall, and F1-score on a separate validation set.

Ethical Considerations

Generating synthetic currency images poses ethical challenges, particularly the risk of misuse for counterfeiting. This project mitigates such concerns by focusing exclusively on improving detection capabilities, not producing usable counterfeit notes. The synthetic data is intended solely for research and training purposes to bolster financial security.

• Responsible Use: Emphasizes ethical AI development by enhancing detection rather than enabling misuse.

Usage

To get started with this repository:

1. Clone the Repository:

   git clone https://github.com/M-S-ibrahim/Generate-Naira-Notes-Synthetic-Using-Generative-Adversarial-Networks.git

2. Install Dependencies: Install required libraries (e.g., TensorFlow or PyTorch) as specified in the repository (assumed standard deep learning tools).
3. Run the Notebook: Open and execute the C_GAN_MAIN.ipynb notebook to train the model and generate synthetic Naira notes.

Note: Exact installation steps and dependencies may depend on the repository’s specific requirements.

This README provides a comprehensive yet concise guide to the project, ensuring users understand its goals, methods, and responsible use.