Abstract:

In the present-day economic environment, paper currency holds significant importance due to its high face value compared to its intrinsic value, along with advantages such as ease of handling, quick counting, and safe storage. However, these very benefits also make currency vulnerable to counterfeiting, posing a serious challenge to financial systems. Traditional methods for counterfeit detection are often hardware-based, complex, and inaccessible to the general public, limiting their usability. Moreover, human vision alone is insufficient to detect sophisticated counterfeit notes, as counterfeiters continuously adopt advanced techniques to mimic real currency. This project introduces a novel approach for counterfeit detection using Generative Adversarial Networks (GANs), specifically tailored to detect Indian paper currency. The system begins by extracting unique currency features using Convolutional Neural Networks (CNNs), ensuring effective image preprocessing and feature extraction. These features are then fed into a GAN model, where the Generator simulates fake currency images, while the Discriminator classifies the input images as real or fake based on learned patterns. This adversarial process significantly improves detection accuracy by enabling the system to learn fine-grained differences between real and forged currency notes. The proposed deep learning-based system is designed to be user-friendly and accessible, empowering even common people to identify counterfeit currency with ease, contributing towards fraud prevention and financial security.

Description:

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