A Deep Automated System for Detection of Fake Currency
K. Sai Nithya Sree, Sampathirao Suneetha, S. Giribabu · 2024
Generating fake currency becomes more complicated for the country's economy. This shows a significant impact on economic growth and harm to national security. Many existing models are available to find fake currency by using machine learning (ML) and deep learning (DL) algorithms. The existing models have several issues detecting fake currency, such as more processing time, highly expensive, and mismatched results. The proposed approach is called the automated approach (AA), which finds the accurate fake currency. In this study, the proposed automated approach combines RESNET50 for training and Generative Adversarial Networks (GANs) to detect and locate fake currency effectively. The training RESNET50 collects the fake currency images and obtains the patterns and factors showing the difference between original and fake currency. The preprocessing technique of Bilateral Filtering is used to denoise the image without losing the edges of the photos. The Grayscale Conversion (GC) model is used as the feature extraction technique, which shows the significant impact on the final output. The proposed GAN model shows high performance in detecting fake currency. Results show that the automated approach performed better in detecting and classifying fake currency.