The proliferation of counterfeit currency poses a significant threat to economic stability, necessitating advanced methods for effective detection. This project, titled "Identification of Fake Indian Currency Using Convolutional Neural Networks," proposes a novel approach to counterfeit detection by leveraging deep learning techniques. The study explores three primary models: MobileNet, ResNet, and a hybrid model combining MobileNet with Support Vector Machines (SVM), along with a variant that integrates MobileNet with both SVM and Random Forest.

MobileNet and ResNet, known for their efficiency and accuracy in image classification tasks, are evaluated for their performance in distinguishing genuine Indian currency from counterfeit notes. The hybrid model aims to enhance detection capabilities by combining the strengths of MobileNet with SVM, offering a robust solution for handling complex counterfeit patterns. Additionally, the integration of SVM and Random Forest with MobileNet seeks to further improve classification performance by leveraging ensemble learning techniques.

The effectiveness of these models is assessed based on their accuracy, precision, recall, and overall robustness in real-world scenarios. The results highlight the potential of convolutional neural networks in enhancing counterfeit currency detection systems, providing valuable insights for improving financial security measures.

Keywords: Counterfeit Detection, Convolutional Neural Networks, MobileNet, ResNet, Support Vector Machines (SVM), Random Forest, Hybrid Model, Indian Currency, Image Classification, Machine Learning.