Investigation of Various Machine Learning Techniques for Banknote Authentication

Banknote authentication is a critical aspect of the financial industry, playing a vital role in ensuring the integrity of transactions and safeguarding against counterfeiting and fraudulent activities. The prevention of counterfeit banknotes is of utmost importance due to the significant economic and national security risks associated with such activities. To address this challenge, advanced techniques utilizing machine learning algorithms trained on extensive datasets are employed to identify distinctive features and patterns characteristic of authentic banknotes. This work focuses on comparing and evaluating the performance of various machine learning models in the task of banknote authentication. The models under consideration include Logistic Regression With LBFGS, Logistic Regression With SGD, SVM With SGD, Random Forest, XgBoost, and Neural Network. By analyzing various parameters, the study aims to identify the most accurate and efficient approach for verifying banknote authenticity, thereby ensuring the integrity and security of financial transactions. The impact of counterfeit banknotes extends beyond the financial sector, affecting the economy and public trust in the currency's value. Thus, an effective and accurate banknote authentication system is crucial for maintaining a stable economy. The findings from this study will contribute to improved accuracy and efficiency in detecting counterfeit banknotes, mitigating financial losses, and preserving the economy's security and stability.