Identification of Counterfeit Currency using Machine Learning and Knowledge Discovery

Abstract

Today, every major economy must deal with the problem of counterfeit money. Counterfeit currency is a currency that is produced without the state's or governments legal approval. A portion of the negative cultural repercussions remembers a drop in the worth of genuine cash and an expansion in costs as more cash circles in the economy. That is a reason why governments have used fictitious currencies to wage economic warfare against one another. As a result, we must implement counter-measures that will aid in the prevention of this threat. It is feasible to create high-quality counterfeit banknotes that are difficult to recognize from real notes using computers and technology. In reality, several counterfeit notes were confiscated, many of which replicated many of the security measures found in actual currency notes. As a result, we must develop new approaches to assist consumers in more accurately and comfortably identifying counterfeit cash notes. Knowledge database discovery and machine learning approaches can be used to create tools that can assist with this endeavor. We can train computers to recognize patterns or traits that help them distinguish between real and counterfeit cash. Therefore, the main goal of this research is to create a model that can be utilized to identify fake currency with the least amount of classification mistakes after being trained using pertinent.