Guarding Finances: The Role of Image Processing in Unmasking Counterfeit Currency

In many economies worldwide, including India, the detection of counterfeit currency is a severe problem. This report suggests an original approach to finding and distinguishing duplicated banknotes based on the discrete wavelet transform (DWT) that is both new and structurally efficient. In order to detect spurious parts in banknotes and take decisions about their veracity, matching scores from all forgery detection modules are combined together. The most important part of our study is comparing features extracted from different currencies which helps us to effectively differentiate between real and fake bills. Use mean square error as a metric for comparison between two images while evaluating performance. We create a database with genuine Indian notes of different denominations, extract its features, convert them into binary equivalents and calculate their MSE (mean square error). Preprocess test currency note image for noise removal and negative artifacts elimination during Fake Note Detection System implementation proposed here after preprocessing operations on a test currency note image. To develop a database of real Indian money notes and extract main attributes by means of object-oriented segmentation and clustering, an algorithm was created. This fake detection system worked well in terms of efficiency because it could also recognize duplicated banknotes quickly through matching scores from various duplicate recognition modules, thus ensuring that only counterfeit items were flagged as such. Therefore, we have come up with this solution for dealing with forged cash problems which can be used to protect the Indian economy from any further damage.