Abstract: Technology regarding shaded printing has grown lately the rate of notes being copied for fake cash on a big scale. Despite the rise in popularity of electronic financial transactions and the recent decline in utilizing paper money, banknotes continue to be widely used because of their dependability and simplicity of operation. Printing was only available to print business entities years ago, but as of late anyone can use an average laser printer to print money paper with the highest accuracy achievable. In light of this, phony currency has grown to hold the position of bigger issue than real money. Phoney money is a significant issue for India, which has lamented concerns like abomination and hidden money. This problem is addressed by proposing a deep learning-based method to identify the fake Indian rupee. The Utilizing MATLAB, a tool has been locate the fake cash. As a result, the legitimacy of the Indian currency note will be determined.

Counterfeiting is the practise of making copies of legitimate currency. Hence, the Indian government forbids the use of fraudulent currency. In India, the only authority in charge of printing money is the RBI. As soon as they've been accepted and released onto the market, counterfeit banknotes present an annual the difficulty for the RBI. The printing and scanning companies have seen major advances in technology, which have led to an upsurge in counterfeiting issue. Therefore, counterfeit money affects the economy and devalues legitimate currency. The need to spot counterfeit money is consequently greatest. The vast majority of older systems relied on hardware and techniques for image processing. Finding phoney currency requires more work and is less efficient using these methods. To ensure that tackle the aforementioned issue, which involves we suggested the Identification of Fake Indian Currency Using Xception Architecture. By evaluating the images of the currency, our system can recognize counterfeit money.

Keywords: MAATLAB, Machine learning, counterfeiting, Quillbot

| DOI: 10.17148/IARJSET.2024.11760