Fake Currency Detection using Inception V3

Fake currency is an issue that causes considerable problems for financial institutions, enterprises andindividuals. Detecting fake currencies with high accuracy is important for the integrity of financial systems. Research describes a unique approach for detection of fake cash that uses Convolutional Neural Networks (CNN). The proposed system automatically detects the fake currencies using deep learning and computer vision techniques. CNN s are outstanding performers in variety of image classification tasks, makes it good alternative for currency authentication. The process starts with capturing high-resolution picture of the currencies using current image capture technologies like cameras or scanners. To extract features and quality of the image, the image is pre-processed. Guarantees the stability of the model in the face of changing environmental circumstances. The CNN scans the new images of banknote and assigns a probability score that indicates potential for genuineness. It is highly precise, can handle a wide range of currency denominations, and can adapt to new fakeing strategies as they appear. It reduces the need for manual inspection, lowering the danger of human error. In using Convolutional Neural Networks to detect fake currency is a viable solution to the persistent issues of protecting the integrity of financial institutions. With the use of this technology, banknote authentication procedures might become much more accurate and efficient, safeguarding the financial interests of both citizens and governments.