Forged Document Detection Using Neural Networks (KEY IJP************230)

• Shivkumar Chandrakant Pujari,Anuj Chandrakant Patil,Snehal Nandkumar Patil

Abstract

In This research presents an innovative and sophisticated fraud document detection system in response to the growing sophistication of fraudulent activities using document forgeries. Unlike other approaches, ours combines state-of-the-art deep learning methods with novel feature extraction algorithms to achieve unmatched accuracy and dependability in document fraud detection. With the use of a carefully selected dataset containing a variety of document tampering examples, such as fake stamps, altered content, and forged signatures, our algorithm is able to identify minute patterns and abnormalities with remarkable accuracy. With the help of modern attention mechanisms, adversarial training techniques, and convolutional and recurrent neural networks (RNNs), we are able to overcome the constraints of current systems and set new performance standards for fraud document detection. By means of meticulous testing and contrasting examination, we exhibit the exceptional effectiveness and resilience of our technology in several real-life situations. This project offers enterprises an unparalleled level of security and assurance in protecting against fraudulent operations, marking a significant advancement in the field of document verification technologies.Keywords: Adversarial training methodologies, recurrent neural networks, dataset curation, superior accuracy, and document forgery