A Novel Method of Fake Signature Detection Using Deep Learning Techniques

In today's digital world, making sure signatures are real is super important. Sometimes, though, people fake signatures, causing problems for money and legal stuff. This research looks into fixing this issue by finding a new way to catch fake signatures. We know that nowadays, a lot of things happen online, like signing documents and doing money stuff, and we need to make sure signatures are real. This paper introduces a fresh perspective by leveraging Convolutional Neural Networks (CNN), a deep learning technique. The objective is to enable the CNN model to autonomously learn and distinguish genuine signatures from counterfeits, offering a dynamic and adaptive solution to the evolving landscape of forgery. The research encompasses the development and application of a novel signature detection model, highlighting its adaptability and effectiveness. Through experimentation with diverse datasets and the optimization of hyperparameters, the approach aims to significantly enhance the accuracy of fake signature detection. The outcome of this research holds the promise of bolstering the security of digital transactions and contributing to the evolution of more robust signature verification systems. Keywords: Forged Signature, Convolutional Neural Networks (CNN), Digital Transactions, Signature Authentication