Signature Forgery Detection Using Deep Learning

Digital signatures are widely adopted by organizations, both public and private, in recent times due to their legal validity and ease of handling and storage. They find extensive usage in e-commerce websites for customer authentication during deliveries, bank procedures, government organizations, and various other businesses. Governments also utilize digital signatures for contract signing and document verification. However, with advancements in Information Technology (IT), there are both advantages and disadvantages. While digital signatures offer convenience, security, and cost savings, they also pose risks, such as potential forgery or manipulation. To address the risk of signature forgery, researchers are exploring deep learning algorithms like VGG16. These algorithms analyze signature data to differentiate between genuine and fake signatures by learning patterns and features from a dataset. By training and testing these algorithms on diverse signature samples, researchers aim to develop robust systems for detecting and mitigating signature forgery attempts. In summary, digital signatures play a vital role in modern organizational operations, offering benefits like legal validity, convenience, and enhanced security. However, addressing potential risks, such as forgery, requires ongoing research and technological advancements, including the application of deep learning algorithms like VGG16.