Enhancing Identity Verification: Deep Learning for Real and Fake Face Classification in Medical Application

A crucial problem in enabling machines to distinguish real identities from fake ones is the classification of real and fake human looks. This research explores these complexities and presents practical solutions in light of the pervasive difficulties related to misleading personification. This study sets out to build a reliable facial identification model that can tell real from fake facial photos by utilizing the power of deep learning. Our research focuses on identifying the subtle differences between real and fake countenances using a dataset that has been enhanced with profile photographs. Our main goal is to create a sophisticated model that can recognize real and fake faces in the medical field. We want to create an innovative deep learning platform by combining a variety of cutting-edge deep learning approaches.