Faux Reality Detector

With the advent of a new era in digital content creation, deep learning and generative models have completely changed the way we interact with multimedia. Strong generative adversarial networks (GANs) have been developed, but this has led to grave worries about the potential for malevolent usage. Deep fakes, which are produced by GAN and are hyper-realistic digital forgeries that might be used to spread incorrect information and fool people, pose a serious threat to civilization. As a result, developing reliable and effective deep fake detection algorithms has become an important study area. The major goal of this project is to create and deploy a deep fake detection system that can accurately identify between authentic and false images. Such fake faces can be recognized using a deep learning architecture based on CNNs (Convolutional Neural Networks). Such fake faces can be identified with practically perfect accuracy using a deep learning architecture based on CNNs (Convolutional Neural Networks). based on the fact that the textures of real faces and synthetic ones are very different. The comprehensive research and analysis carried out to understand the fundamental concepts underlying deep fake generation and detection are described in this paper.