A review examining the application of deepfake

Hyper realistic modified videos, or "deepfakes," have become a potent tool with both malevolent and artistic uses. The state-of-the-art in deepfake implementation is examined in this article, with a particular emphasis on Generative Adversarial Networks (GANs) and its capacity to produce convincing forgeries. We explore the architectures utilized for producing videos and manipulating facial attributes, emphasizing important datasets that these models were trained on. The research also looks at deepfake detection strategies, evaluating approaches that make use of neural texture analysis, geometric clues, and video representations. Through comprehending the production and identification of deepfakes, we may better prepare ourselves to makeover through this dynamic technological environment.