Most of the people use their mobile phones for connecting with one another within the digital age. They share their good and bad moments of their day-to-day life. They share videos, images, and text. They share what they do or did daily. They also exchange pictures and video communications. They change the video or picture by using artificial intelligence to swap out individuals in the picture or video. To solve this problem, you need to identify pictures or videos as fake or original. Deepfake technology is required to lessen its detrimental effects on the world. As deepfake technology has grown in popularity, it has made it possible to produce incredibly lifelike but fake photos and videos. Substituting faces or creating fictitious events. Information integrity and confidence in the public have been undermined by such tampering, making it difficult to distinguish between real and modified media. The model identifies subtle inconsistencies, such as unnatural blending and artifact patterns, to identify the deepfakes. The motive behind that is to demonstrate strong performance in distinguishing manipulated content from real images, contributing to the field of digital media integrity and supporting efforts to maintain online information authenticity. This model aims to create a deepfake detection system using advanced artificial intelligence (AI) techniques, combining convolutional neural networks (CNNs), like YOLOv8, with generative adversarial networks (GANs). These deepfakes help identify whether content is original or fake, as they are created by fusing deep learning methods with manipulated data and have the ability to alter visual information.