Real-Time Deepfake Image Generation Based on Stylegan2-ADA

Abstract

Training Generative Adversarial Networks (GAN) usually leads to hyper-specialization due to few data and this causes training to diverge. This paper proposes a method that significantly stabilizes training without making changes. The method which will be used is stylegan2-ADA method to get fake images, the images will be entered in several steps, where the first step is using 76,400 Flickr-Faces-HQ (FFHQ) images and training them to get fake images. The program will be dividing images inside seven test folders, as the performance rate of 1000 images is 83.3%, which is a very good percentage when compared with the stylegan2 method because our proposed method contains augmentation that generates many images through the use of few images. The second step is represented by using personal images, we used two personal images and made a projection between them. The result of the performance of generating 200 images is 99.9%. Additionally, will be took a direct photo using the computer camera in real-time mode, and i generated 300 images. The generation performance is 99.9%, and our approach outperformed earlier ones in terms of accuracy, the ability to produce images without noise, and ability to create fake images of people who are not actually there.