Improved DeepFake Image Generation Using StyleGAN2-ADA with Real-Time Personal Image Projection

This paper presents an improved approach for DeepFake image generation using StyleGAN2-ADA framework. The system is designed to generate high-quality synthetic facial images from a limited dataset of personal photos in real time. By leveraging the Adaptive Discriminator Augmentation (ADA) mechanism, the training process is stabilized without modifying the network architecture, enabling robust image generation even with small-scale datasets. Real-time image capturing and projection techniques are integrated to enhance personalization and identity consistency. The experimental results demonstrate that the proposed method achieve a very high generation performance, significantly outperforming the baseline StyleGAN2 model. The proposed system using StyleGAN2-ADA achieves 99.1% identity similarity, a low Fréchet Inception Distance (FID) of 8.4, and less than 40 ms latency per generated frame. This approach provides a practical solution for dataset augmentation and supports ethical applications in animation, digital avatars, and AI-driven simulations.