FAKE HUMAN FACE GENERATION USING DCGAN

Abstract

The rise of Generative Adversarial Networks (GANs) has enabled the creation of highly realistic synthetic images, transforming applications in computer vision and graphics. This project focuses on generating fake human faces using Deep Convolutional Generative Adversarial Networks (DCGANs). DCGANs combine deep convolutional neural networks with adversarial learning, where a generator network produces synthetic images and a discriminator network evaluates their authenticity. Through iterative training, the generator improves its capability to create photorealistic faces that mimic real human features, expressions, and variations. The system is trained on large-scale facial datasets, allowing it to generate diverse and realistic human faces. This approach demonstrates the potential of DCGANs in image synthesis, virtual reality, computer graphics, and AI-driven media creation, while also raising important considerations regarding deepfakes, ethical usage, and digital authenticity.