Identification of Fake Human Faces Using DCGAN

This project explored the implementation of a Deep Convolutional GAN (DCGAN) to synthesize high-quality, realistic human face images. By leveraging the CelebA dataset for training, the generator network learns to map random noise vectors to human-like faces, whereas the discriminator distinguishes between real and generated images. The architecture employs convolutional and deconvolutional layers to enhance the image fidelity and training stability. Key techniques, including binary cross-entropy loss, Adam optimization, and data normalization, ensure effective learning.