Generative Artificial Intelligence for image synthesis using Generative Adversarial Networks (GANs) and variational autoencoders
In recent years, the rapid advancement of Generative Artificial Intelligence (GenAI) has transformed the landscape of digital content creation, enabling high-fidelity image synthesis across various fields including healthcare, art, and computer vision. However, the proliferation of synthesized images has introduced critical challenges, specifically the need to distinguish real physical imagery from synthetic, AI-generated counterfeits. This research paper presents a comprehensive, end-to-end framework that addresses both generative synthesis and discriminative detection under hardware-constrained (CPU-only) environments. We implement two synthesis methodologies like a Deep Convolutional Generative Adversarial Network (DCGAN) and a Convolutional Variational Autoencoder (ConvVAE) - trained on real image distributions to generate synthetic data. Concurrently, we present a compact Convolutional Neural Network (MiniCNNClassifier) designed to detect and classify images as real or fake. The framework is validated using a balanced dataset of 60,000 images (30,000 real and 30,000 synthetic). Our preprocessing pipeline ensures uniform size and resolution across heterogeneous inputs. Experimental results demonstrate that the MiniCNNClassifier achieves an outstanding validation accuracy of 98.7% and a Precision of 99.5%, Recall of 97.8%, F1-score of 98.6% in detecting fake samples. Furthermore, we provide a qualitative and quantitative comparison of DCGAN and ConvVAE architectures, discussing trade-offs between training stability and sample fidelity. Finally, we host the models on an interactive Streamlit-based web interface to enable real-time generation and classification.