FAKE IMAGE DETECTION USING GENERATIVE ADVERSARIAL NETWORKS (GANS) AND DEEP LEARNING MODELS

In recent years, the proliferation of deep fake images and other manipulated media has raised significant concerns about the authenticity of digital content.The advent of Generative Adversarial Networks (GANs) has enabled the generation of highly realistic fake images, posing new challenges in image forensics and security.This paper explores the application of GANs and deep learning models for the detection of fake images.By leveraging the unique architecture of GANs-comprising a generator and a discriminator-alongside advanced deep learning techniques, we propose a robust framework capable of distinguishing between real and synthetic images with high accuracy.Our approach integrates convolutional neural networks (CNNs) for feature extraction, deep residual networks (ResNet) for complex pattern recognition, and GAN-based anomaly detection to enhance the system's ability to identify subtle manipulations.Experimental results demonstrate that the proposed model outperforms traditional methods, achieving superior detection rates on a variety of benchmark datasets.This work highlights the potential of GANs not only as a tool for image generation but also as a powerful asset in the fight against digital misinformation, providing a critical layer of defense in the detection of fake imagery.