Detecting GAN-Generated Fake Images Using Deep Learning and Feature Analysis

The dissemination of extremely realistic deep-fake images created by Generative Adversarial Networks (GANs) is a serious threat to the integrity of digital information in a number of critical fields. The forged images, normally indistinguishable from real ones for the human eye, compromise security systems, media credibility, and the reliability of digital proof for forensics. To address the increasing demand for effective detection, this work presents a new approach that synergistically combines deep learning strategies with advanced feature analysis. The proposed approach is particularly designed for detecting the subtle artifacts and statistical anomalies inherent in the generation process of the GANs. The proposed approach is anticipated to obtain state of-the-art detection performance and improved generalizability across a large number of GAN architectures compared with the current detection strategies. The outcome of this work will be of great significance for the ongoing endeavors toward limiting the detrimental impact of fake images on security, media integrity, and the reliability of digital proof.