Unmasking GAN-Generated Faces with Optimal Deep Learning and Cognitive Computing-Based Cutting-Edge Detection System

The emergence of deep learning (DL) has improved the excellence of generated media. However, with the enlarged level of photorealism, synthetic media is becoming very similar to tangible media, increasing severe problems regarding transmitting fake or deployed data over the Internet. In this situation, it is significant to improve automatic tools to constantly and early identify synthetic media. Generative Adversarial Network (GAN)-based models can create realistic faces that cause deep social and security issues. Existing techniques for identifying GAN-generated faces can execute well on restricted public datasets. Nevertheless, images from existing datasets must signify real situations sufficient for view variants and data distributions, where real faces mainly outnumber artificial ones. Therefore, this study develops an optimal DL-based GAN-generated face detection and classification (ODL-GANFDC) technique. The ODL-GANFDC technique aims to examine the input images properly and recognize whether GAN generates them. To accomplish this, the ODL-GANFDC technique follows the initial stage of the CLAHE-based contrast enhancement process. In addition, the deep residual network (DRN) model must be employed to learn the complex and intrinsic patterns from the preprocessed images. Besides, the hyperparameters of the DRN model can be optimally chosen using an improved sand cat swarm optimization (ISCSO) algorithm. Finally, the GAN-generated faces can be detected using a variational autoencoder (VAE). An extensive set of experimentations can be carried out to highlight the performance of the ODL-GANFDC technique. The experimental outcomes stated the promising results of the ODL-GANFDC technique over compared approaches on the GAN-generated face detection process.