GAN-Generated Face Detection with Advanced Deep Neural Network Architectures and Pre-Processing Techniques

The incredible hyper-realism that Generative Adversarial Networks (GANs) have been able to produce in recent years-including photographs of human faces-makes it very difficult to distinguish genuine photos from fakes. In order to identify GAN-generated faces, this research suggests a particular architecture for deep neural networks (DNNs). For each category, the DNN model learns complex features and patterns from a large dataset that includes both actual and GAN-generated face photos. The model's generalizability and robustness are improved by using advanced preprocessing methods, such as data normalization and augmentation. In order to accomplish the classification job, the design makes use of fully connected layers after convolutional layers have extracted high-level information. The proposed DNN outperforms conventional image analysis techniques in experiments that compare GAN-generated images to genuine faces, demonstrating great accuracy in face detection. In addition to adding to the expanding corpus of literature in digital picture forensics, this study highlights the need for trustworthy detection methods to prevent the possible abuse of GAN-generated information. The results show that deep learning methods work well to combat new cyber risks and open the door to better synthetic media identification in the future.