Advanced Deep Learning Approaches for Detecting Real and Fake Images

The rapid use of artificial intelligence has grown over the world. With this, a lot of AI-generated content is being created in the form of photographs, movies, and so on, leading to people being manipulated. Generative AI models are operating effectively and looks realistic to human sight, mostly in the name of generative fake images taking place with these generated images. Many people are experiencing troubles, so early detection of phony photos is required to stop all of these human concerns. This research aims to evaluate various deep-learning Models for detecting real or fake images using a global dataset. The evaluated deep learning models such as conditional generative adversarial networks(CGANs), RESNET 50, Siamese neural networks, Convolution neural networks (CNNs), deep belief networks (DBNs), and Generative Adversarial Networks(GANs). Finally, the study has found that conditional generative adversarial networks(CGANs) are the best model for detecting an image is real or fake, accomplishing an accuracy of 99.67 percentage on our dataset. These findings suggest that generative adversarial networks(CGAN) is helpful for detecting real or fake images. The outcomes of this research can provide a solid starting point for future studies focused on improving the deep learning models for the detection of real and fake images.