Efficient Identification of DeepFake Images using CNN

The development of Deepfakes has become more prevalent with the rise of Generative Adversarial Networks (GANs). Deepfakes are synthetically created, modified images that have been made to look real; they pose severe social concerns with privacy an d security issues. To solve these issues, this paper offers a novel method to detect Deepfakes in photographs using a Convolutional Neural Network (CNN) architecture. To detect whether a image has been altered or not, the suggested method examines many aspects of the image. The proposed methods are tested using a sizable benchmark dataset of Deepfakes and non-Deepfakes, and were able to identify modified images with high precision. The strategy is resistant to a variety of manipulations, including those intended to trick cutting-edge detection techniques. It is found that proposed strategy will improve digital media's security and credibility while helping mitigate any possible problems of Deepfake images.