A Systematic Review of Deepfake Detection Using Learning Techniques and Vision Transformer

Deepfake technology has transformed the environment of multimedia content generation and manipulation. Deepfakes, which are powered by advanced artificial intelligence (AI) algorithms, can create hyper-realistic images, movies, and sounds that are often indistinguishable from authentic or original content. While these advancements have great potential for the entertainment, education, and creative industries, they also present serious ethical and security concerns at large. The work that has been presented in the paper investigates the growth of deepfake technology, its underlying models, and its numerous societal implications. It navigates the ethical considerations, potential misuse, and countermeasures that are critical for understanding and resolving the consequences of this disruptive technology, delving into the challenges and opportunities related with deepfakes. This work discusses a thorough review of deepfake detection strategies that employ machine learning (ML), deep learning (DL) algorithms, and Vision Transformer (ViT). The goal of this work is to offer understanding on (1) the manner in which deepfakes are generated and detected, (2) the latest results and accomplishments in this field, (3) faults in current security solutions, and (4) areas that require additional investigation and attention. Based on the outcomes, the conventional neural networks (CNN) technique is the most widely used DL method for deepfake identification. There are various deepfake detection techniques which are discussed in this paper such as the generative adversarial network (GAN), convolutional neural network, and Vision Transformer.