Deep Learning for Fake Image Detection

Abstract

A prominent deep learning-based approach which has gained significant attention in deepfake detection is the Vision Transformer (ViT) model. ViT utilizes self-attention[2] mechanisms to capture global and local features of an image. By exploiting the distinctive patterns and inconsistencies introduced by deepfake generation algorithms, ViT-based detection systems have shown promising results in preciously distinguishing between genuine and manipulated media. Deep learning is a strong and versatile technology that has found widespread use in of Machine Learning, Convolution Neural Network, Computer Vision and technologies like GANs which alter people's photos and videos to the point that humans can't tell them apart from the real thing. Numerous studies have been conducted in recent years to better understand how deepfakes function, and many deep learning-based methods for detecting deepfakes videos or photographs have been presented. We present a thorough examination of deepfake generation and detection systems based on deep learning methods in this study. Additionally, we give a thorough analysis of various technologies and their application in deepfakes detection. Our study will assist researchers in this subject because it will cover the most current state-of-the-art approaches for detecting deepfakes videos or images on social media. Furthermore, because it covers the most recent methodologies and datasets utilized in this domain in depth, it allows comparison with previous work.