A Comprehensive Survey on the Advancements in Deep Learning Techniques for Detecting Deep Fake Images

Abstract

Understanding and detecting deepfake images is crucial to safeguarding the authenticity and reliability of visual content in digital media, preserving trust and credibility in online information dissemination. Effective detection using advanced deep learning techniques is vital in mitigating the potential risks posed by the proliferation of deceptive content.This survey explores recent advances in deep learning methods used to identify deepfake photos, emphasising their increasing importance in the modern digital environment. The paper provides an overview of the problems caused by deepfakes, examines the methods used to create false content, and studies the use of different deep learning models in the field of deepfake detection, including convolutional neural networks (CNNs), XceptionNet, and capsule networks. It draws attention to the ongoing struggle between the advancement of deepfakes and the development of detection techniques, highlighting the necessity of highly developed and adaptable neural network architectures in order to effectively prevent the spread of misleading data.