A Prescriptive Deep Leaning-Based Architecture for Deepfake Detection

Over the past few years, detecting and creating deepfakes has emerged as a prominent research challenge within the realm of deep learning. This technology enables the manipulation of audio, video, and facial features with remarkable realism, often disseminated on the internet for various purposes, including tarnishing individuals' reputations. Numerous studies have been conducted on both detecting and generating deepfake content generated by deep learning. This study aims to provide an overview of deepfake detection approaches, available datasets, and research obstacles within this domain. The objective of this study is to propose a deep-learning-based architecture for detecting deepfake images. Although several architectures are capable of detecting deepfakes, deep learning models demonstrate a significant advantage over traditional methods. This study offers a thorough overview of deepfake approaches, aiding in the development of new and more robust architecture to tackle the growing challenges posed by deepfakes.