Advancements in Deepfake Detection : A Review of Emerging Techniques and Technologies

Abstract

This review paper provides a comprehensive analysis of the current state of deepfake detection technologies, driven by the growing concerns over the misuse of synthetic media for malicious purposes, such as misinformation, identity theft, and privacy invasion. The motivation behind this work stems from the increasing sophistication of deepfake generation methods, making it challenging to differentiate between real and manipulated content. While numerous detection techniques have been proposed, they often face limitations in scalability, generalization across different types of deepfakes, and robustness against adversarial attacks. The aim of this paper is to critically assess existing deepfake detection approaches, highlighting their strengths and weaknesses. The objectives include categorizing detection methods, evaluating their performance in diverse contexts, identifying the limitations of current technologies, and proposing future research directions to enhance detection efficacy and adaptability.