Abstract: Deep learning has revolutionized various fields including computer vision, big data analytics, and automation. However, the same technologies that drive innovation have also enabled the rise of deepfakes—AI-generated media designed to mimic real human expressions and voices with alarming accuracy. This paper presents a comprehensive overview of the mechanisms behind deepfake creation and critically evaluates the current state of detection techniques. Through a review of literature and research methodologies, we examine the evolution of both generation and detection approaches, discuss emerging challenges, and propose future directions for enhancing the robustness of deepfake detection systems. This work aims to provide a solid foundation for researchers and developers striving to mitigate the misuse of deepfake technology and preserve digital integrity.
sequences in videos, and inconsistencies in spatial features.
Keywords: Deepfake, Machine Learning, Convolutional Neural Network, Transfer Learning, FaceForensics++, Detection Algorithms.
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DOI:
10.17148/IJARCCE.2025.14597
