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A systematic literature review on deepfake detection techniquesPublished: 02 August 2024(2024)
Cite this article
Multimedia Tools and ApplicationsAims and scopeSubmit manuscriptVishal Kumar Sharma,
Rakesh Garg &
Quentin Caudron
57 Accesses
Abstract
Big data analytics, computer vision, and human-level governance are key areas where deep learning has been impactful. However, its advancements have also led to concerns over privacy, democracy, and national security, particularly with the advent of deepfake technology. Deepfakes, a term coined in 2017, primarily involve face-swapping in videos. Initially easy to detect, rapid advancements in machine learning have made deepfakes increasingly realistic and challenging to distinguish from reality. Generative Adversarial Networks (GANs) and other deep learning methods are instrumental in creating deepfakes, leading to the development of applications like Faceapp and Fake App. These technological advancements, while impressive, pose significant risks to individual integrity and societal trust. Recognizing this, the necessity to develop systems capable of instantaneously identifying and assessing the authenticity of digital visual media has become paramount. This study aims to evaluate deepfake detection methods by discussing manipulations, optimizations, and enhancements of existing algorithms. It explores various datasets for image, video, and audio deepfake detection, including performance metrics to gauge detection algorithm effectiveness. Through a comprehensive review, this paper identifies gaps in current research, proposes future research directions, and provides a detailed quantitative and qualitative analysis of existing deepfake detection techniques. By consolidating existing literature and presenting new insights, this study serves as a valuable resource for researchers and practitioners aiming to advance the field of deepfake detection.~
https://link.springer.com/article/10.1007/s11042-024-19906-1
