A comprehensive survey of detecting deepfakes techniques

Recent advancements in technology have led to the proliferation of deepfakes. Deepfakes, AI-generated fake videos, pose a significant threat by spreading misinformation. This survey paper offers a comparative study of various techniques employed in detecting deepfake content. Through a systematic ex...

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Bibliographic Details
Main Authors: Maske, Dheeraj, Munot, Satyam, Mugut, Aman, Mundada, Girish
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Artificial neural networks
Comparative studies
Cost effectiveness
Deception
Generative adversarial networks
Image manipulation
Parameter robustness
Online Access:Get full text
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Summary:Recent advancements in technology have led to the proliferation of deepfakes. Deepfakes, AI-generated fake videos, pose a significant threat by spreading misinformation. This survey paper offers a comparative study of various techniques employed in detecting deepfake content. Through a systematic examination of algorithms such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and hybrid models, the study provides valuable insights into their strengths, limitations, and applicability. It also explores innovative methods like Blockchain-based Traceability, Lip Sync Detection, and Human-in-the-Loop Verification encompasses parameters like efficiency, robustness, and cost-effectiveness, aiding readers in understanding the nuanced complexities of deepfake detection.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0228224