A Comprehensive Review of Deepfake Detection Techniques: Challenges, Methodologies, and Future Directions
Keywords:
Deepfake Detection, Generative Adversarial Networks, Transformers, CNNs, Fairness, Generalization, RobustnessAbstract
This review paper provides a comprehensive analysis of various deepfake detection techniques, focusing on image, video, audio, and textual deepfakes. The study explores recent advancements in deep learning models, including Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Transformers, and hybrid frameworks. Additionally, issues related to generalization, fairness, and robustness are examined. The methodologies from different research papers are compared and evaluated based on their performance, datasets, evaluation metrics, and applicability. Furthermore, the paper highlights challenges in deepfake detection and presents potential future research directions. The findings aim to contribute to developing more reliable, scalable, and generalizable deepfake detection systems.
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