A Comprehensive Review of Machine Learning Techniques for DeepFake Detection

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Deep learning models enhance detection capabilities for deep fakes across various media types, including text and video.
Generative adversarial networks and convolutional neural networks are key techniques deployed to combat misinformation.
Analysis of multiple datasets like FaceForensics++ reveals gaps in diversity impacting detection accuracy and reliability.
Addressing privacy concerns through adaptive models may improve the generalization of detection systems over time.

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This review demonstrates the effectiveness of AI-based deep fake detection methods, indicating privacy concerns must be addressed.

Noman et al. (2025) studied this question.

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