A Comprehensive Review of Machine Learning Techniques for DeepFake Detection

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Deep learning models enhance detection reliability against various DeepFake forms, improving detection accuracy.
Key methods evaluated include transformers, generative adversarial networks, and convolutional neural networks.
A comprehensive analysis based on several datasets reveals significant biases affecting detection performance.
The research underscores the need for improved dataset standards and ethical frameworks for maintaining digital trust.

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Comprehensive review finds advanced deep learning models improve detection reliability in multimedia DeepFakes, highlighting critical challenges.

Noman et al. (2025) studied this question.

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Comprehensive review finds advanced deep learning models improve detection reliability in multimedia DeepFakes, highlighting critical challenges.