Unmasking Deepfakes: A Systematic Review of Generation Techniques and Detection Strategies

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This review examines deepfake generation and detection methods, highlighting the challenges and future directions in AI governance.

Deepfake technology, fueled by generative adversarial networks, is raising concerns over misinformation and identity theft.
A variety of detection techniques, including CNNs and transformers, are being developed to combat manipulated content.
Challenges include the need for efficient, scalable detection models and addressing ethical implications in AI development.
The future lies in self-supervised learning and interpretable AI, which could enhance the understanding of detection models.

Eidan et al. (2025) studied this question.

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View Full Paper

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Authors

This review examines deepfake generation and detection methods, highlighting the challenges and future directions in AI governance.

Deepfake technology, fueled by generative adversarial networks, is raising concerns over misinformation and identity theft.
A variety of detection techniques, including CNNs and transformers, are being developed to combat manipulated content.
Challenges include the need for efficient, scalable detection models and addressing ethical implications in AI development.
The future lies in self-supervised learning and interpretable AI, which could enhance the understanding of detection models.

Eidan et al. (2025) studied this question.

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