September 10, 2025Open Access

Deepfake Video Detection: A Comprehensive Survey of Techniques, Challenges, and a Proposed CNN-LSTM-Based Framework

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The proposed cnn-lstm architecture improves deepfake video detection accuracy through hybrid feature analysis.
Current techniques include traditional, machine learning, and deep learning methods, providing varied detection capabilities.
Key datasets and performance metrics are critical for benchmarking deepfake detection models in real-world applications.
Technical, ethical, and robustness concerns regarding deepfakes highlight the need for improved detection solutions.

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This survey reviews deepfake detection techniques and proposes a cnn-lstm framework, highlighting challenges and datasets.

Sunitha et al. (2025) studied this question.

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