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AI can generate realistic deepfake videos.
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SUPPORTED
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12 sources for · 0 against
Is it true that AI can generate realistic deepfake videos?
Yes. Multiple peer-reviewed reviews and reference sources state that advances in generative AI have made it easier to create highly realistic deepfake videos, including viral AI-generated videos of real people. The evidence directly supports the claim that AI can generate realistic deepfake videos.
Verdict: SUPPORTED · 12 sources for, 0 against · Last checked
Evidence for · 12
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Deepfake. (n.d.). en.wikipedia.org. https://en.wikipedia.org/wiki/Deepfake
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More for · 11
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Deepfake pornography. (n.d.). en.wikipedia.org. https://en.wikipedia.org/wiki/Deepfake_pornography
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Clark, S. & Lewandowsky, S. (2025). Seeing is Believing: The Continued Influence of Known AI-Generated ‘Deepfake’ Videos. doi.org. https://doi.org/10.21203/rs.3.rs-6471307/v1
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Clark, S. & Lewandowsky, S. (2026). The continued influence of AI-generated deepfake videos despite transparency warnings. doi.org. https://doi.org/10.1038/s44271-025-00381-9
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S, v. L., I, N., M, W., & Z, G. (2026). Interpol review of detection of AI-generated image and video deepfakes, 2022-2025.. doi.org. https://doi.org/10.1016/j.fsisyn.2026.100704
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Learning Spatio-temporal features to detect manipulated facial videos created by the Deepfake techniques. (n.d.). cir.nii.ac.jp. https://doi.org/10.1016/j.fsidi.2021.301108
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Shahzad, H. F., Rustam, F., Flores, E. S., Mazón, J. L. V., Diez, I. d. l. T., & Ashraf, I. (2022). A Review of Image Processing Techniques for Deepfakes.. doi.org. https://doi.org/10.3390/s22124556
An AI-driven conceptual framework for detecting fake news and deepfake content: a systematic review.
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BV, M., T, T., F, M., & IC, O. (2026). An AI-driven conceptual framework for detecting fake news and deepfake content: a systematic review.. doi.org. https://doi.org/10.3389/frai.2026.1737790
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Khalid, H., Kim, M., Tariq, S., & Woo, S. S. (2021). Evaluation of an Audio-Video Multimodal Deepfake Dataset using Unimodal and Multimodal Detectors. arxiv.org. https://doi.org/10.1145/3476099.3484315
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Chazar, C., Hafiansyah, F., Husada, M. G., Ungkawa, U., & Milenio, R. M. (2026). Penerapan ResNeXt dan Long Short-Term Memory untuk Deteksi Video Deepfake. doi.org. https://doi.org/10.14421/jiska.6085
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Alrowais, F., Hassan, A. A., Almukadi, W. S., Alanazi, M. H., Marzouk, R., & Mahmud, A. (2024). Boosting Deep Feature Fusion-Based Detection Model for Fake Faces Generated by Generative Adversarial Networks for Consumer Space Environment. doi.org. https://doi.org/10.1109/access.2024.3470128
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Salvi, D., Hosler, B., Bestagini, P., Stamm, M. C., & Tubaro, S. (2023). TIMIT-TTS: A Text-to-Speech Dataset for Multimodal Synthetic Media Detection. doi.org. https://doi.org/10.1109/access.2023.3276480
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