Learning Spatio-temporal features to detect manipulated facial videos created by the Deepfake techniques
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Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
X. H. Nguyen et al. (2021)'s 3D CNN model performs very well on deepfake FaceForensic and VidTIMIT datasets.
“Studies showed that their suggested model performs very well on deepfake FaceForensic and VidTIMIT datasets.”
From Deepfake detection using deep learning methods: A systematic and comprehensive review (2024) · cites this paper as X. H. Nguyen et al. (2021)
X. H. Nguyen et al. (2021)'s suggested model excelled on both FaceForensic and VidTIMIT datasets by more than 99%.
“The suggested model excelled on both datasets by more than 99% due to the elevated videos.”
From Deepfake detection using deep learning methods: A systematic and comprehensive review (2024) · cites this paper as X. H. Nguyen et al. (2021)
Cites
Rich Models for Steganalysis of Digital Images
Fridrich, J., & Kodovsky, J. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2190402 · Cited by 2,144 · see citing works
DeepFakes: a New Threat to Face Recognition? Assessment and Detection
Korshunov, P., & Marcel, S. (2018) · arXiv · doi:10.48550/arxiv.1812.08685 · Cited by 497 · see citing works
The Deepfake Detection Challenge (DFDC) Preview Dataset
Dolhansky, B., Howes, R., Pflaum, B., Baram, N., & Ferrer, C. C. (2019) · arXiv · doi:10.48550/arxiv.1910.08854 · Cited by 413 · see citing works
Cozzolino, D., Poggi, G., & Verdoliva, L. (2017) · Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security · doi:10.1145/3082031.3083247 · Cited by 349 · see citing works
Energy compaction-based image compression using convolutional autoencoder
Cheng, Z., Sun, H., Takeuchi, M., & Katto, J. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2938345 · Cited by 97 · see citing works
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Deepfake detection using deep learning methods: A systematic and comprehensive review
Heidari, A., Jafari Navimipour, N., Dag, H., & Unal, M. (2024) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1520 · Cited by 349 · see citing works
“Also, X. H. Nguyen et al. (2021) proposed creating three-dimensional (3D) pictures as well as a 3D CNN model that can learn characteristics of spatial and temporal dimensions from the 3D images.”
BackgroundVisual Deepfake Detection: Review of Techniques, Tools, Limitations, and Future Prospects
Ur Rehman Ahmed, N., Badshah, A., Adeel, H., Tajammul, A., Daud, A., & Alsahfi, T. (2025) · IEEE Access · doi:10.1109/access.2024.3523288 · Cited by 45 · see citing works
- Authors
- Nguyen, X. H., Tran, T. S., Le, V. T., Nguyen, K. D., & Truong, D.-T.
- Affiliations
- Trường Đại học Xây dựng Miền Trung (VN)
- Thu Dau Mot University (VN)
- Ton Duc Thang University (VN)
- Corresponding author
- Truong, Dinh-Tu
- ORCID
- Truong, Dinh-Tu
- Venue
- Forensic Science International: Digital Investigation
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 36
- Pages
- 301108
- ISSN
- 2666-2817 (ISSN-L)
- OpenAlex
- W3124321534
- Cited by
- 53
- References
- 5
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 12 · 2023: 18 · 2022: 7 · 2021: 4
- FWCI
- 3.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Forensic Science International Digital Investigation (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- deepfake detection, spatio-temporal features, 3D convolutional neural networks, video forgery detection, face synthesis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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