Improving Video Vision Transformer for Deepfake Video Detection Using Facial Landmark, Depthwise Separable Convolution and Self Attention
Open accessAbstract
In this paper, we present our result of research in video deepfake detection. We built a deepfake detection system to detect whether a video is a deepfake or real. The deepfake detection algorithm still struggle in providing a sufficient accuracy values, especially in challenging deepfake dataset. Our deepfake detection system utilized spatiotemporal feature that extracted using Video Vision Transformer (ViViT). The main contribution of our research is providing a deepfake detection system that based on ViViT architecture and using landmark area images for the input of the system. Our system extracted the feature from a number of spatial features. The spatial feature was extracted using Depthwise Separable Convolution (DSC) block combined with Convolution Block Attention Module (CBAM) from tubelet. The tubelet was a representation of facial landmark area that was extracted from the input video. In our system, we used 25 facial landmark area for an input video. In our experiment we used Celeb-DF version 2 dataset because it is considered to be a challenging deepfake dataset. We conducted augmentation to the dataset, so we obtained 8335 videos for training set, 390 videos for validation set, and 1123 videos for testing set. We trained our deepfake detection system using Adam optimizer, with learning rate of 10-4 and 100 epoch. From the experiment, we obtained the accuracy score of 87.18% and F1 score of 92.52%. We also conducted the ablation study to display the effect of each part of our model to the overall system performance. From this research, we obtained that by using landmark area images, our ViViT based deepfake detection system had a good performance in detecting deepfake videos.
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Transformer-based models achieve notable accuracy improvements, particularly for low-quality forgeries, but their computational intensity remains a significant barrier to real-time deployment.
“While these approaches achieve notable accuracy improvements, particularly for low-quality forgeries, their computational intensity remains a significant barrier to real-time deployment.”
From Beyond Accuracy: A Deployment-Oriented Benchmark of Deepfake Detection Models (2025) · cites this paper as [12]
The Results and Discussion section reports that the modified ViViT detector improved accuracy over conventional ViViT (87.18% versus 52.14%) on Celeb-DF v2. It does not establish that the improvement was particularly for low-quality forgeries or that computational intensity prevents real-time deployment. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredCeleb-DF(v2) contains 590 real and 5,639 high-quality fake videos sourced from YouTube, featuring diverse demographics and expressions.
“Celeb-DF(v2) [12] contains 590 real and 5,639 highquality fake videos sourced from YouTube, featuring diverse demographics and expressions.”
From Beyond Accuracy: A Deployment-Oriented Benchmark of Deepfake Detection Models (2025) · cites this paper as [12]
The Experimental Setup section (page unavailable) identifies Celeb-DF version 2 and reports 5,639 deepfake videos, but it gives 890 original videos rather than 590. It describes the dataset as varied and challenging, but does not establish that the videos came from YouTube or specifically feature diverse demographics and expressions. (LLM, conf=0.90)
Experimental — accuracy not yet measured
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Adaptive Face Region-based Vision Transformer for Video Deepfake Detection
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Temporal Visual–Semantic Graph Reasoning for Fake News Video Detection
Layal, P. K., Chatley, G., & Sharma, R. (2026) · doi:10.1109/aidml70736.2026.11749246
- Authors
- Ramadhani, K. N., Munir, R., & Utama, N. P.
- Affiliations
- Bandung Institute of Technology (ID)
- Venue
- IEEE Access
- Published
- 2024
- Volume
- 12
- Pages
- 8932-8939
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4390738584
- Cited by
- 57
- References
- 27
- Citations by year
- 2026: 27 · 2025: 21 · 2024: 9
- FWCI
- 8.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Access (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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, ViViT, facial landmarks, depthwise separable convolution, convolution block attention module, spatio-temporal features, data augmentation, self-attention
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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