The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Deepfake detection using convolutional vision transformers and convolutional neural networks
Open accessAbstract
Deepfake technology has rapidly advanced in recent years, creating highly realistic fake videos that can be difficult to distinguish from real ones. The rise of social media platforms and online forums has exacerbated the challenges of detecting misinformation and malicious content. This study leverages many papers on artificial intelligence techniques to address deepfake detection. This research proposes a deep learning (DL)-based method for detecting deepfakes. The system comprises three components: preprocessing, detection, and prediction. Preprocessing includes frame extraction, face detection, alignment, and feature cropping. Convolutional neural networks (CNNs) are employed in the eye and nose feature detection phase. A CNN combined with a vision transformer is also used for face detection. The prediction component employs a majority voting approach, merging results from the three models applied to different features, leading to three individual predictions. The model is trained on various face images using FaceForensics++ and DFDC datasets. Multiple performance metrics, including accuracy, precision, F1, and recall, are used to assess the proposed model’s performance. The experimental results indicate the potential and strengths of the proposed CNN that achieved enhanced performance with an accuracy of 97%, while the CViT-based model achieved 85% using the FaceForences++ dataset and demonstrated significant improvements in deepfake detection compared to recent studies, affirming the potential of the suggested framework for detecting deepfakes on social media. This study contributes to a broader understanding of CNN-based DL methods for deepfake detection.
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“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 [3]
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“Intriguingly, as stated in [3], a multitude of these models have exhibited a level of performance that has surpassed the 90-percent accuracy benchmark in binary classification benchmarks.”
From Beyond Accuracy: A Deployment-Oriented Benchmark of Deepfake Detection Models (2025) · cites this paper as [3]
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Metadata
- Authors
- Soudy, A. H., Sayed, O., Tag-Elser, H., Ragab, R., Mohsen, S., Mostafa, T., Abohany, A. A., & Slim, S. O.
- Affiliations
- Helwan University (EG)
- Kafrelsheikh University (EG)
- Corresponding author
- Soudy, Ahmed Hatem
- Venue
- Neural Computing and Applications
- Published
- 2024-08-08
- Volume
- 36
- Issue
- 31
- Pages
- 19759-19775
- ISSN
- 0941-0643 (ISSN-L)
- 1433-3058
- OpenAlex
- W4401426400
- Cited by
- 129
- References
- 30
- Citations by year
- 2026: 79 · 2025: 49 · 2024: 1
- FWCI
- 18.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Neural Computing and Applications (publishedVersion, cc-by)
- Funders
- Helwan University
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- deepfake detection, convolutional vision transformer, convolutional neural networks, fake video detection, face detection, majority voting, FaceForensics++, misinformation, social media
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