Deepfake detection using convolutional vision transformers and convolutional neural networks

Soudy, Ahmed Hatem; Sayed, Omnia; Tag-Elser, Hala; Ragab, Rewaa; Mohsen, Sohaila; Mostafa, Tarek; Abohany, Amr A.; Slim, Salwa O.

doi:10.1007/s00521-024-10181-7

Cited by 130Digital Media Forensic DetectionFace recognition and analysisMisinformation and Its Impacts

Deepfake detection using convolutional vision transformers and convolutional neural networks

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Soudy, Ahmed Hatem, Sayed, Omnia, Tag-Elser, Hala, Ragab, Rewaa, Mohsen, Sohaila, Mostafa, Tarek, Abohany, Amr A., Slim, Salwa O.·2024·Neural Computing and Applications

DOI 10.1007/s00521-024-10181-7

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Abstract

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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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 [3]
The Abstract reports improved deepfake-detection performance, and Related work (section 2) describes a transformer-based model with high accuracy but substantial training-resource needs. Limitations and future work (section 6.5) also identifies high computational demands for training and inference. The copy does not establish that transformer-based models improve accuracy particularly on low-quality forgeries or that their computational demands are a significant barrier specifically to real-time deployment; its low-resolution result concerns a convolutional model. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Many deepfake detection models have surpassed 90% accuracy in binary classification benchmarks.
“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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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
ORCID: Soudy, Ahmed Hatem
Abohany, Amr A.
Venue: Neural Computing and Applications
Publisher: Springer Science and Business Media LLC
Published: 2024-08-08
Volume: 36
Issue: 31
Pages: 19759-19775
ISSN: 0941-0643 (ISSN-L)
1433-3058
DOI: 10.1007/s00521-024-10181-7
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
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
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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