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Cited by 129Digital Media Forensic DetectionFace recognition and analysisMisinformation and Its Impacts

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 access
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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Research works printed but not linked: 9

  1. Al-Hussein M, Venkataraman S, Jawahar C (2020) Deepfake detection for video: an open source challenge. arXiv preprint arXiv:2006.06058 · arXiv
  2. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 · arXiv
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  4. Kfir O, Kfir A, Friedman A (2019) Generative adversarial attacks in social media. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & sata mining, pp 2558–2566
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  8. Zhao M, Qin W, Yu W, Li X, Gao B (2020) Adversarial examples: attacks on deepfake detection models. IEEE Trans Multimed 23(8):1295–1305
  9. Zhou XY, Jiang WJ, Yu ZH (2020) Deepfakes detection using deep learning methods: a survey. Int J Imaging Robot 14(3):196–211

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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
ORCID
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
Licence
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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