iCaps-Dfake: An Integrated Capsule-Based Model for Deepfake Image and Video Detection
Open accessAbstract
Fake media is spreading like wildfire all over the internet as a result of the great advancement in deepfake creation tools and the huge interest researchers and corporations are showing to explore its limits. Now anyone can create manipulated unethical media forensics, defame, humiliate others or even scam them out of their money with a click of a button. In this research a new deepfake detection approach, iCaps-Dfake, is proposed that competes with state-of-the-art techniques of deepfake video detection and addresses their low generalization problem. Two feature extraction methods are combined, texture-based Local Binary Patterns (LBP) and Convolutional Neural Networks (CNN) based modified High-Resolution Network (HRNet), along with an application of capsule neural networks (CapsNets) implementing a concurrent routing technique. Experiments have been conducted on large benchmark datasets to evaluate the performance of the proposed model. Several performance metrics are applied and experimental results are analyzed. The proposed model was primarily trained and tested on the DeepFakeDetectionChallenge-Preview (DFDC-P) dataset then tested on Celeb-DF to examine its generalization capability. Experiments achieved an Area-Under Curve (AUC) score improvement of 20.25% over state-of-the-art models.
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The use of YOLO-v3 as a face detector in Khalil et al. (2021)'s model resulted in a significant decrease in false-positive predictions, eliminating the requirement for additional preprocessing steps.
“The use of You Only Look Once (YOLO-v3) as a face detector resulted in a significant decrease in falsepositive predictions, eliminating the requirement for additional preprocessing steps.”
From Deepfake detection using deep learning methods: A systematic and comprehensive review (2024) · cites this paper as Khalil et al. (2021)
The Discussion (Section 5) says YOLO-v3 greatly reduced false-positive face detections, but says this reduced the need for further preprocessing—not that it eliminated it. The Preprocessing section explicitly describes normalization, cropping, and augmentation of detected faces. (LLM, conf=0.90)
Experimental — accuracy not yet measuredKhalil et al. (2021)'s approach combines texture feature extraction with an HRNet-based strategy for training a capsule network to enhance classification accuracy.
“Khalil et al. (2021) presented a deepfake detection approach that combines texture feature extraction with a High-Resolution Network (HRNet)-based strategy for training a capsule network to enhance classification accuracy.”
From Deepfake detection using deep learning methods: A systematic and comprehensive review (2024) · cites this paper as Khalil et al. (2021)
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Deepfake Creation and Detection using Ensemble Deep Learning Models
Rao, S., Shelke, N. A., Goel, A., & Bansal, H. (2022) · International Conference on Contemporary Computing · doi:10.1145/3549206.3549263 · Cited by 5 · see citing works
SiamLBP: Exploiting Texture Discrepancies for Deepfake Detection
Kingra, S., Aggarwal, N., & Kaur, N. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-0085-5_36 · Cited by 5 · see citing works
Feature fusion capsule network for cow face recognition
Xu, F., Pan, X., & Gao, J. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.31.6.061817 · Cited by 4 · see citing works
A Comparative Analysis of Deepfake Detection Methods Using Overlapping Multiple Dynamic Images
Purevsuren, E., Sato, J., & Akashi, T. (2025) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.24258 · Cited by 3 · see citing works
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Face Swapping Consistency Transfer with Neural Identity Carrier
Liu, K., Wang, P., Zhou, W., Zhang, Z., Ge, Y., Liu, H., Zhang, W., & Yu, N. (2021) · Future Internet · doi:10.3390/fi13110298 · Cited by 3 · see citing works
Capsule Networks and LSTM Models for Robust Deepfake Detection in Audio and Video
Abisha, M. B., Kathrine, G. J. W., & Kushmitha, S. (2024) · International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS) · doi:10.1109/icicnis64247.2024.10823109 · Cited by 2 · see citing works
Abed, B. N. A.-D., Karimpour, J., & Mahan, F. (2025) · Egyptian Informatics Journal · doi:10.1016/j.eij.2025.100748 · Cited by 2 · see citing works
RetractedDeepfake Face Identification Using Deep Learning Ensemble Methods
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DeepStream-X: A Two-Stream Deepfake Detection Framework using Spatiotemporal and Frequency Features
Emara, N. M., & Elagamy, M. N. (2024) · International Conference on Computer Theory and Applications (ICCTA) · doi:10.1109/iccta64612.2024.10974904 · Cited by 2 · see citing works
Detecting Fake Faces with AI: A Deep Neural Network Improvement Project
Kumar, L. R., Yadav, R. K., & S.Yuvaraj (2023) · International Journal of Scientific Methods in Engineering and Management · doi:10.58599/ijsmem.2023.1405 · Cited by 2 · see citing works
Detection of Facial Deep Fake Images using CNN Architecture
Telaprolu, G., Tallapally, S., Thakkalapelly, S. R., Vattam, M., & Shereesha, M. (2024) · International Conference on Communication, Computing and Energy Efficient Technologies (I3CEET) · doi:10.1109/i3ceet61722.2024.10993881 · Cited by 1 · see citing works
Ethical and Legal Concerns of Deepfake Technology in Biomedical Imaging: A Comprehensive Survey
Rajpar, S. A., Chen, H., & Ashiru, A. (2026) · Annals of Biomedical Engineering · doi:10.1007/s10439-026-04128-3 · Cited by 1 · see citing works
Recent Trends in Deepfake Detection
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Spatio-Temporal Feature Pyramid Network for Deepfake DetectionPreprint
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MAREPVGG: Multi-attention RepPVGG to Facefake Detection
Huang, Z., Yang, R., Lan, R., Pang, C., & Luo, X. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47665-5_19
Detecting Deepfake Videos Dilemma Revisited: Finding Gaps and Shortcomings
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GAN-Deepfakes Detection Using ELA and Deep Learning
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Unified Deepfake Detection System for Image, Video and Audio Media
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A Spatial Adaptive Transformer Network for Sequential Deepfake Detection
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A Novel CNN-Swish-RNN Approach for DeepFake Detection
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DeepFake Analysis : A Review of Recent Studies, Detection Methods and Trends in Deepfake Detection
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A Unified Generative AI System for Synthetic Image Creation and Detection
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Detecting Fake Images Generated by Artificial Intelligence Using Deep Learning Approach
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Comprehensive Analysis of Deepfake Generation and Detection Techniques
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A dual-domain spatio-temporal and frequency framework for robust deepfake detection
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PAttSBiL: an efficient deep fake video detection using integrated deep learning methodologies
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Deepfake anomaly detection using kernel density estimation based convolutional autoencoder
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- Authors
- Khalil, S. S., Youssef, S. M., & Saleh, S. N.
- Affiliations
- Arab Academy for Science, Technology, and Maritime Transport (EG)
- Corresponding author
- Saleh, Sherine Nagy
- Venue
- Future Internet
- Publisher
- MDPI AG
- Published
- 2021-04-05
- Volume
- 13
- Issue
- 4
- Pages
- 93
- ISSN
- 1999-5903 (ISSN-L)
- OpenAlex
- W3152384838
- Cited by
- 67
- References
- 56
- Citations by year
- 2026: 11 · 2025: 17 · 2024: 8 · 2023: 16 · 2022: 10 · 2021: 5
- FWCI
- 3.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Future Internet (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- 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
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- deepfake detection, capsule networks, local binary pattern, HRNet, fake media, generalization, Celeb-DF
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