A Comparative Study of Deepfake Video Detection Method
Abstract
Deepfake technology allows humans to manipulate images and videos using deep learning technology. The results from deepfakes are very difficult to distinguish using ordinary vision. Many algorithms are built to detect deepfake content in images and videos. There are several approaches in deepfake detection, including a visual feature-based approach, a local feature-based approach, a deep feature-based approach and a temporal feature-based approach. The main challenge in developing deepfake detection algorithms is the variety of existing deepfake models in both images and videos. Another challenge is that deepfake technology is still evolving, making deepfake images and videos look more realistic and harder to detect.
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The presence of visible or nonverbal clues for evaluating information has become diminished or nonexistent due to recent technological breakthroughs, heightening marketplace deception to unprecedented levels.
“The presence of visible or nonverbal clues (e.g., facial expressions, eye contact) for evaluating a piece of information has become diminished or even nonexistent as a result of recent technological breakthroughs (Maksutov et al., 2020; Ramadhani & Munir, 2020; Tong et al., 2020), thus heightening the degree of marketplace deception to unprecedented levels (Ho et al., 2016; Taylor, 2021).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Ramadhani & Munir, 2020
Technology-based protections against deepfake deceptions come with specific limitations owing to the fast pace of improvement in generating synthetic media.
“However, it must be noted that these technology-based protections against deepfake deceptions come with specific limitations owing to the fast pace of improvement in generating synthetic media (Johnson & Diakopoulos, 2021; Ramadhani & Munir, 2020).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Ramadhani & Munir, 2020
In a machine-versus-machine scenario, the whole detection method becomes obsolete.
“In this machine-versus-machine scenario, the whole detection method then becomes obsolete (Maksutov et al., 2020; Ramadhani & Munir, 2020).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Ramadhani & Munir, 2020
Cites
Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics
Li, Y., Yang, X., Sun, P., Qi, H., & Lyu, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00327 · Cited by 1,846 · cited 3× · see citing works
“The following are some of the datasets commonly used in research on deepfake detection in images and videos[36].”
BackgroundMethodInfluentialDeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection
Jiang, L., Li, R., Wu, W., Qian, C., & Loy, C. C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00296 · Cited by 599 · see citing works
Deepfake Video Detection through Optical Flow Based CNN
Amerini, I., Galteri, L., Caldelli, R., & Del Bimbo, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00152 · Cited by 424 · cited 2× · see citing works
“Amerini et al. [8] using a Convolutional Neural Network (CNN) with Optical Flow feature.”
MethodDetection of GAN-Generated Fake Images over Social Networks
Marra, F., Gragnaniello, D., Cozzolino, D., & Verdoliva, L. (2018) · 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr.2018.00084 · Cited by 399 · see citing works
Deep fake image detection based on pairwise learning
Hsu, C.-C., Zhuang, Y.-X., & Lee, C.-Y. (2020) · Applied Sciences · doi:10.3390/app10010370 · Cited by 304 · see citing works
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Deepfakes: Deceptions, mitigations, and opportunities
Mustak, M., Salminen, J., Mäntymäki, M., Rahman, A., & Dwivedi, Y. K. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2022.113368 · Cited by 209 · see citing works
Deepfake generation and detection: Case study and challenges
Patel, Y., Tanwar, S., Gupta, R., Bhattacharya, P., Davidson, I. E., Nyameko, R., Aluvala, S., & Vimal, V. (2023) · IEEE Access · doi:10.1109/access.2023.3342107 · Cited by 186 · see citing works
- Authors
- Ramadhani, K. N., & Munir, R.
- Affiliations
- Bandung Institute of Technology (ID)
- Venue
- 2020 3rd International Conference on Information and Communications Technology (ICOIACT)
- Publisher
- IEEE
- Published
- 2020-11-24
- Pages
- 394-399
- OpenAlex
- W3128292651
- Cited by
- 42
- References
- 5
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 20 · 2023: 7 · 2022: 4
- FWCI
- 0.85
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Information and Communications Technology (ICOIACT) (publishedVersion)
- 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, deepfake videos, image editing
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