The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Abbreviated View of Deepfake Videos Detection Techniques
Abstract
In the era of technological advances and a qualitative breakthrough in the artificial intelligence field and deep neural networks, a new age of hyper-realistic digital videos forgery called DeepFake has been born, with that new technology, it is difficult to distinguish between real videos and fake ones which are uploaded daily on various websites across the Internet. Many open-source DeepFake creation methods have risen, leading to a growing number of synthesized media clips over the internet. There are many efficient fast methods and techniques which have been designed to detect and spot such phenomenon. Background Comparison, Temporal Pattern Analysis, Eye blinking, Facial Artifacts, Mesoscopic Analysis, and Pose Estimation are some of those techniques. Some of these approaches designed to detect and identify the video forgery without any prior enlightenment concerning the videos under analysis. The primary scope of this study is to provide an abbreviate review of these methodologies of a method-comparison study that has been presented to assist the researcher's evaluation of such studies.
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“The authors of many surveys (e.g., Lyu, 2020; Younus and Hasan, 2020; Deshmukh and Wankhade, 2021; Masood et al., 2023; Sandotra and Arora, 2024), argued that better (higher-quality, more up-to-date, and more standard) deepfake datasets are needed to develop more effective deepfake detection methods.”
From Deepfake: definitions, performance metrics and standards, datasets, and a meta-review (2024) · cites this paper as Younus and Hasan, 2020
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Li, Y., Chang, M.-C., & Lyu, S. (2018) · 2018 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs.2018.8630787 · Cited by 1,089
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Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions
Naitali, A., Ridouani, M., Salahdine, F., & Kaabouch, N. (2023) · Computers · doi:10.3390/computers12100216 · Cited by 117
Deepfake: definitions, performance metrics and standards, datasets, and a meta-review
Altuncu, E., Franqueira, V. N. L., & Li, S. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1400024 · Cited by 134
Metadata
- Authors
- Younus, M. A., & Hasan, T. M.
- Affiliations
- University of Diyala (IQ)
- ORCID
- Hasan, Taha M.
- Venue
- 2020 6th International Engineering Conference “Sustainable Technology and Development" (IEC)
- Publisher
- IEEE
- Published
- 2020-02
- Pages
- 115-120
- OpenAlex
- W3037969213
- Cited by
- 17
- References
- 3
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2023: 5 · 2022: 2 · 2021: 4
- FWCI
- 0.81
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Engineering Conference "Sustainable Technology and Development" (IEC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Mathematics › Statistics and Probability › Benford’s Law and Fraud Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- deepfake detection, video forgery, synthetic media, temporal pattern analysis, blinking, mesoscopic analysis, pose estimation
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- Younus, M. A., & Hasan, T. M. (2020). Abbreviated View of Deepfake Videos Detection Techniques. 2020 6th International Engineering Conference “Sustainable Technology and Development" (IEC). https://doi.org/10.1109/iec49899.2020.9122916
