Deepfake Video Detection Based on MesoNet with Preprocessing Module
Open accessAbstract
With the development of computer hardware and deep learning, face manipulation videos represented by Deepfake have been widely spread on social media. From the perspective of symmetry, many forensics methods have been raised, while most detection performance might drop under compression attacks. To solve this robustness issue, this paper proposes a Deepfake video detection method based on MesoNet with preprocessing module. First, the preprocessing module is established to preprocess the cropped face images, which increases the discrimination among multi-color channels. Next, the preprocessed images are fed into the classic MesoNet. The detection performance of proposed method is verified on two datasets; the AUC on FaceForensics++ can reach 0.974, and it can reach 0.943 on Celeb-DF which is better than the current methods. More importantly, even in the case of heavy compression, the detection rate can still be more than 88%.
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Xia et al.'s MesoNet method using image texture achieved an AUC of 84.30 on the DFD dataset.
“| | | | AUC = 84.30 | DFD |”
From Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions (2023) · cites this paper as [112]
Xia et al.'s MesoNet method using image texture achieved an accuracy of 76.31 on the FF++(LQ) dataset.
“| | | | ACC = 76.31 | FF++(LQ) |”
From Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions (2023) · cites this paper as [112]
Xia et al.'s MesoNet method using image texture achieved an accuracy of 94.90 and an AUC of 94.30 on the Celeb-DF dataset.
“| Xia et al. [112] | Image texture | MesoNet | ACC = 94.90 AUC = 94.30 | Celeb-DF |”
From Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions (2023) · cites this paper as [112]
Xia et al.'s MesoNet method using image texture achieved an AUC of 96.50 on the UADFV dataset.
“| | | | AUC = 96.50 | UADFV |”
From Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions (2023) · cites this paper as [112]
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Cited by
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Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions
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- Authors
- Xia, Z., Qiao, T., Xu, M., Wu, X., Han, L., & Chen, Y.
- Affiliations
- Hangzhou Dianzi University (CN)
- Hangzhou Vocational and Technical College (CN)
- School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310005, China
- Corresponding author
- Qiao, Tong
- Xu, Ming
- Venue
- Symmetry
- Publisher
- MDPI AG
- Published
- 2022-05-05
- Volume
- 14
- Issue
- 5
- Pages
- 939
- ISSN
- 2073-8994 (ISSN-L)
- OpenAlex
- W4229019234
- Cited by
- 56
- References
- 37
- Citations by year
- 2026: 13 · 2025: 15 · 2024: 17 · 2023: 6 · 2022: 5
- FWCI
- 3.52
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Symmetry (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Fundamental Research Funds for the Provincial Universities of Zhejiang
- 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
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- deepfake detection, face manipulation, mesonets, compression attacks, robustness, face forensics, FaceForensics++, Celeb-DF
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