Quality-based Artifact Modeling for Facial Deepfake Detection in Videos
Open accessAbstract
Facial deepfakes are becoming more and more realistic, to the point that it is often difficult for humans to distinguish between a fake and a real video. However, it is acknowledged that deepfakes contain artifacts at different levels; we hypothesize a connection between manipulations and visible or non-visible artifacts, especially where the subject’s movements are difficult to reproduce in detail. Accordingly, our approach relies on different quality measures, No-Reference (NR) and Full-Reference (FR), over the detected faces in the video. The measurements allow us to adopt a frame-by-frame approach to build an effective matrix-based representation of a video sequence. We show that the results obtained by this basic feature set for a neural network architecture constitute the first step that encourages the empowerment of this representation, aimed to extend our investigation to further deepfake classes. The FaceForensics++ dataset is chosen for experiments, which allows the evaluation of the proposed approach over different deepfake generation algorithms.
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Intra-face texture analysis examines inconsistencies across different regions of the same face.
“Intra-face texture analysis examines inconsistencies across different regions of the same face [28].”
From Deepfake Media Forensics: Status and Future Challenges (2025) · cites this paper as [28]
Section 3.3 (page unavailable) says the method compares left- and right-eye patches from the same frame, supporting the idea of examining differences within one face. Sections 3.1–3.2 describe quality measures applied to facial patches, but do not characterize the method as texture analysis across facial regions generally. (LLM, conf=0.83)
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Cited by
Sorted by citations
Deepfake Media Forensics: Status and Future Challenges
Amerini, I., Barni, M., Battiato, S., Bestagini, P., Boato, G., Bruni, V., Caldelli, R., De Natale, F., De Nicola, R., Guarnera, L., et al. (2025) · Journal of Imaging · doi:10.3390/jimaging11030073 · Cited by 113 · see citing works
Self-citation
- Authors
- Concas, S., La Cava, S. M., Casula, R., Orrù, G., Puglisi, G., & Marcialis, G. L.
- Affiliations
- University of Cagliari (IT)
- University of Cagliari,Cagliari,Italy
- Venue
- 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2024-06-17
- Pages
- 3845-3854
- OpenAlex
- W4402915964
- Cited by
- 12
- References
- 37
- Citations by year
- 2026: 4 · 2025: 8
- FWCI
- 2.49
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- UNICA IRIS Institutional Research Information System (University of Cagliari) (submittedVersion, other-oa)
- Funders
- European Commission (PRIN 2022 PNRR, P2022K39K8, PE00000014, PRIN-2022)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- video forensics, image quality metrics, no-reference image quality assessment, full-reference quality assessment, face manipulation, FaceForensics++, neural network features
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