A comprehensive evaluation of feature-based AI techniques for deepfake detection
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Authors of most survey papers did not recognise the positive applications of "deepfake" technologies, while Seow et al. (2022) and Sandotra and Arora (2024) covered positive applications.
“While authors of most survey papers did not recognise the positive applications of “deepfake” technologies, Seow et al. (2022) and Sandotra and Arora (2024) covered positive applications, including entertainment, business, education, art, and medicine.”
From Deepfake: definitions, performance metrics and standards, datasets, and a meta-review (2024) · cites this paper as Sandotra and Arora, 2024
The citation lists haven’t been fetched yet
References 116 · Cited by 37
- Authors
- Sandotra, N., & Arora, B.
- Affiliations
- Central University of Jammu (IN)
- Corresponding author
- Sandotra, Neha
- Venue
- Neural Computing and Applications
- Published
- 2023-12-14
- Volume
- 36
- Issue
- 8
- Pages
- 3859-3887
- ISSN
- 0941-0643 (ISSN-L)
- 1433-3058
- OpenAlex
- W4389742134
- Cited by
- 37
- References
- 116
- Citations by year
- 2026: 9 · 2025: 20 · 2024: 8
- FWCI
- 3.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Neural Computing and Applications (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- deepfake detection, face manipulation, image generation, 3D morphable model, frequency-based features, temporal features, spatial characteristics, identity forgery
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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