A comprehensive overview of Deepfake: generation, detection, datasets, and opportunities
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 Seow et al., 2022
The citation lists haven’t been fetched yet
References 166 · Cited by 149
- Authors
- Seow, J. W., Lim, M. K., Phan, R. C. W., & Liu, J. K.
- Affiliations
- Monash University Malaysia (MY)
- Monash University (AU)
- Corresponding author
- Seow, Jia Wen
- Lim, Mei Kuan
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 513
- Pages
- 351-371
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W4297498506
- Cited by
- 149
- References
- 166
- Citations by year
- 2026: 26 · 2025: 56 · 2024: 49 · 2023: 18
- FWCI
- 10.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Neurocomputing (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
- deepfakes, deepfake generation, deepfake detection, deepfake datasets, misinformation, public trust, benchmarking
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.
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