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Deepfakes and beyond: A Survey of face manipulation and fake detection
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The free access to large-scale public databases, together with the fast progress of deep learning techniques, in particular Generative Adversarial Networks, have led to the generation of very realistic fake content with its corresponding implications towards society in this era of fake news. This survey provides a thorough review of techniques for manipulating face images including DeepFake methods, and methods to detect such manipulations. In particular, four types of facial manipulation are reviewed: i) entire face synthesis, ii) identity swap (DeepFakes), iii) attribute manipulation, and iv) expression swap. For each manipulation group, we provide details regarding manipulation techniques, existing public databases, and key benchmarks for technology evaluation of fake detection methods, including a summary of results from those evaluations. Among all the aspects discussed in the survey, we pay special attention to the latest generation of DeepFakes, highlighting its improvements and challenges for fake detection. In addition to the survey information, we also discuss open issues and future trends that should be considered to advance in the field.
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“These methods are typically very effective when the target video has been generated with a manipulation present in training, and almost useless otherwise [57, 58].”
From Audio-Visual Person-of-Interest DeepFake Detection (2023) · cites this paper as [57]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Recent works on adversarial perturbation attacks to fool DNN-based detectors make the deepfake detection task more difficult [157, 171–174].”
From Deep learning for deepfakes creation and detection: A survey (2022) · cites this paper as [157]
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“In fact, they highlight several instances where various fake detection techniques are already based on the synthesis of various information sources, such as steroids and DL features, spatial and spectral characteristics, or other combinations thereof.”
From Deepfakes: current and future trends (2024) · cites this paper as Tolosana et al. (2020)
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“They lack a comprehensive experimental evaluation to demonstrate the practical eicacy of state-of-the-art methods.”
From DEEPFAKER: A Unified Evaluation Platform for Facial Deepfake and Detection Models (2024) · cites this paper as [68]
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“This review di ff ers from previous reviews, which focus primarily on the performance of automated deepfake detectors [1].”
From Human Performance in Deepfake Detection: A Systematic Review (2025) · cites this paper as [1]
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“according to the surveys (Verdoliva 2020; Nguyen et al. 2019d; Tolosana et al. 2020; Lyu 2020), GANs are the mainstream baseline technique.”
From Countering Malicious DeepFakes: Survey, Battleground, and Horizon (2022) · cites this paper as Tolosana et al. 2020
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“according to the consensus in the DeepFake field (Mirsky and Lee 2021; Verdoliva 2020; Tolosana et al. 2020), there are mainly four categories based on their function: entire face synthesis, attribute manipulation, identity swap, and expression swap”
From Countering Malicious DeepFakes: Survey, Battleground, and Horizon (2022) · cites this paper as Tolosana et al. 2020
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“Despite significant accuracy improvements in existing detection models, their inherent opacity limits their practical application in high-risk areas such as forensic investigations and news verification.”
From From Black Boxes to Glass Boxes: Explainable AI for Trustworthy Deepfake Forensics (2025) · cites this paper as [1]
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“The work in Tolosana et al. (2020) implies that feature-level fusion approaches might offer a superior adaptability for deepfake detection in various contexts.”
From Deepfakes: current and future trends (2024) · cites this paper as Tolosana et al. (2020)
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“Existing studies and reviews on deepfake detection have predominantly concentrated on AI-based detection methods [1, 11].”
From Human Performance in Deepfake Detection: A Systematic Review (2025) · cites this paper as [1]
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“The Unsupervised Cross-Modal Inconsistencies [57] model obtained the second-best performance, with an AUC of 0 . 968 .”
From Contextual Cross-Modal Attention for Audio-Visual Deepfake Detection and Localization (2024) · cites this paper as [57]
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Citations
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- Raja, 2020, Workshop on Deepfakes and Presentation Attacks in Biometrics
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Metadata
- Authors
- Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J.
- Affiliations
- Universidad Autónoma de Madrid (ES)
- ORCID
- Morales, Aythami
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 64
- Pages
- 131-148
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W2998718632
- Semantic Scholar
- 5434f7c3b212a83d3f01949048ed1da114758cff
- Cited by
- 1,237
- References
- 213
- Citations by year
- 2026: 238 · 2025: 264 · 2024: 200 · 2023: 166 · 2022: 181 · 2021: 131 · 2020: 57
- FWCI
- 52.00
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Information Fusion (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Hispana (submittedVersion)
- Biblos-e Archivo (Universidad Autónoma de Madrid) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Federación Española de Enfermedades Raras (RTI2018-101248-B-I00)
- Ministerio de Economía y Competitividad
- Defense Advanced Research Projects Agency (MFC2018)
- 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
- deepfakes, face manipulation, generative adversarial networks, face synthesis, fake news, benchmark dataset
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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