Deepfakes and beyond: A Survey of face manipulation and fake detection

Tolosana, Ruben; Vera-Rodriguez, Ruben; Fierrez, Julian; Morales, Aythami; Ortega-Garcia, Javier

doi:10.1016/j.inffus.2020.06.014

Cited by 1,237Digital Media Forensic DetectionFace recognition and analysisGenerative Adversarial Networks and Image Synthesis

Deepfakes and beyond: A Survey of face manipulation and fake detection

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Tolosana, Ruben, Vera-Rodriguez, Ruben, Fierrez, Julian, Morales, Aythami, Ortega-Garcia, Javier·2020·Information Fusion

DOI 10.1016/j.inffus.2020.06.014

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Abstract

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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Most supervised deepfake detection methods are very effective when the target video has been generated with a manipulation present in training, and almost useless otherwise.
“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]
The survey says detectors achieve very low error rates when evaluated under the conditions in which they were trained (Audio-to-Video and Text-to-Video section). Its Manipulation Detection section reports poor generalisation to unseen databases, with most approaches scoring below 60% AUC on second-generation DeepFake databases. That supports a substantial performance drop, but does not establish that detectors are almost useless whenever the manipulation was absent from training. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Recent works on adversarial perturbation attacks to fool DNN-based detectors make the deepfake detection task more difficult.
“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]
The Manipulation Detection section (page unavailable) reports that simple image perturbations can undermine a deep-learning detector and that altered synthetic images can spoof fake detectors. The survey thus supports the claim that such attacks make detection harder, but the passages do not specifically establish recent work on adversarial perturbations designed to fool DNN-based detectors. (LLM, conf=0.82)
Experimental — accuracy not yet measured
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.
“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)
The Audio-to-Video and Text-to-Video section says that fake detectors combine different information sources, including spatial and spectral features. It names steganalysis and deep-learning features—not “steroids” and deep-learning features—as the other example. No page number is provided in the supplied copy. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Existing surveys lack a comprehensive experimental evaluation to demonstrate the practical efficacy of state-of-the-art methods.
“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]
In the introductory discussion of other surveys (page not available), the paper says its review adds a more detailed account of detection benchmarks and their results. That supports a narrower claim about the detail of earlier surveys, but it does not establish that existing surveys generally lack a comprehensive experimental evaluation of state-of-the-art methods. (LLM, conf=0.76)
Experimental — accuracy not yet measured
This review differs from previous reviews, which focus primarily on the performance of automated deepfake detectors.
“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]
The Manipulation Detection section compares automated detectors and their performance, so the cited review does cover that subject. But the introduction says it also reviews manipulation techniques and public databases in detail; it does not establish that previous reviews focus primarily on detector performance. Our copy gives no page numbers for these passages. (LLM, conf=0.79)
Experimental — accuracy not yet measured

GANs are the mainstream baseline technique for entire face synthesis.
“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
According to consensus in the DeepFake field, DeepFake generation methods mainly fall into four categories based on function: entire face synthesis, attribute manipulation, identity swap, and expression swap.
“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
Existing deepfake detection models have achieved significant accuracy improvements.
“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]
The work in Tolosana et al. (2020) implies that feature-level fusion approaches might offer superior adaptability for deepfake detection in various contexts.
“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)
Existing studies and reviews on deepfake detection have predominantly concentrated on AI-based detection methods.
“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]
The Unsupervised Cross-Modal Inconsistencies model obtained the second-best performance on FakeAVCeleb with an AUC of 0.968.
“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]
The Manipulation Detection section compares detection methods and AUCs on other datasets, including Celeb-DF, but does not report a result for FakeAVCeleb. Searches for “Unsupervised Cross-Modal Inconsistencies,” “FakeAVCeleb,” and “0.968” found no passage establishing the model’s AUC or second-best ranking. (LLM, conf=0.76)
Experimental — accuracy not yet measured

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Retracted
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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)
DOI: 10.1016/j.inffus.2020.06.014
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
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

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Deepfakes and beyond: A Survey of face manipulation and fake detection

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