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Cited by 297Digital Media Forensic DetectionFace recognition and analysisGenerative Adversarial Networks and Image Synthesis

DeepFake Detection for Human Face Images and Videos: A Survey

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Malik, Asad, Kuribayashi, Minoru, Abdullahi, Sani M., Khan, Ahmad Neyaz·2022·IEEE Access
DOI 10.1109/access.2022.3151186
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Abstract

Techniques for creating and manipulating multimedia information have progressed to the point where they can now ensure a high degree of realism. DeepFake is a generative deep learning algorithm that creates or modifies face features in a superrealistic form, in which it is difficult to distinguish between real and fake features. This technology has greatly advanced and promotes a wide range of applications in TV channels, video game industries, and cinema, such as improving visual effects in movies, as well as a variety of criminal activities, such as misinformation generation by mimicking famous people. To identify and classify DeepFakes, research in DeepFake detection using deep neural networks (DNNs) has attracted increased interest. Basically, DeepFake is the regenerated media that is obtained by injecting or replacing some information within the DNN model. In this survey, we will summarize the DeepFake detection methods in face images and videos on the basis of their results, performance, methodology used and detection type. We will review the existing types of DeepFake creation techniques and sort them into five major categories. Generally, DeepFake models are trained on DeepFake datasets and tested with experiments. Moreover, we will summarize the available DeepFake dataset trends, focusing on their improvements. Additionally, the issue of how DeepFake detection aims to generate a generalized DeepFake detection model will be analyzed. Finally, the challenges related to DeepFake creation and detection will be discussed. We hope that the knowledge encompassed in this survey will accelerate the use of deep learning in face image and video DeepFake detection methods.

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  • Existing surveys fail to examine how detectors perform when facing new data types, unseen manipulation techniques, or emerging generator architectures.

    “While surveys report impressive within-domain accuracies, they fail to examine how these same detectors perform when facing new data types, unseen manipulation techniques, or emerging generator architectures [5, 7, 9, 15].”

    From Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness (2026) · cites this paper as [9]

    The survey does examine performance on unseen manipulations: its DNN-based techniques section reports a detector tested on unseen DeepFakes and gives its performance on an in-the-wild video dataset. Its Challenges for DeepFake Detection section also discusses unknown manipulation types, contrary to the claim that surveys fail to examine this issue. The cited passages do not establish whether it evaluates emerging generator architectures. (LLM, conf=0.88)

    Checked by a reasoning model — may be wrong.

    Experimental — accuracy not yet measured

  • Current survey papers primarily focus on reviewing detection methods based on their accuracy performance within the same dataset (within-domain evaluation), which achieves high accuracy often exceeding 95%.

    “Current survey papers [5-9, 15, 15, 16] primarily focus on reviewing detection methods based on their accuracy performance within the same dataset (within-domain evaluation), which achieves high accuracy (often exceeding 95%).”

    From Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness (2026) · cites this paper as [9]

    The Introduction says this survey reviews DeepFake detection techniques, and the traditional-techniques section reports accuracy figures including 98% for DeepFake videos and 95% for Face2Face videos (page numbers unavailable). The paper does not establish that survey papers primarily focus on within-domain evaluation or that such evaluations often exceed 95% accuracy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Existing surveys of passive deepfake detection examine specific modalities in isolation.

    “While there are several surveys of passive DF detection that examine specific modalities in isolation [5-9], the field requires a holistic analysis that transcends these single approaches.”

    From Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness (2026) · cites this paper as [9]

  • Current survey papers provide extensive taxonomies and theoretical discussions but lack empirical evaluations that would enable researchers to understand the relative strengths and limitations of different approaches.

    “While current survey papers [5, 7, 9, 15] provide extensive taxonomies and theoretical discussions, they lack empirical evaluations that would enable researchers to understand the relative strengths and limitations of different approaches.”

    From Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness (2026) · cites this paper as [9]

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  89. ASAD MALIK (Member, IEEE) received the B.Sc. degree (Hons.) in computer application from Aligarh Muslim University, Aligarh, India, in 2012, the master's degree in computer applica- tion from Jamia Millia Islamia University, India, in 2015, and the Ph.D. degree from the School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China, in 2020. He is currently an Assistant Professor with the Department of Computer Science, Aligarh Muslim University. His research interests include multimedia forensics and security, image processing, information hiding, and deep learning.
  90. MINORU KURIBAYASHI (Senior Member, IEEE) received the B.E., M.E., and D.E. degrees from Kobe University, Japan, in 1999, 2001, and 2004, respectively. From 2002 to 2007, he was

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Authors
Malik, A., Kuribayashi, M., Abdullahi, S. M., & Khan, A. N.
Affiliations
  • Aligarh Muslim University (IN)
  • Okayama University (JP)
  • China Three Gorges University (CN)
  • Integral University (IN)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2022
Volume
10
Pages
18757-18775
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2022.3151186
OpenAlex
W4211008685
Cited by
297
References
33
Citations by year
2026: 81 · 2025: 94 · 2024: 83 · 2023: 30 · 2022: 9
FWCI
17.67
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Japan Society for the Promotion of Science (JSPS) KAKENHI (19K22846)
  • Japan Science and Technology agency Strategic International Collaborative Research Program (JPMJSC20C3)
  • Japan Science and Technology agency Core Research for Evolutional Science and Technology (JPMJCR20D3)
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
deepfake detection, face manipulation, face images, facial video, deep neural network, deepfake datasets, misinformation, generative adversarial networks, synthetic media

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