GAN-Generated Faces Detection: A Survey and New Perspectives
Open accessAbstract
Generative Adversarial Networks (GAN) have led to the generation of very realistic face images, which have been used in fake social media accounts and other disinformation matters that can generate profound impacts. Therefore, the corresponding GAN-face detection techniques are under active development that can examine and expose such fake faces. In this work, we aim to provide a comprehensive review of recent progress in GAN-face detection. We focus on methods that can detect face images that are generated or synthesized from GAN models. We classify the existing detection works into four categories: (1) deep learning-based, (2) physical-based, (3) physiological-based methods, and (4) evaluation and comparison against human visual performance. For each category, we summarize the key ideas and connect them with method implementations. We also discuss open problems and suggest future research directions.
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Earlier surveys predominantly address research published up to 2023, missing significant advancements in recent years.
“Earlier surveys [16], [17], [18], [19] predominantly address research published up to 2023, missing significant advancements in recent years.”
From Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges (2024) · cites this paper as [17]
The 2023 source describes itself in the Conclusion as an up-to-date review of GAN-face detection methods, supporting its identification as an earlier survey of research available by then. It cannot establish the citing sentence’s further claim that significant advancements occurred afterward and were missed by earlier surveys. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPrevious surveys limit their discussions primarily to visual manipulations.
“While previous surveys [4], [16], [17] limit their discussions primarily to visual manipulations, we systematically explore recent developments in detecting multi-modal forgeries that simultaneously involve audio, visual, and textual modalities.”
From Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges (2024) · cites this paper as [17]
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Semantic facial features and expression manipulation using multi-level IC-DGAN framework
Abbas, F., & Taeihagh, A. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650839
Paleczek, A., & Rydosz, A. M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-36370-2.00005-0
FAQIVS: Face Query-based Interactive Video Synopsis*
Chandrasekaran, N., & Yogameena, B. (2025) · Automatika · doi:10.1080/00051144.2025.2459987
Identity-Preserved Facial Diversity With Gans and Recognition Via Fewshot Approach
Bhattacharjee, S., & Sultanova, N. (2024) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese63988.2024.10912019
Analysis of Deep Learning Frameworks for Occluded Face Recognition
Yan, Z. (2025) · Applied and Computational Engineering · doi:10.54254/2755-2721/2025.gl23384
Face Detection in Computer Vision: A Comprehensive Review
Kou, Y., Chen, J., Ke, H., & Xiong, J. (2025) · International Conference on Advanced Algorithms and Control Engineering (ICAACE) · doi:10.1109/icaace65325.2025.11019031
Data-Driven Facial Image Synthesis from Text Descriptions with Deep Fusion GANs
Annaiah, N. A. K. J., & Mahanty, M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-3250-3_37
Hybrid CNN and Forensic Approach for Detecting AI-Generated Human Faces
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Real-Time Binary Classification of Real and Fake Faces via MobileNetV2 and Transfer Learning
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Promoting Racial Fairness in Deepfake Face Detection
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Bias Analysis for Synthetic Face Detection: A Case Study of the Impact of Facial Attributes
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Real-Time AI-Generated Face Detection: A Deep Learning Framework using GAN
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Deepfake Detection Based on Multimodal Feature Fusion
Hong, T., Chien, H., Su, J.-H., Chen, W.-C., & Li, S.-M. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-22592-4_37
Detection of AI Generated Faces and Real Human Faces Using Deep Learning-Based Transfer Learning
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- Authors
- Wang, X., Guo, H., Hu, S., Chang, M.-C., & Lyu, S.
- Affiliations
- University at Albany, State University of New York (US)
- University at Buffalo, State University of New York (US)
- Indiana University – Purdue University Indianapolis (US)
- Venue
- Frontiers in Artificial Intelligence and Applications
- Publisher
- IOS Press
- Published
- 2023-09-28
- ISSN
- 0922-6389 (ISSN-L)
- 1879-8314
- OpenAlex
- W4226146350
- Cited by
- 98
- References
- 98
- Citations by year
- 2026: 3 · 2025: 27 · 2024: 31 · 2023: 26 · 2022: 11
- FWCI
- 26.28
- Type
- book chapter
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Frontiers in artificial intelligence and applications (publishedVersion, cc-by-nc)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (2153112)
- 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
- synthetic face detection, deepfakes, disinformation, synthetic image detection, fake social media accounts
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