Generative Adversarial Networks for Visual Content: A Comprehensive Review of Image and Video Synthesis, Challenges, and Ethical Implications
Article Sidebar
Main Article Content
Abstract
Generative adversarial networks (GANs) represent a ground-breaking advancement in artificial intelligence, revolutionizing data generation and content creation. This systematic review critically analyses literature from 2019-2024, sourced from major databases like IEEE Xplore and Scopus, to go beyond descriptive surveys and provide a synthesized, critical evaluation. We identify a pivotal gap in the existing literature: a frequent disconnect between reporting technical advancements and examining their profound ethical and societal implications. Our analysis yields several integrated insights. First, while GAN applications predominantly excel in visual content generation with architectures like StyleGAN and BigGAN achieving remarkable progress in photorealistic image and video synthesis their efficacy is highly application-specific, revealing significant trade-offs in stability and diversity in domains such as medical imaging and real-time video processing. Second, persistent core challenges like training instability and mode collapse continue to drive architectural innovations; however, we critically examine how solutions like Wasserstein loss or minibatch discrimination demonstrate varying effectiveness and limitations across different tasks and datasets. Third, and most critically, we argue that ethical concerns, particularly regarding deepfakes and data bias, are not peripheral issues but are intrinsically linked to architectural choices and the quality of training data. The key contribution of this review is its novel, integrated framework for evaluating GANs, which concurrently assesses architectural efficacy, application-specific maturity, and associated ethical risks. This holistic and critical synthesis provides researchers with a nuanced reference and outlines clear, responsible directions for future GAN development, emphasizing the need for models that are not only more powerful but also more robust, fair, and accountable.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
P. Audichya, D.G. Amritpal Singh, Generative Adversarial Networks: Models and Techniques - A Review, in: IEEE World Conference on Applied Intelligence and Computing, 2022.
A.O. Arik, B.O. Kose, Data Guardians: Empowering Cybersecurity With Generative Adversarial Networks, in: Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs), IGI Global, 2024, pp. 102–123.
M.E. Laino, et al., Generative adversarial networks in brain imaging: A narrative review, Journal of Imaging 8 (4) (2022) 83.
A. Gunasekaran, Generative Adversarial Networks: A Brief History and Overview, Journal of Student Research 12 (1) (2023).
A. Kammoun, et al., Generative adversarial networks for face generation: A survey, ACM Computing Surveys 55 (5) (2022) 1–37.
A.A. Nayak, P. Venugopala, B. Ashwini, A Systematic Review on Generative Adversarial Network (GAN): Challenges and Future Directions, Archives of Computational Methods in Engineering (2024) 1–34.
P. Cobelli, S. Nesmachnow, J. Toutouh, A comparison of Generative Adversarial Networks for image super-resolution, in: 2022 IEEE Latin American Conference on Computational Intelligence (LA-CCI), IEEE, 2022.
T. Karras, S. Laine, T. Aila, A style-based generator architecture for generative adversarial networks, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.
I. Goodfellow, et al., Generative adversarial networks, Communications of the ACM 63 (11) (2020) 139–144.
Z. Liu, Y. Li, T. Yuan, B. Zeng, Recent Advances of Generative Adversarial Networks, in: IEEE 2nd International Conference on Electronic Technology, Communication and Information (ICETCI), 2022.
L. Xu, et al., TGAN-AD: Transformer-based GAN for anomaly detection of time series data, Applied Sciences 12 (16) (2022) 8085.
M. Megahed, A. Mohammed, A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges, Wiley Interdisciplinary Reviews: Computational Statistics 16 (1) (2024) e1629.
J. Gui, et al., A review on generative adversarial networks: Algorithms, theory, and applications, IEEE Transactions on Knowledge and Data Engineering 35 (4) (2021) 3313–3332.
Y. Zhang, Analysis of learning mechanism of generative adversarial network, in: 2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE), IEEE, 2022.
R. Kanniga Devi, M. Asha Jerlin, Generative Adversarial Networks: A Comprehensive Review, in: Generative Artificial Intelligence (AI) Approaches for Industrial Applications, 2025, pp. 69–92.
C. Shen, et al., Training generative adversarial networks in one stage, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021.
F. Ramzan, et al., Generative adversarial networks for synthetic data generation in finance: Evaluating statistical similarities and quality assessment, AI 5 (2) (2024) 667–685.
Z. Wang, Q. She, T. Ward, Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy, ACM Computing Surveys 54 (2021) 1–38.
P. Sharma, M. Kumar, H.K. Sharma, GAN-CNN ensemble: A robust deepfake detection model of social media images using minimized catastrophic forgetting and generative replay technique, Procedia Computer Science 235 (2024) 948–960.
H. Alqahtani, M. Kavakli-Thorne, G. Kumar, Applications of generative adversarial networks (GANs): An updated review, Archives of Computational Methods in Engineering 28 (2021) 525–552.
P. Sharma, A Brief History of Generative Adversarial Networks, 2022.
S. Bengesi, et al., Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers, IEEE Access (2024).
J. Jenkins, K. Roy, Exploring deep convolutional generative adversarial networks (DCGAN) in biometric systems: A survey study, Discover Artificial Intelligence 4 (1) (2024) 42.
Y. Wang, A mathematical introduction to generative adversarial nets (GAN), arXiv preprint arXiv:2009.00169, 2020.
T. Karras, et al., Analyzing and improving the image quality of StyleGAN, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.
J. Donahue, K. Simonyan, Large scale adversarial representation learning, Advances in Neural Information Processing Systems 32 (2019).
H.K. Kılınç, Ö.F. Keçecioğlu, Generative Artificial Intelligence: A Historical and Future Perspective, Academic Platform Journal of Engineering and Smart Systems 12 (2) (2024) 47–58.
S.M. Razak, B. Jafarpour, History matching with generative adversarial networks, in: ECMOR XVII, European Association of Geoscientists & Engineers, 2020.
P. Purwono, et al., Understanding Generative Adversarial Networks (GANs): A Review, Control Systems and Optimization Letters 3 (1) (2025) 36–45.
R. Bhat, R. Nanjundegowda, A Review on Comparative Analysis of Generative Adversarial Networks’ Architectures and Applications, Journal of Robotics and Control (JRC) 6 (1) (2025) 53–64.
Y. Li, Q. Zhang, The Analysis of Aesthetic Preferences for Cultural and Creative Design Trends under Artificial Intelligence, IEEE Access (2024).
Z. Wang, Q. She, T.E. Ward, Generative adversarial networks in computer vision: A survey and taxonomy, ACM Computing Surveys (CSUR) 54 (2) (2021) 1–38.
Z. Li, et al., A systematic survey of regularization and normalization in GANs, ACM Computing Surveys 55 (11) (2023) 1–37.
N. Pandey, A. Savakis, Poly-GAN: Multi-conditioned GAN for fashion synthesis, Neurocomputing 414 (2020) 356–364.
T. DeVries, et al., On the evaluation of conditional GANs, arXiv preprint arXiv:1907.08175, 2019.
P. Saxena, et al., Semantic image completion and enhancement using GANs, in: High Performance Vision Intelligence: Recent Advances, 2020, pp. 151–170.
Y. Zhang, et al., SCGAN: Generative Adversarial Networks of Skip Connection for Face Image Inpainting, in: 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), IEEE, 2022.
R. Fang, Infrared and Visible Image Fusion Using Generative Adversarial Network with Feature Complement Block, in: 2023 6th International Conference on Electronics Technology (ICET), IEEE, 2023.
R. Ashwini, R. Vani, S. Suvitha, Enhancing the Resolution of Ancient Artworks using Generative Adversarial Networks, in: 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA), IEEE, 2022.
T. Kaneko, et al., StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion, arXiv preprint arXiv:1907.12279, 2019.
R. Sarode, et al., Image Super Resolution Using Enhanced Super Resolution Generative Adversarial Network, ITM Web of Conferences, EDP Sciences, 2022.
H. Zhang, et al., Self-Attention Generative Adversarial Networks, in: International Conference on Machine Learning, PMLR, 2019.
A. Singh, T.P., Creating Objects for Metaverse using GANs and Autoencoders, International Research Journal of Engineering and Technology (IRJET) 9 (6) (2022).
S. Park, et al., A Lightweight Spiking Generative Adversarial Network for IoT Applications, in: 2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC), IEEE, 2023.
V.-H. Phan, M.-Q. Ha, T.-H. Do, A Novel License Plate Image Reconstruction System Using Generative Adversarial Network, in: 2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), IEEE, 2022.
M. Hou, et al., Extreme Low-Resolution Activity Recognition Using a Super-Resolution-Oriented Generative Adversarial Network, Micromachines 12 (6) (2021) 670.
K. Al Butainy, M. Felemban, H. Luqman, Realistic Face Masks Generation Using Generative Adversarial Networks, in: 2022 14th International Conference on Computational Intelligence and Communication Networks (CICN), IEEE, 2022.
M. Qian, B. Chen, W. Chen, Context-Based Image Inpainting with Improved Generative Adversarial Networks, in: 2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE), IEEE, 2023.
L. Shen, Application of a Deep Generative Model for Diversified Video Subtitles Based on Generative Adversarial Networks, in: 2023 3rd Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS), 2023.
E.R. Chan, et al., Efficient Geometry-Aware 3D Generative Adversarial Networks, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022.
T. Dong, Effects of Different Generative Adversarial Networks on the Face Generation Task, in: 2023 IEEE International Conference on Image Processing and Computer Applications (ICIPCA), IEEE, 2023.
N. Li, et al., Single Image Deraining with Generative Adversarial Network, in: 2023 IEEE International Conference on Mechatronics and Automation (ICMA), IEEE, 2023.
S. Sabnam, S. Rajagopal, Application of Generative Adversarial Networks in Image, Face Reconstruction and Medical Imaging: Challenges and the Current Progress, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 12 (1) (2024) 2330524.
J.J. Huallpa, Exploring the Ethical Considerations of Using Chat GPT in University Education, Periodicals of Engineering and Natural Sciences 11 (4) (2023) 105–115.
I. Masi, et al., Two-Branch Recurrent Network for Isolating Deepfakes in Videos, in: Computer Vision–ECCV 2020: Proceedings, Part VII, Springer, 2020.
K. Hoo Teo, et al., Emerging GaN Technologies for Power, RF, Digital, and Quantum Computing Applications: Recent Advances and Prospects, Journal of Applied Physics 130 (16) (2021).
X. Yi, E. Walia, P. Babyn, Generative Adversarial Network in Medical Imaging: A Review, Medical Image Analysis 58 (2019) 101552.
V.S.S.P. Reddy, S.L.M.A.R. Siddharth Kumar, Enhancing Image Quality with Super-Resolution Generative Adversarial Networks, ResearchGate, 2024.
O.R. Nasir, et al., Text2FaceGAN: Face Generation from Fine Grained Textual Descriptions, in: 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), IEEE, 2019.
X. Wang, et al., ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks, in: Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018.
S. Tulyakov, et al., MoCoGAN: Decomposing Motion and Content for Video Generation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018.
F. Wang, Generation of Digital Artistic Images Based on GANs, in: 2nd International Conference on Intelligent Design and Innovative Technology (ICIDIT 2023), Atlantis Press, 2023.
S. Ruan, Anime Characters Generation with Generative Adversarial Networks, in: 2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), IEEE, 2022.
Z. Yi, et al., The Research of Anime Character Portrait Generation Based on Optimized Generative Adversarial Networks, in: 2021 33rd Chinese Control and Decision Conference (CCDC), IEEE, 2021.
J. Oshiba, M. Iwata, K. Kise, Automatic Landmark-Guided Face Image Generation for Anime Characters Using C2GAN, in: International Conference on Pattern Recognition, Springer, 2021.
X. Xing, D. Zhang, Image-to-Image Translation Using a Relativistic Generative Adversarial Network, in: Eleventh International Conference on Digital Image Processing (ICDIP 2019), SPIE, 2019.
R. Paliwal, Generative Artificial Intelligence in Metaverse Era, International Research Journal of Modernization in Engineering Technology and Science 5 (9) (2023).
D. Bau, et al., Seeing What a GAN Cannot Generate, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.
V. Sorin, et al., Creating Artificial Images for Radiology Applications Using Generative Adversarial Networks (GANs)–A Systematic Review, Academic Radiology 27 (8) (2020) 1175–1185.
L. Wang, et al., A State-of-the-Art Review on Image Synthesis with Generative Adversarial Networks, IEEE Access 8 (2020) 63514–63537.
X. Mao, Q. Li, Generative Adversarial Networks for Image Generation, Springer, 2021.
E.R. Chan, et al., π-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021.
S. Xun, et al., Generative Adversarial Networks in Medical Image Segmentation: A Review, Computers in Biology and Medicine 140 (2022) 105063.
V.L.T. de Souza, et al., A Review on Generative Adversarial Networks for Image Generation, Computers & Graphics (2023).
A. Purwanto, E. Utami, D. Agustriawan, A Comprehensive Literature Review on Generative Adversarial Networks (GANs) for AI Anime Image Generation, in: 2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS), IEEE, 2024.
B. Gecer, et al., GANFit: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.
Z. Huang, et al., PFA-GAN: Progressive Face Aging with Generative Adversarial Network, IEEE Transactions on Information Forensics and Security 16 (2020) 2031–2045.
M. Toshpulatov, W. Lee, S. Lee, Generative Adversarial Networks and Their Application to 3D Face Generation: A Survey, Image and Vision Computing 108 (2021) 104119.
X. Wang, et al., GAN-Generated Faces Detection: A Survey and New Perspectives, ECAI 2023 (2023) 2533–2542.
L. Jin, F. Tan, S. Jiang, Generative Adversarial Network Technologies and Applications in Computer Vision, Computational Intelligence and Neuroscience 2020 (2020).
F.B. Aissa, M. Mejdoub, M. Zaied, A Survey on Generative Adversarial Networks and Their Variants Methods, in: Twelfth International Conference on Machine Vision (ICMV 2019), SPIE, 2020.
A. Sasithradevi, S.M.M. Roomi, R. Sivaranjani, Generative Adversarial Network for Video Analytics, in: Generative Adversarial Networks for Image-to-Image Translation, 2021, pp. 329–345.
N. Aldausari, et al., Video Generative Adversarial Networks: A Review, ACM Computing Surveys (CSUR) 55 (2) (2022) 1–25.
M. Hamza, et al., Generative Adversarial Networks (GANs) Video Framework: A Systematic Literature Review, in: 2023 International Conference on Energy, Power, Environment, Control, and Computing (ICEPECC), IEEE, 2023.
K. Silva, et al., Text Data Augmentation Using Generative Adversarial Networks–A Systematic Review, Journal of Computational and Applied Linguistics 1 (2023) 6–38.
M.S. Meor Yahaya, J. Teo, Data Augmentation Biomarkers in Medicine and Neuroscience, Frontiers in Applied Mathematics and Statistics 9 (2023) 1162760.
S. Rani, et al., The Role of Artificial Intelligence in Art: A Comprehensive Review of a Generative Adversarial Network Portrait Painting, in: International Conference on Intelligent Computing & Optimization, Springer, 2023.
Z. Cai, et al., Generative Adversarial Networks: A Survey Toward Private and Secure Applications, ACM Computing Surveys (CSUR) 54 (6) (2021) 1–38.
D. Saxena, J. Cao, Generative Adversarial Networks (GANs) Challenges, Solutions, and Future Directions, ACM Computing Surveys (CSUR) 54 (3) (2021) 1–42.
M. Pavan Kumar, P. Jayagopal, Generative Adversarial Networks: A Survey on Applications and Challenges, International Journal of Multimedia Information Retrieval 10 (1) (2021) 1–24.
H. Chen, Challenges and Corresponding Solutions of Generative Adversarial Networks (GANs): A Survey Study, in: Journal of Physics: Conference Series, IOP Publishing, 2021.
E. Brophy, et al., Generative Adversarial Networks in Time Series: A Survey and Taxonomy, arXiv preprint arXiv:2107.11098, 2021.
H. Ku, M. Lee, TextControlGAN: Text-to-Image Synthesis with Controllable Generative Adversarial Networks, Applied Sciences 13 (8) (2023) 5098.
D. Habeeb, et al., Advancements and Challenges: A Comprehensive Review of GAN-Based Models for the Mitigation of Small Dataset and Texture Sticking Issues in Fake License Plate Recognition, Engineering, Technology & Applied Science Research 14 (6) (2024) 18401–18408.
N. Kumar, A. Aeron, S. Kumar, Generative AI Empowerment in IoT Ecosystems: Review of Real-Time Processing Challenges and Solution, 2025.
U. Sirisha, et al., An Iterative PRISMA Review of GAN Models for Image Processing, Medical Diagnosis, and Network Security, Computers, Materials & Continua 82 (2) (2025).
C. Uddagiri, B.V. Isunuri, Ethical and Privacy Challenges of Generative AI, in: Generative AI: Current Trends and Applications, Springer, 2024, pp. 219–244.
S.A. Alajaji, et al., Generative Adversarial Networks in Digital Histopathology: Current Applications, Limitations, Ethical Considerations, and Future Directions, Modern Pathology (2023) 100369.
X. Fang, et al., Bias of AI-Generated Content: An Examination of News Produced by Large Language Models, Scientific Reports 14 (1) (2024) 5224.
Y. Wang, et al., A Survey on ChatGPT: AI–Generated Contents, Challenges, and Solutions, IEEE Open Journal of the Computer Society 4 (2023) 280–302.
T. Zhang, Deepfake Generation and Detection, a Survey, Multimedia Tools and Applications 81 (5) (2022) 6259–6276.
P. Edwards, et al., A Review of Deepfake Techniques: Architecture, Detection and Datasets, IEEE Access (2024).
M. Tschaepe, Pragmatic Ethics for Generative Adversarial Networks: Coupling, Cyborgs, and Machine Learning, Contemporary Pragmatism 18 (1) (2021) 95–111.
A. Sharma, R. Sharma, Generative Artificial Intelligence and Legal Frameworks: Identifying Challenges and Proposing Regulatory Reforms, Kutafin Law Review 11 (3) (2024) 415–451.
P. Zlateva, et al., A Conceptual Framework for Solving Ethical Issues in Generative Artificial Intelligence, in: Electronics, Communications and Networks, IOS Press, 2024, pp. 110–119.
J. Anderson, Legal and Regulatory Frameworks for AI-Generated, 2024.
P. Saxena, et al., Semantic Image Completion and Enhancement Using GANs, in: High Performance Vision Intelligence: Recent Advances, Springer, 2020, pp. 151–170.
H.-Y. Tseng, et al., Regularizing Generative Adversarial Networks Under Limited Data, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021.
M. Jaweed, R.F. Shaikh, Optimizing Generative AI by Overcoming Stability Mode Collapse and Quality Challenges in GANs and VAEs, MSW Management Journal 34 (2) (2024) 497–507.
J. Wang, L. Yao, Unrolled GAN-Based Oversampling of Credit Card Dataset for Fraud Detection, in: 2022 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), IEEE, 2022.
A. Abusitta, E. Aïmeur, O. Abdel Wahab, Generative Adversarial Networks for Mitigating Biases in Machine Learning Systems, in: ECAI 2020, IOS Press, 2020, pp. 937–944