mireabulletinRussian Technological JournalRussian Technological Journal2782-32102500-316XRTU MIREA10.32362/2500-316X-2025-13-5-7-24ISXHGAmireabulletin-1241Research ArticleИНФОРМАЦИОННЫЕ СИСТЕМЫ. ИНФОРМАТИКА. ПРОБЛЕМЫ ИНФОРМАЦИОННОЙ БЕЗОПАСНОСТИINFORMATION SYSTEMS. COMPUTER SCIENCES. ISSUES OF INFORMATION SECURITYГенеративные состязательные сети в кибербезопасности: обзор литературыGenerative adversarial networks in cyber security: Literature reviewhttps://orcid.org/0009-0001-0886-5370АрафатЗ.ArafatZ.

Арафат Заид, доцент, кафедра кибербезопасности

56001, Кербала

Scopus Author ID 57963547500

Zaid Arafat, Assistant Lecturer, Department of Cybersecurity

Karbala, 56001

Scopus Author ID 57963547500

zaid.q@uokerbala.edu.iqhttps://orcid.org/0009-0005-6367-1076ЮдинаО. В.YudinaO. V.

Юдина Ольга Вадимовна, к.т.н., доцент, доцент кафедры математического и программного обеспечения ЭВМ

162600, Россия, Череповец, пр-т Луначарского, д. 5

Olga V. Yudina, Cand. Sci. (Eng.), Associate Professor, Department of Mathematics and Computer Software

Cherepovets, 162600

oviudina@chsu.ruhttps://orcid.org/0009-0004-9801-4888АбдулазизЗ. А.AbdulazeezZ. A.

Абдулазиз Зайнаб А., ассистент преподавателя, Колледж образования в области гуманитарных наук

56001, Ирак

Zainab A. Abdulazeez, Assistant Lecturer, College of Education for Human Sciences

Karbala, 56001

Scopus Author ID 57220186609

zainab.abdulhameed@uokerbala.edu.iqУниверситет КербалыИракUniversity of KerbalaIraqЧереповецкий государственный университетРоссияCherepovets State UniversityRussian Federation202508102025135724Copyright © Арафат З., Юдина О.В., Абдулазиз З.А., 20252025Арафат З., Юдина О.В., Абдулазиз З.А.Arafat Z., Yudina O.V., Abdulazeez Z.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.rtj-mirea.ru/jour/article/view/1241

Цели. Основной целью обзора является оценка изменений кибербезопасности и методов обнаружения аномалий в результате действия генеративно-состязательных сетей (ГСС). В исследовании анализируются возможности ГСС при генерации синтетических данных, моделировании состязательных атак, выявлении выбросов, а также решении проблем нестабильности обучения и этических вопросов.Методы. Проведено систематическое исследование на основе научных статей, охватывающих период с 2014 по 2024 гг.Результаты. Обсуждение сосредоточено на двух основных областях применения ГСС: обеспечении кибербезопасности посредством обнаружения вторжений и проведения состязательного тестирования, а также обнаружении аномалий в целях медицинской диагностики и мониторинга. Исследованы два ключевых варианта ГСС – вассерштейновские ГСС и условные ГСС – с точки зрения их эффективности в решении технических задач. При оценке качества синтетических данных использованы две метрики: расстояние Фреше и показатель структурного сходства.Выводы. ГСС улучшают безопасность за счет генерации специализированных наборов данных, что приводит к повышению точности систем обнаружения на 25%. Метод позволяет проводить углубленную состязательную оценку для выявления слабых мест систем, а также способствует обнаружению нарушений в областях с дефицитом данных для медицинской диагностики. Высокоразмерные задачи демонстрируют 40%-ю нестабильность обучения и приводят к 30%-й потере разнообразия выходных данных. ГСС способствуют развитию кибербезопасности и систем обнаружения аномалий, однако остаются вызовы, связанные с обеспечением стабильности обучения, снижением эксплуатационных расходов и соблюдением этических норм, регулирующих их использование. Развитие методов обучения с применением для ГСС и разработка прозрачных систем требуют дальнейших усилий в рамках ответственных инновационных инициатив.

Objectives. This review article sets out to evaluate the use of Generative Adversarial Networks (GANs) to revolutionize cybersecurity and anomaly detection process. The research focuses in particular on the capabilities of GANs to produce synthetic data and simulate adversarial attacks, as well as identifying outliers and resolving training, instability, and ethical issues.Methods. A systematic review of relevant peer-reviewed articles spanning 2014 through 2024 was undertaken.Results. The discussion concentrated on two main areas of GAN application: (1) cybersecurity through intrusion detection and adversarial testing; (2) anomaly detection for medical diagnostics and surveillance purposes. The research studied two essential GAN variants named Wasserstein GANs and Conditional GANs for their performance in addressing technical challenges. The assessment of synthetic data quality used the Fréchet Inception Distance and Structural Similarity Index Measure as evaluation metrics.Conclusions. GANs enhance security measures through their production of caused datasets resulting in a 25% improvement of detection systems accuracy. The technique allows strong adversarial assessment to reveal system weaknesses while helping detect irregularities in data-poor areas for medical diagnostics. High-dimensional tasks demonstrate 40% training instability and lead to 30% output diversity loss. The need for regulatory frameworks becomes essential due to ethical issues, which include the use of deepfakes that result in 25% success rates of biometric system evasion. Given ethical rules regulating their proper use, GANs advance cybersecurity by providing anomaly detection simultaneously with improved training stability and lower operating expenses. Prior versions of GAN-reinforcement learning and additional transparent systems require focused development as part of responsible innovation efforts.

генеративные состязательные сетикибербезопасностьобнаружение аномалийсинтетические данныесостязательные атакивассерштейновские генеративные состязательные сетиgenerative adversarial networkscybersecurityanomaly detectionsynthetic data generationadversarial attacksWasserstein GANsReferencesGoodfellow I., Pouget-Abadie J., Mirza M., et al. Generative adversarial networks. Commun. ACM. 2020;63(11):139–144. https://doi.org/10.1145/3422622Goodfellow I., Pouget-Abadie J., Mirza M., et al. Generative adversarial networks. Commun. ACM. 2020;63(11):139–144. https://doi.org/10.1145/3422622Arifin M.M., Ahmed M.S., Ghosh T.K., Udoy I.A., Zhuang J., Yeh J. A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective. Direction and Open Research Scopes. 2024. ArXiv Prepr. arXiv:2407.08839. https://doi.org/10.48550/arXiv.2407.08839Arifin M.M., Ahmed M.S., Ghosh T.K., Udoy I.A., Zhuang J., Yeh J. A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective. Direction and Open Research Scopes. 2024. ArXiv Prepr. arXiv:2407.08839. https://doi.org/10.48550/arXiv.2407.08839Sabuhi M., Zhou M., Bezemer C.-P., Musilek P. Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review. IEEE Access. 2021;9:161003–161029. https://doi.org/10.1109/ACCESS.2021.3131949Sabuhi M., Zhou M., Bezemer C.-P., Musilek P. Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review. IEEE Access. 2021;9:161003–161029. https://doi.org/10.1109/ACCESS.2021.3131949Aggarwal A., Mittal V., Battineni G. Generative adversarial network: An overview of theory and applications. Int. J. Inf. Manag. Data Insights. 2021;1(1):100004. https://doi.org/10.1016/j.jjimei.2020.100004Aggarwal A., Mittal V., Battineni G. Generative adversarial network: An overview of theory and applications. Int. J. Inf. Manag. Data Insights. 2021;1(1):100004. https://doi.org/10.1016/j.jjimei.2020.100004Cao Y.-J., Jia L.-L., Chen Y.-X., et al. Recent Advances of Generative Adversarial Networks in Computer Vision. IEEE Access. 2019;7:14985–15006. https://doi.org/10.1109/ACCESS.2018.2886814Cao Y.-J., Jia L.-L., Chen Y.-X., et al. Recent Advances of Generative Adversarial Networks in Computer Vision. IEEE Access. 2019;7:14985–15006. https://doi.org/10.1109/ACCESS.2018.2886814Radford A., Metz L., Chintala S. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. 2016.ArXiv Prepr. arXiv:1511.06434. https://doi.org/10.48550/arXiv.1511.06434Radford A., Metz L., Chintala S. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. 2016.ArXiv Prepr. arXiv:1511.06434. https://doi.org/10.48550/arXiv.1511.06434Arjovsky M, Chintala S., Bottou L. Wasserstein generative adversarial networks. In: Proceedings of the International Conference on Machine Learning (ICML). PMLR. 2017. P. 214–223. Available from URL: https://proceedings.mlr.press/v70/arjovsky17a/arjovsky17a.pdfArjovsky M, Chintala S., Bottou L. Wasserstein generative adversarial networks. In: Proceedings of the International Conference on Machine Learning (ICML). PMLR. 2017. P. 214–223. Available from URL: https://proceedings.mlr.press/v70/arjovsky17a/arjovsky17a.pdfZhu J.-Y., Park T., Isola P., Efros A.A. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. 2020. ArXiv Prepr. arXiv:1703.10593. https://doi.org/10.48550/arXiv.1703.10593Zhu J.-Y., Park T., Isola P., Efros A.A. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. 2020. ArXiv Prepr. arXiv:1703.10593. https://doi.org/10.48550/arXiv.1703.10593Mirza M., Osindero S. Conditional Generative Adversarial Nets. 2014. ArXiv Prepr. arXiv:1411.1784. https://doi.org/10.48550/arXiv.1411.1784Mirza M., Osindero S. Conditional Generative Adversarial Nets. 2014. ArXiv Prepr. arXiv:1411.1784. https://doi.org/10.48550/arXiv.1411.1784Karras T., Laine S., Aila T. A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019. P. 4401–4410. https://doi.org/10.1109/CVPR.2019.00453Karras T., Laine S., Aila T. A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019. P. 4401–4410. https://doi.org/10.1109/CVPR.2019.00453Sedjelmaci H. Attacks detection and decision framework based on generative adversarial network approach: Case of vehicular edge computing network. Trans. Emerg. Telecommun. Technol. 2022;33(10):e4073. https://doi.org/10.1002/ett.4073Sedjelmaci H. Attacks detection and decision framework based on generative adversarial network approach: Case of vehicular edge computing network. Trans. Emerg. Telecommun. Technol. 2022;33(10):e4073. https://doi.org/10.1002/ett.4073Kumaran U., Thangam S., Prabhakar T.N., Selvaganesan J., Vishwas H.N. Adversarial Defense: A GAN-IF Based Cybersecurity Model for Intrusion Detection in Software Piracy. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 2023;14(4):96–114. http://doi.org/10.58346/JOWUA.2023.I4.008Kumaran U., Thangam S., Prabhakar T.N., Selvaganesan J., Vishwas H.N. Adversarial Defense: A GAN-IF Based Cybersecurity Model for Intrusion Detection in Software Piracy. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 2023;14(4):96–114. http://doi.org/10.58346/JOWUA.2023.I4.008Haloui I., Gupta J.S., Feuillard V. Anomaly detection with Wasserstein GAN. 2018. ArXiv Prepr. arXiv:1812.02463. https://doi.org/10.48550/arXiv.1812.02463Haloui I., Gupta J.S., Feuillard V. Anomaly detection with Wasserstein GAN. 2018. ArXiv Prepr. arXiv:1812.02463. https://doi.org/10.48550/arXiv.1812.02463Kimura D., Chaudhury S., Narita M., Munawar A., Tachibana R. Adversarial Discriminative Attention for Robust Anomaly Detection. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE; 2020. P. 2161–2170. https://doi.org/10.1109/WACV45572.2020.9093428Kimura D., Chaudhury S., Narita M., Munawar A., Tachibana R. Adversarial Discriminative Attention for Robust Anomaly Detection. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE; 2020. P. 2161–2170. https://doi.org/10.1109/WACV45572.2020.9093428Dunmore A., Jang-Jaccard J., Sabrina F., Kwak J. A Comprehensive Survey of Generative Adversarial Networks (GANs) in Cybersecurity Intrusion Detection. IEEE Access. 2023;11:76071–76094. https://doi.org/10.1109/ACCESS.2023.3296707Dunmore A., Jang-Jaccard J., Sabrina F., Kwak J. A Comprehensive Survey of Generative Adversarial Networks (GANs) in Cybersecurity Intrusion Detection. IEEE Access. 2023;11:76071–76094. https://doi.org/10.1109/ACCESS.2023.3296707Kos J., Fischer I., Song D. Adversarial examples for generative models. 2017. ArXiv Prepr. arXiv:1702.06832. https://doi.org/10.48550/arXiv.1702.06832Kos J., Fischer I., Song D. Adversarial examples for generative models. 2017. ArXiv Prepr. arXiv:1702.06832. https://doi.org/10.48550/arXiv.1702.06832Chhetri S.R., Lopez A.B., Wan J., Al Faruque M.A. GAN-Sec: Generative Adversarial Network Modeling for the Security Analysis of Cyber-Physical Production Systems. In: 2019 Design. Automation & Test in Europe Conference & Exhibition (DATE). IEEE; 2019. P. 770–775. https://doi.org/10.23919/DATE.2019.8715283Chhetri S.R., Lopez A.B., Wan J., Al Faruque M.A. GAN-Sec: Generative Adversarial Network Modeling for the Security Analysis of Cyber-Physical Production Systems. In: 2019 Design. Automation & Test in Europe Conference & Exhibition (DATE). IEEE; 2019. P. 770–775. https://doi.org/10.23919/DATE.2019.8715283Mao X., Li Q., Xie H., et al. Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV). 2017. P. 2794–2802. https://doi.org/10.1109/ICCV.2017.304Mao X., Li Q., Xie H., et al. Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV). 2017. P. 2794–2802. https://doi.org/10.1109/ICCV.2017.304Nataraj L., Karthikeyan S., Jacob G., Manjunath B.S. Malware images: visualization and automatic classification. In: Proceedings of the 8th International Symposium on Visualization for Cyber Security. 2011. P. 1–7. https://doi.org/10.1145/2016904.2016908Nataraj L., Karthikeyan S., Jacob G., Manjunath B.S. Malware images: visualization and automatic classification. In: Proceedings of the 8th International Symposium on Visualization for Cyber Security. 2011. P. 1–7. https://doi.org/10.1145/2016904.2016908Chen X., Duan Y., Houthooft R., et al. InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets. In: Advances in Neural Information Processing Systems (NeurIPS). 2016;29:2172–2180.Chen X., Duan Y., Houthooft R., et al. InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets. In: Advances in Neural Information Processing Systems (NeurIPS). 2016;29:2172–2180.Alo S.O., Jamil A.S., Hussein M.J., Al-Dulaimi M.K.H., Taha S.W., Khlaponina A. Automated Detection of Cybersecurity Threats Using Generative Adversarial Networks (GANs). In: 2024 36th Conference of Open Innovations Association (FRUCT). IEEE. 2024. P. 566–577. https://doi.org/10.23919/FRUCT64283.2024.10749874Alo S.O., Jamil A.S., Hussein M.J., Al-Dulaimi M.K.H., Taha S.W., Khlaponina A. Automated Detection of Cybersecurity Threats Using Generative Adversarial Networks (GANs). In: 2024 36th Conference of Open Innovations Association (FRUCT). IEEE. 2024. P. 566–577. https://doi.org/10.23919/FRUCT64283.2024.10749874Zhang J., Li C. Adversarial examples: Opportunities and challenges. IEEE Trans. Neural Netw. Learn. Syst. 2019;31(7): 2578–2593. https://doi.org/10.1109/TNNLS.2019.2933524Zhang J., Li C. Adversarial examples: Opportunities and challenges. IEEE Trans. Neural Netw. Learn. Syst. 2019;31(7): 2578–2593. https://doi.org/10.1109/TNNLS.2019.2933524Zhang S., Xie X., Xu Y. A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity. IEEE Access. 2020;8:128250–128263. https://doi.org/10.1109/ACCESS.2020.3008433Zhang S., Xie X., Xu Y. A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity. IEEE Access. 2020;8:128250–128263. https://doi.org/10.1109/ACCESS.2020.3008433Papernot N., McDaniel P., Goodfellow I., Jha S., Celik Z.B., Swami A. Practical Black-Box Attacks against Machine Learning. In: Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security. ACM. 2017. P. 506–519. https://doi.org/10.1145/3052973.3053009Papernot N., McDaniel P., Goodfellow I., Jha S., Celik Z.B., Swami A. Practical Black-Box Attacks against Machine Learning. In: Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security. ACM. 2017. P. 506–519. https://doi.org/10.1145/3052973.3053009Kurakin A., Goodfellow I.J., Bengio S. Adversarial examples in the physical world. In book: Artificial Intelligence Safety and Security. Chapman and Hall/CRC. 2018. P. 99–112. https://doi.org/10.1201/9781351251389, Available from URL: https://www.taylorfrancis.com/chapters/edit/10.1201/9781351251389-8/adversarial-examples-physical-world-alexeykurakin-ian-goodfellow-samy-bengioKurakin A., Goodfellow I.J., Bengio S. Adversarial examples in the physical world. In book: Artificial Intelligence Safety and Security. Chapman and Hall/CRC. 2018. P. 99–112. https://doi.org/10.1201/9781351251389, Available from URL: https://www.taylorfrancis.com/chapters/edit/10.1201/9781351251389-8/adversarial-examples-physical-world-alexeykurakin-ian-goodfellow-samy-bengioTaheri S., Khormali A., Salem M., Yuan J.-S. Developing a robust defensive system against adversarial examples using generative adversarial networks. Big Data Cogn. Comput. 2020;4(2):11. https://doi.org/10.3390/bdcc4020011Taheri S., Khormali A., Salem M., Yuan J.-S. Developing a robust defensive system against adversarial examples using generative adversarial networks. Big Data Cogn. Comput. 2020;4(2):11. https://doi.org/10.3390/bdcc4020011Carlini N., Wagner D. Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (SP). IEEE. 2017. P. 39–57. https://doi.org/10.1109/SP.2017.49Carlini N., Wagner D. Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (SP). IEEE. 2017. P. 39–57. https://doi.org/10.1109/SP.2017.49Madry A., Makelov A., Schmidt L., Tsipras D., Vladu A. Towards Deep Learning Models Resistant to Adversarial Attacks. 2019. ArXiv Prepr. arXiv:1706.06083. https://doi.org/10.48550/arXiv.1706.06083Madry A., Makelov A., Schmidt L., Tsipras D., Vladu A. Towards Deep Learning Models Resistant to Adversarial Attacks. 2019. ArXiv Prepr. arXiv:1706.06083. https://doi.org/10.48550/arXiv.1706.06083Sharif M., Bhagavatula S., Bauer L., Reiter M.K. Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM. 2016. P. 1528–1540. https://doi.org/10.1145/2976749.2978392Sharif M., Bhagavatula S., Bauer L., Reiter M.K. Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM. 2016. P. 1528–1540. https://doi.org/10.1145/2976749.2978392Akhtar N., Mian A. Threat of adversarial attacks on deep learning in computer vision: A survey. IEEE Access. 2018;6: 14410–14430. https://doi.org/10.1109/ACCESS.2018.2807385Akhtar N., Mian A. Threat of adversarial attacks on deep learning in computer vision: A survey. IEEE Access. 2018;6: 14410–14430. https://doi.org/10.1109/ACCESS.2018.2807385Dong Y., Pang T., Su H., Zhu J. Evading defenses to transferable adversarial examples by translation-invariant attacks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019. P. 4312–4321. Available from URL: http://openaccess.thecvf.com/content_CVPR_2019/html/Dong_Evading_Defenses_to_Transferable_Adversarial_Examples_by_Translation-Invariant_Attacks_CVPR_2019_paper.htmlDong Y., Pang T., Su H., Zhu J. Evading defenses to transferable adversarial examples by translation-invariant attacks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019. P. 4312–4321. Available from URL: http://openaccess.thecvf.com/content_CVPR_2019/html/Dong_Evading_Defenses_to_Transferable_Adversarial_Examples_by_Translation-Invariant_Attacks_CVPR_2019_paper.htmlShafahi A., Najibi M., Ghiasi A., et al. Adversarial training for free! Adv. Neural Inf. Process. Syst. 2019:32. Available from URL: https://proceedings.neurips.cc/paper/by-source-2019-1853Shafahi A., Najibi M., Ghiasi A., et al. Adversarial training for free! Adv. Neural Inf. Process. Syst. 2019:32. Available from URL: https://proceedings.neurips.cc/paper/by-source-2019-1853Xiao C., Li B., Zhu J., He W., Liu M., Song D. Generating Adversarial Examples with Adversarial Networks. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization. 2018. P. 3905–3911. https://doi.org/10.24963/ijcai.2018/543Xiao C., Li B., Zhu J., He W., Liu M., Song D. Generating Adversarial Examples with Adversarial Networks. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization. 2018. P. 3905–3911. https://doi.org/10.24963/ijcai.2018/543Hou T., Wang T., Lu Z., Liu Y., Sagduyu Y. IoTGAN: GAN powered camouflage against machine learning based IoT device identification. In: 2021 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). IEEE. 2021. P. 280–287. https://doi.org/10.1109/DySPAN53946.2021.9677264Hou T., Wang T., Lu Z., Liu Y., Sagduyu Y. IoTGAN: GAN powered camouflage against machine learning based IoT device identification. In: 2021 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). IEEE. 2021. P. 280–287. https://doi.org/10.1109/DySPAN53946.2021.9677264Kurakin A., Goodfellow I., Bengio S. Adversarial examples in the physical world. 2017. ArXiv Prepr. arXiv:1607.02533. https://doi.org/10.48550/arXiv.1607.02533.Kurakin A., Goodfellow I., Bengio S. Adversarial examples in the physical world. 2017. ArXiv Prepr. arXiv:1607.02533. https://doi.org/10.48550/arXiv.1607.02533.Goodfellow I., Pouget-Abadie J., Mirza M., et al. Generative adversarial nets. Adv. Neural Inf. Process. Syst. 2014;27. Available from URL: https://proceedings.neurips.cc/paper/5423-generative-adversarial-netsGoodfellow I., Pouget-Abadie J., Mirza M., et al. Generative adversarial nets. Adv. Neural Inf. Process. Syst. 2014;27. Available from URL: https://proceedings.neurips.cc/paper/5423-generative-adversarial-netsBengio Y. Learning Deep Architectures for AI. Found. Trends® Mach. Learn. 2009;2(1):1–127. https://doi.org/10.1561/2200000006Bengio Y. Learning Deep Architectures for AI. Found. Trends® Mach. Learn. 2009;2(1):1–127. https://doi.org/10.1561/2200000006Isola P., Zhu J.-Y., Zhou T., Efros A.A. Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017. P. 1125–1134. Available from URL: http://openaccess.thecvf.com/content_cvpr_2017/html/Isola_Image-To-Image_Translation_With_CVPR_2017_paper.htmlIsola P., Zhu J.-Y., Zhou T., Efros A.A. Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017. P. 1125–1134. Available from URL: http://openaccess.thecvf.com/content_cvpr_2017/html/Isola_Image-To-Image_Translation_With_CVPR_2017_paper.htmlSalimans T., Goodfellow I., Zaremba W., et al. Improved techniques for training GANs. In: Adv. Neural Inf. Process. Syst. (NeurIPS). 2016;29. Available from URL: https://proceedings.neurips.cc/paper_files/paper/2016/hash/8a3363abe792db2d8761d6403605aeb7-Abstract.htmlSalimans T., Goodfellow I., Zaremba W., et al. Improved techniques for training GANs. In: Adv. Neural Inf. Process. Syst. (NeurIPS). 2016;29. Available from URL: https://proceedings.neurips.cc/paper_files/paper/2016/hash/8a3363abe792db2d8761d6403605aeb7-Abstract.htmlArjovsky M., Bottou L. Towards Principled Methods for Training Generative Adversarial Networks. 2017. ArXiv Prepr. arXiv:1701.04862. https://doi.org/10.48550/arXiv.1701.04862Arjovsky M., Bottou L. Towards Principled Methods for Training Generative Adversarial Networks. 2017. ArXiv Prepr. arXiv:1701.04862. https://doi.org/10.48550/arXiv.1701.04862Ho J., Ermon S. Generative adversarial imitation learning. Adv. Neural Inf. Process. Syst. (NeurIPS). 2016;29. Available from URL: https://papers.nips.cc/paper_files/paper/2016/hash/cc7e2b878868cbae992d1fb743995d8f-Abstract.htmlHo J., Ermon S. Generative adversarial imitation learning. Adv. Neural Inf. Process. Syst. (NeurIPS). 2016;29. Available from URL: https://papers.nips.cc/paper_files/paper/2016/hash/cc7e2b878868cbae992d1fb743995d8f-Abstract.htmlMittal S., Joshi A., Finin T. Cyber-All-Intel: An AI for Security related Threat Intelligence. 2019. ArXiv Prepr. arXiv:1905.02895. https://doi.org/10.48550/arXiv.1905.02895Mittal S., Joshi A., Finin T. Cyber-All-Intel: An AI for Security related Threat Intelligence. 2019. ArXiv Prepr. arXiv:1905.02895. https://doi.org/10.48550/arXiv.1905.02895Yinka-Banjo C., Ugot O.-A. A review of generative adversarial networks and its application in cybersecurity. Artif. Intell. Rev. 2020;53(3):1721–1736. https://doi.org/10.1007/s10462-019-09717-4Yinka-Banjo C., Ugot O.-A. A review of generative adversarial networks and its application in cybersecurity. Artif. Intell. Rev. 2020;53(3):1721–1736. https://doi.org/10.1007/s10462-019-09717-4Yan Q., Wang M., Huang W., Luo X., Yu F.R. Automatically synthesizing DoS attack traces using generative adversarial networks. Int. J. Mach. Learn. Cybern. 2019;10(12):3387–3396. https://doi.org/10.1007/s13042-019-00925-6Yan Q., Wang M., Huang W., Luo X., Yu F.R. Automatically synthesizing DoS attack traces using generative adversarial networks. Int. J. Mach. Learn. Cybern. 2019;10(12):3387–3396. https://doi.org/10.1007/s13042-019-00925-6Goodfellow I.J., Pouget-Abadie M., Mirza M., et al. Generative adversarial nets. Adv. Neural Inf. Process. Syst. (NeurIPS). 2014;27. Available from URL: https://papers.nips.cc/paper_files/paper/2014/hash/f033ed80deb0234979a61f95710dbe25-Abstract.htmlGoodfellow I.J., Pouget-Abadie M., Mirza M., et al. Generative adversarial nets. Adv. Neural Inf. Process. Syst. (NeurIPS). 2014;27. Available from URL: https://papers.nips.cc/paper_files/paper/2014/hash/f033ed80deb0234979a61f95710dbe25-Abstract.htmlChoi Y., Choi M., Kim M., Ha J.-W., Kim S., Choo J. StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE. 2018. P. 8789–8797. https://doi.org/10.1109/CVPR.2018.00916Choi Y., Choi M., Kim M., Ha J.-W., Kim S., Choo J. StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE. 2018. P. 8789–8797. https://doi.org/10.1109/CVPR.2018.00916Karras T., Aila T., Laine S., Lehtinen J. Progressive Growing of GANs for Improved Quality, Stability, and Variation. In: International Conference on Learning Representations. 2018. Available from URL: https://research.aalto.fi/en/publications/progressive-growing-of-gans-for-improved-quality-stability-and-vaKarras T., Aila T., Laine S., Lehtinen J. Progressive Growing of GANs for Improved Quality, Stability, and Variation. In: International Conference on Learning Representations. 2018. Available from URL: https://research.aalto.fi/en/publications/progressive-growing-of-gans-for-improved-quality-stability-and-vaBrock A., Donahue J., Simonyan K. Large Scale GAN Training for High Fidelity Natural Image Synthesis. In: International Conference on Learning Representations. 2018. https://doi.org/10.48550/arXiv.1809.11096Brock A., Donahue J., Simonyan K. Large Scale GAN Training for High Fidelity Natural Image Synthesis. In: International Conference on Learning Representations. 2018. https://doi.org/10.48550/arXiv.1809.11096Wang T.-C., Liu M.-Y., Zhu J.-Y., Tao A., Kautz J., Catanzaro B. High-resolution image synthesis and semantic manipulation with conditional gans. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2018. P. 8798–8807. Available from URL: http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_High-Resolution_Image_Synthesis_CVPR_2018_paper.htmlWang T.-C., Liu M.-Y., Zhu J.-Y., Tao A., Kautz J., Catanzaro B. High-resolution image synthesis and semantic manipulation with conditional gans. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2018. P. 8798–8807. Available from URL: http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_High-Resolution_Image_Synthesis_CVPR_2018_paper.htmlLi C., Wand M. Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks. In: Leibe B., Matas J., Sebe N., Welling M. (Eds.). Computer Vision – ECCV 2016. Series: Lecture Notes in Computer Science. Cham: Springer; 2016. V. 9907. P. 702–716. https://doi.org/10.1007/978-3-319-46487-9_43Li C., Wand M. Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks. In: Leibe B., Matas J., Sebe N., Welling M. (Eds.). Computer Vision – ECCV 2016. Series: Lecture Notes in Computer Science. Cham: Springer; 2016. V. 9907. P. 702–716. https://doi.org/10.1007/978-3-319-46487-9_43Mirsky Y., Lee W. The Creation and Detection of Deepfakes: A Survey. ACM Comput. Surv. Jan. 2022;54(1):1–41. https://doi.org/10.1145/3425780Mirsky Y., Lee W. The Creation and Detection of Deepfakes: A Survey. ACM Comput. Surv. Jan. 2022;54(1):1–41. https://doi.org/10.1145/3425780Odena A., Olah C., Shlens J. Conditional image synthesis with auxiliary classifier GANs. In: Proceedings of the 34th International Conference on Machine Learning (ICML). PMLR. 2017. P. 2642–2651. Available from URL: https://proceedings.mlr.press/v70/odena17a.htmlOdena A., Olah C., Shlens J. Conditional image synthesis with auxiliary classifier GANs. In: Proceedings of the 34th International Conference on Machine Learning (ICML). PMLR. 2017. P. 2642–2651. Available from URL: https://proceedings.mlr.press/v70/odena17a.htmlWang Z., She Q., Ward T.E. Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy. ACM Comput. Surv. 2022;54(2):1–38. https://doi.org/10.1145/3439723Wang Z., She Q., Ward T.E. Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy. ACM Comput. Surv. 2022;54(2):1–38. https://doi.org/10.1145/3439723Creswell A., White T., Dumoulin V., Arulkumaran K., Sengupta B., Bharath A.A. Generative Adversarial Networks: An Overview. IEEE Signal Process. Mag. 2018;35(1):53–65. https://doi.org/10.1109/MSP.2017.2765202Creswell A., White T., Dumoulin V., Arulkumaran K., Sengupta B., Bharath A.A. Generative Adversarial Networks: An Overview. IEEE Signal Process. Mag. 2018;35(1):53–65. https://doi.org/10.1109/MSP.2017.2765202Zhang H., Goodfellow I., Metaxas L., et al. Self-attention generative adversarial networks. In: Proceedings of the 36th International Conference on Machine Learning (ICML). PMLR. 2019. P. 7354–7363. Available from URL: https://proceedings.mlr.press/v97/zhang19d.htmlZhang H., Goodfellow I., Metaxas L., et al. Self-attention generative adversarial networks. In: Proceedings of the 36th International Conference on Machine Learning (ICML). PMLR. 2019. P. 7354–7363. Available from URL: https://proceedings.mlr.press/v97/zhang19d.htmlLucic M., Kurach K., Michalski M., Gelly S., Bousquet O. Are gans created equal? A large-scale study. Adv. Neural Inf. Process. Syst. (NeurIPS) 2018;31. Available from URL: https://proceedings.neurips.cc/paper/2018/hash/e46de7e1bcaaced9a54f1e9d0d2f800d-Abstract.htmlLucic M., Kurach K., Michalski M., Gelly S., Bousquet O. Are gans created equal? A large-scale study. Adv. Neural Inf. Process. Syst. (NeurIPS) 2018;31. Available from URL: https://proceedings.neurips.cc/paper/2018/hash/e46de7e1bcaaced9a54f1e9d0d2f800d-Abstract.htmlSun H., Zhu T., Zhang Z., Xiong D.J.P., Zhou W. Adversarial Attacks Against Deep Generative Models on Data: A Survey. IEEE Trans. Knowl. Data Eng. 2023;35(4):3367–3388. https://doi.org/10.1109/TKDE.2021.3130903Sun H., Zhu T., Zhang Z., Xiong D.J.P., Zhou W. Adversarial Attacks Against Deep Generative Models on Data: A Survey. IEEE Trans. Knowl. Data Eng. 2023;35(4):3367–3388. https://doi.org/10.1109/TKDE.2021.3130903Miyato T., Kataoka T., Koyama M., Yoshida Y. Spectral Normalization for Generative Adversarial Networks. 2018. ArXiv Prepr. arXiv:1802.05957. https://doi.org/10.48550/arXiv.1802.05957Miyato T., Kataoka T., Koyama M., Yoshida Y. Spectral Normalization for Generative Adversarial Networks. 2018. ArXiv Prepr. arXiv:1802.05957. https://doi.org/10.48550/arXiv.1802.05957Che T., Li Y., Jacob A.P., Bengio Y., Li W. Mode Regularized Generative Adversarial Networks. 2017. ArXiv Prepr. arXiv:1612.02136. https://doi.org/10.48550/arXiv.1612.02136Che T., Li Y., Jacob A.P., Bengio Y., Li W. Mode Regularized Generative Adversarial Networks. 2017. ArXiv Prepr. arXiv:1612.02136. https://doi.org/10.48550/arXiv.1612.02136Bao J., Chen D., Wen F., Li H., Hua G. CVAE-GAN: Fine-Grained Image Generation Through Asymmetric Training. Presented at the Proceedings of the IEEE International Conference on Computer Vision (ICCV). 2017. P. 2745–2754. Available from URL: https://openaccess.thecvf.com/content_iccv_2017/html/Bao_CVAE-GAN_Fine-Grained_Image_ICCV_2017_paper.htmlBao J., Chen D., Wen F., Li H., Hua G. CVAE-GAN: Fine-Grained Image Generation Through Asymmetric Training. Presented at the Proceedings of the IEEE International Conference on Computer Vision (ICCV). 2017. P. 2745–2754. Available from URL: https://openaccess.thecvf.com/content_iccv_2017/html/Bao_CVAE-GAN_Fine-Grained_Image_ICCV_2017_paper.htmlWesterlund M. The emergence of deepfake technology: A review. Technol. Innov. Manag. Rev. 2019;9(11):39–52.Westerlund M. The emergence of deepfake technology: A review. Technol. Innov. Manag. Rev. 2019;9(11):39–52.Tolosana R., Vera-Rodriguez R., Fierrez J., Morales A., Ortega-Garcia J. Deepfakes and beyond: A survey of face manipulation and fake detection. Inf. Fusion. 2020;64:131–148. https://doi.org/10.1016/j.inffus.2020.06.014Tolosana R., Vera-Rodriguez R., Fierrez J., Morales A., Ortega-Garcia J. Deepfakes and beyond: A survey of face manipulation and fake detection. Inf. Fusion. 2020;64:131–148. https://doi.org/10.1016/j.inffus.2020.06.014Zhao J. Energy-based Generative Adversarial Network. 2016. ArXiv Prepr. arXiv:1609.03126. https://doi.org/10.48550/arXiv.1609.03126Zhao J. Energy-based Generative Adversarial Network. 2016. ArXiv Prepr. arXiv:1609.03126. https://doi.org/10.48550/arXiv.1609.03126Reed S., Akata Z., Yan X., Logeswaran L., Schiele B., Lee H. Generative adversarial text to image synthesis. In: Proceedings of the 33th International Conference on Machine Learning. PMLR. 2016. P. 1060–1069. Available from URL: http://proceedings.mlr.press/v48/reed16.htmlReed S., Akata Z., Yan X., Logeswaran L., Schiele B., Lee H. Generative adversarial text to image synthesis. In: Proceedings of the 33th International Conference on Machine Learning. PMLR. 2016. P. 1060–1069. Available from URL: http://proceedings.mlr.press/v48/reed16.htmlLloyd S., Weedbrook C. Quantum Generative Adversarial Learning. Phys. Rev. Lett. 2018;121(4):040502. https://doi.org/10.1103/PhysRevLett.121.040502Lloyd S., Weedbrook C. Quantum Generative Adversarial Learning. Phys. Rev. Lett. 2018;121(4):040502. https://doi.org/10.1103/PhysRevLett.121.040502Gulrajani I., Ahmed F., Arjovsky M., Dumoulin V., Courville A.C. Improved training of wasserstein gans. Adv. Neural Inf. Process. Syst. 2017;30. Available from URL: https://proceedings.neurips.cc/paper/2017/hash/892c3b1c6dccd52936e27cbd0ff683d6-Abstract.htmlGulrajani I., Ahmed F., Arjovsky M., Dumoulin V., Courville A.C. Improved training of wasserstein gans. Adv. Neural Inf. Process. Syst. 2017;30. Available from URL: https://proceedings.neurips.cc/paper/2017/hash/892c3b1c6dccd52936e27cbd0ff683d6-Abstract.htmlPark T., Liu M.-Y., Wang T.-C., Zhu J.-Y. Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019. P. 2337–2346. Available from URL: http://openaccess.thecvf.com/content_CVPR_2019/html/Park_Semantic_Image_Synthesis_With_Spatially-Adaptive_Normalization_CVPR_2019_paper.htmlPark T., Liu M.-Y., Wang T.-C., Zhu J.-Y. Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019. P. 2337–2346. Available from URL: http://openaccess.thecvf.com/content_CVPR_2019/html/Park_Semantic_Image_Synthesis_With_Spatially-Adaptive_Normalization_CVPR_2019_paper.htmlHoang Q., Nguyen T.D., Le T., Phung D. MGAN: Training Generative Adversarial Nets with Multiple Generators. 2018.Hoang Q., Nguyen T.D., Le T., Phung D. MGAN: Training Generative Adversarial Nets with Multiple Generators. 2018.Hitaj B., Gasti P., Ateniese G., Perez-Cruz F. PassGAN: A Deep Learning Approach for Password Guessing. 2019. ArXiv Prepr. arXiv:1709.00440. https://doi.org/10.48550/arXiv.1709.00440Hitaj B., Gasti P., Ateniese G., Perez-Cruz F. PassGAN: A Deep Learning Approach for Password Guessing. 2019. ArXiv Prepr. arXiv:1709.00440. https://doi.org/10.48550/arXiv.1709.00440Sharma Y., Ding G.W., Brubaker M. On the Effectiveness of Low Frequency Perturbations. 2019. ArXiv Prepr. arXiv:1903.00073. https://doi.org/10.48550/arXiv.1903.00073Sharma Y., Ding G.W., Brubaker M. On the Effectiveness of Low Frequency Perturbations. 2019. ArXiv Prepr. arXiv:1903.00073. https://doi.org/10.48550/arXiv.1903.00073Zhang C., Yu S., Tian Z., Yu J.J.Q. Generative Adversarial Networks: A Survey on Attack and Defense Perspective. ACM Comput. Surv. 2024;56(4):1–35. https://doi.org/10.1145/3615336Zhang C., Yu S., Tian Z., Yu J.J.Q. Generative Adversarial Networks: A Survey on Attack and Defense Perspective. ACM Comput. Surv. 2024;56(4):1–35. https://doi.org/10.1145/3615336Zhang J., Zhao L., Yu K., Min G., Al-Dubai A.Y., Zomaya A.Y. A Novel Federated Learning Scheme for Generative Adversarial Networks. IEEE Trans. Mob. Comput. 2024;23(5):3633–3649. https://doi.org/10.1109/TMC.2023.3278668Zhang J., Zhao L., Yu K., Min G., Al-Dubai A.Y., Zomaya A.Y. A Novel Federated Learning Scheme for Generative Adversarial Networks. IEEE Trans. Mob. Comput. 2024;23(5):3633–3649. https://doi.org/10.1109/TMC.2023.3278668Kaviani S., Han K.J., Sohn I. Adversarial attacks and defenses on AI in medical imaging informatics: A survey. Expert Syst. Appl. 2022;198:116815. https://doi.org/10.1016/j.eswa.2022.116815Kaviani S., Han K.J., Sohn I. Adversarial attacks and defenses on AI in medical imaging informatics: A survey. Expert Syst. Appl. 2022;198:116815. https://doi.org/10.1016/j.eswa.2022.116815Ribeiro M.T., Singh S., Guestrin C. Why Should I Trust You? Explaining the Predictions of Any Classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, California, USA. 2016. P. 1135–1144. https://doi.org/10.1145/2939672.2939778Ribeiro M.T., Singh S., Guestrin C. Why Should I Trust You? Explaining the Predictions of Any Classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, California, USA. 2016. P. 1135–1144. https://doi.org/10.1145/2939672.2939778Zhang Q., Wu Y.N., Zhu S.-C. Interpretable convolutional neural networks. In: Proceedings of the IEEE/CVPR Conference on Computer Vision and Pattern Recognition. 2018. P. 8827–8836. https://doi.org/10.1109/CVPR.2018.00920Zhang Q., Wu Y.N., Zhu S.-C. Interpretable convolutional neural networks. In: Proceedings of the IEEE/CVPR Conference on Computer Vision and Pattern Recognition. 2018. P. 8827–8836. https://doi.org/10.1109/CVPR.2018.00920Borji A. Pros and Cons of GAN Evaluation Measures. 2018. ArXiv Prepr. arXiv:1802.03446. https://doi.org/10.48550/arXiv.1802.03446Borji A. Pros and Cons of GAN Evaluation Measures. 2018. ArXiv Prepr. arXiv:1802.03446. https://doi.org/10.48550/arXiv.1802.03446Yang Y., Li Y., Zhang W., Qin F., Zhu P., Wang C.-X. Generative-Adversarial-Network-Based Wireless Channel Modeling: Challenges and Opportunities. IEEE Commun. Mag. 2019;57(3):22–27. https://doi.org/10.1109/MCOM.2019.1800635Yang Y., Li Y., Zhang W., Qin F., Zhu P., Wang C.-X. Generative-Adversarial-Network-Based Wireless Channel Modeling: Challenges and Opportunities. IEEE Commun. Mag. 2019;57(3):22–27. https://doi.org/10.1109/MCOM.2019.1800635Li T., Zhang S., Xia J. Quantum generative adversarial network: A survey. Comput. Mater. Contin. 2020;64(1):401–438. https://doi.org/10.32604/cmc.2020.010551Li T., Zhang S., Xia J. Quantum generative adversarial network: A survey. Comput. Mater. Contin. 2020;64(1):401–438. https://doi.org/10.32604/cmc.2020.010551Zhao S., Liu Z., Lin J., Zhu J.-Y., Han S. Differentiable augmentation for data-efficient GAN training. Adv. Neural Inf. Process. Syst. 2020;33:7559–7570. Available from URL: https://proceedings.neurips.cc/paper/2020/hash/55479c55ebd1efd3ff125f1337100388-Abstract.htmlZhao S., Liu Z., Lin J., Zhu J.-Y., Han S. Differentiable augmentation for data-efficient GAN training. Adv. Neural Inf. Process. Syst. 2020;33:7559–7570. Available from URL: https://proceedings.neurips.cc/paper/2020/hash/55479c55ebd1efd3ff125f1337100388-Abstract.htmlMittelstadt B., Russell C., Wachter S. Explaining Explanations in AI. In: Proceedings of the Conference on Fairness. Accountability. and Transparency. Atlanta, GA, USA. 2019. P. 279–288. https://doi.org/10.1145/3287560.3287574Mittelstadt B., Russell C., Wachter S. Explaining Explanations in AI. In: Proceedings of the Conference on Fairness. Accountability. and Transparency. Atlanta, GA, USA. 2019. P. 279–288. https://doi.org/10.1145/3287560.3287574

The authors declare that there are no conflicts of interest present.