psymgouПсихологические наукиPsychological Sciences3033-64303033-6414Federal State University of Education10.18384/3033-6414-2026-2-92-104psymgou-1545Research ArticleТЕМА НОМЕРА: ЭКОЛОГИЧЕСКАЯ ОБРАЗОВАТЕЛЬНАЯ СРЕДАISSUE THEME: ECOLOGICAL EDUCATIONAL ENVIRONMENTФормирование умения распознавать фотографии лиц, сгенерированных искусственным интеллектомForming the Ability of Recognition of Faces in AI-Generated Photoshttps://orcid.org/0000-0003-0740-7260ВасильеваИ. В.VasilevaI. V.

Васильева Инна Витальевна (г. Тюмень) – доктор психологических наук, директор департамента, департамент психологии и дефектологии «Школа образования» ; профессор кафедры философии, иностранных языков и гуманитарной подготовки сотрудников ОВД 

Inna V. Vasileva (Tyumen) – Dr. Sci. (Psychology), Assoc. Prof., Head of the Department, Department of Psychology and Defectology, School of Education ; Prof., Department of Philosophy, Foreign Languages, Humanitarian Training

i.v.vasileva@utmn.ruhttps://orcid.org/0009-0000-8296-4919АсафоваА. Н.Asafova A. N.

Асафова Анастасия Николаевна (г. Тюмень) – независимый исследователь

Anastasia N. Asafova (Tyumen) – Independent researcher

asafova03@mail.ruhttps://orcid.org/0009-0008-7036-0155ГрищенкоД. С.GrischenkoD. S.

Грищенко Дарья Станиславовна (г. Тюмень) – независимый исследователь

Darya S. Grischenko (Tyumen) – Independent researcher

green4misty@gmail.comТюменский государственный университет; Тюменский институт повышения квалификации сотрудников МВД РоссииРоссияUniversity of Tyumen; Tyumen Institute for Advanced Training of Employees of the Ministry of Internal Affairs of RussiaRussian FederationНезависимый исследовательРоссияIndependent researcherRussian Federation2026300520260292104Copyright © Васильева И.В., Асафова А.Н., Грищенко Д.С., 20262026Васильева И.В., Асафова А.Н., Грищенко Д.С.Vasileva I.V., Asafova  A.N., Grischenko D.S.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.psymgou.ru/jour/article/view/1545Цель

Цель. Разработка стратегии обучения, позволяющей повысить умение распознавания реальных и сгенерированных ИИ фотографий.

Процедура и методы

Процедура и методы. В исследовании приняли участие 201 респондент в возрасте от 14 до 66 лет, студенты и специалисты различных профилей (m-45, f-156, Mвозраст=23,66, SD=8,65). Исследование выполнено в формате формирующего эксперимента с начальным и итоговым замерами. Экспериментальная группа участников исследования принимала участие в обучении, основанном на синтезе стратегий обратной связи и прямого указания. Для обработки количественных данных используются U-критерий Манна–Уитни и T-критерий Уилкоксона в программе Statsoft STATISTICA 10.0.1

Результаты

Результаты. На начальном замере экспериментальная и контрольная группы были эквивалентны. Были обнаружены статистически значимые различия между начальным и итоговым замерами в экспериментальной группе. Между замерами в контрольной группе также существуют статистически значимые различия, но в меньшей степени, чем у экспериментальной группы.

Теоретическая и/или практическая значимость заключается в том, что стратегия обучения умению распознавать фотографии, сгенерированные искусственным интеллектом, основанная на объединении стратегий обратной связи и прямого указания, действительно повышает эффективность распознавания генераций участниками. Результаты могут быть полезны для дальнейших исследований в области восприятия и взаимодействия с ИИ.

Aim

Aim. To develop a training strategy that enhances the ability to distinguish between real and AI-generated photos.

Methodology

Methodology. The study involved 201 respondents aged from 14 to 66 years, students and specialists of various profiles (m-45, f-156, Mage=23.66, SD=8.65). The study was conducted as a formative experiment with initial and final measurements. The experimental group participated in training based on a synthesis of feedback strategies and direct instruction. Quantitative data were processed using the Mann-Whitney U-test and Wilcoxon test in Statsoft STATISTICA 10.0.

Results

Results. At the initial measurement, the experimental and control groups were equivalent. Statistically significant differences were found between the initial and final measurements in the experimental group. There were also statistically significant differences in the control group, though to a lesser extent than in the experimental group.

Research implications

Research implications. The training strategy for recognizing AI-generated photos, based on combining feedback and direct instruction strategies, indeed improves participants’ detection accuracy. The results may be useful for further research in the field of perception and interaction with AI.

восприятие изображенийискусственный интеллект (ИИ)лицоопыт с ИИраспознавание изображенийAI experienceartificial intelligence (AI)faceimages perceptionimages recognitionИсследование выполнено при поддержке Министерства науки и высшего образования Российской Федерации в рамках проекта «Фундаментальные проблемы методики разработки и связанного с ней правового и этического регулирования в сфере применения систем и моделей искусственного интеллекта» (FEWZ-2024-0052).This study was supported by the Ministry of Science and Higher Education of the Russian Federation within the frame-work of a State assignment (FEWZ-2024-0052)ReferencesKatsyri J., de Gelder B., Takala T. Virtual faces evoke only a weak uncanny valley effect: An empirical investigation with controlled virtual face images // Perception. 2019. № 48 (10). P. 968–991. DOI: 10.1177/0301006619869134.Katsyri, J., de Gelder, B. & Takala, T. (2019). Virtual Faces Evoke Only a Weak Uncanny Valley Effect: An Empirical Investigation With Controlled Virtual Face Images. In: Perception, 48 (10), 968–991. DOI: 10.1177/0301006619869134.Natale D. A., Simonetti M. E., La Rocca S. Uncanny valley effect: A qualitative synthesis of empirical research to assess the suitability of using virtual faces in psychological research // Computers in Human Behavior Reports. 2023. № 10 (3). URL: https://www.sciencedirect.com (дата обращения: 10.10.2025). DOI: 10.1016/j.chbr.2023.100288.Natale, D. A., Simonetti, M. E. & La Rocca, S. (2023). Uncanny Valley Effect: A Qualitative Synthesis of Empirical Research to Assess the Suitability of Using Virtual Faces in Psychological Research. In: Computers in Human Behavior Reports, 10 (3). URL: https://www.sciencedirect.com (accessed: 10.10.2025). DOI: 10.1016/j.chbr.2023.100288.Miller E. J., Foo Y. Z., Mewton P. et al. How do people respond to computer-generated versus human faces? A systematic review and meta-analyses // Computers in Human Behavior Reports. 2023. № 10. URL: https://www.sciencedirect.com (дата обращения: 10.10.2025). DOI: 10.1016/j.chbr.2023.100283.Miller, E. J., Foo, Y. Z. & Mewton, P. et al. (2023). How Do People Respond to Computer-generated Versus Human Faces? A Systematic Review and Meta-analyses. In: Computers in Human Behavior Reports, 10. URL: https://www.sciencedirect.com (accessed: 10.10.2025). DOI: 10.1016/j.chbr.2023.100283.Korteling J. E., van de Boer-Visschedijk G. C. et al. Human- versus Artificial Intelligence // Frontiers in Artificial Intelligence. 2021. № 4. URL: https://www.frontiersin.org (дата обращения: 10.10.2025). DOI: 10.3389/frai.2021.622364.Korteling, J. E., van de Boer-Visschedijk, G. C. et al. (2021). Human- Versus Artificial Intelligence. In: Frontiers in Artificial Intelligence, 4. URL: https://www.frontiersin.org (accessed: 10.10.2025). DOI: 10.3389/frai.2021.622364.Köbis N. C., Doležalová B., Soraperra I. Fooled twice: People cannot detect deepfakes but think they can // iScience. 2021. № 24 (11). URL: https://www.cell.com/iscience (дата обращения: 10.10.2025).Köbis, N. C., Doležalová, B. & Soraperra, I. (2021). Fooled Twice: People Cannot Detect Deepfakes but Think They Can. In: iScience, 24 (11). URL: https://www.cell.com/iscience (accessed: 10.10.2025).Rathgeb C., Tolosana R., Vera-Rodriguez R. et al. Handbook of Digital Face Manipulation and Detection. From Deepfakes to Morphing attacks. Boston: Springer, 2022. 481 p. DOI: 10.1007/978-3-030-87664-7.Rathgeb, C., Tolosana, R. & Vera-Rodriguez, R. et al. (2022). Handbook of Digital Face Manipulation and Detection. From Deepfakes to Morphing attacks. Boston: Springer publ. DOI: 10.1007/978-3-030-87664-7.Connolly B. Digital Trust: Social Media Strategies to Increase Trust and Engage Customers. London: Bloomsbury Business, 2020. 256 p.Connolly, B. (2020). Digital Trust: Social Media Strategies to Increase Trust and Engage Customers. London: Bloomsbury Business publ.Fletcher J. Deepfakes, Artificial Intelligence, and Some Kind of Dystopia: The New Faces of Online Post-Fact Performance // Theatre Journal. 2018. Vol. 70. № 4. P. 455–471. DOI: 10.1353/tj.2018.0097.Fletcher, J. (2018). Deepfakes, Artificial Intelligence, and Some Kind of Dystopia: The New Faces of Online Post-Fact Performance. In: Theater Journal, 70, 4, 455–471. DOI: 10.1353/tj.2018.0097.Willis J., Todorov A. First Impression: Making Up Your Mind After a 100-Ms Exposure to a Face // Psychological Science. 2006. № 17 (7). P. 592–598. DOI: 10.1111/j.1467-9280.2006.01750.x.Willis, J. & Todorov, A. (2006). First Impression: Making Up Your Mind After a 100-Ms Exposure to a Face. In: Psychological Science, 17 (7), 592–598. DOI: 10.1111/j.1467-9280.2006.01750.x.Nemrodov D., Niemeier M., Mok J. N. Y. et al. The time course of individual face recognition: A pattern analysis of ERP signals // NeuroImage. 2016. № 132. P. 469–476. DOI: 10.1016/j.neuroimage.2016.03.006.Nemrodov, D., Niemeier, M. & Mok, J. N. Y. et al. (2016). The Time Course of Individual Face Recognition: A Pattern Analysis of ERP Signals. In: NeuroImage, 132, 469–476. DOI: 10.1016/j.neuroimage.2016.03.006.Miller E. J., Steward B. A., Witkower Z. et al. AI hyperrealism: Why AI faces are perceived as more real than human ones // Psychological Science. 2023. № 34 (12). P. 1390–1403. DOI: 10.1177/09567976231207095.Miller, E. J., Steward, B. A. & Witkower, Z. et al. (2023). AI Hyperrealism: Why AI Faces Are Perceived as More Real Than Human Ones. In: Psychological Science, 34 (12), 1390–1403. DOI: 10.1177/09567976231207095.Барабанщиков В. А. Восприятие выражений лица. М.: Институт психологии РАН, 2009. 448 с.Barabanshchikov, V. A. (2009). Perception of Facial Expressions. Moscow: Institute of Psychology of the RAS publ. (in Russ.).Diel A., Lalgi T., Schroeter I. C. et al. As good as chance: A systematic review and meta-analysis of human deepfake detection performance based on 56 papers // Expert Systems with Applications. 2024. № 252. URL: https://www.sciencedirect.com (дата обращения: 10.10.2025). DOI: 10.31219/osf.io/cxv4r.Diel, A., Lalgi, T. & Schroeter, I. C. et al. (2024). As Good as Chance: A Systematic Review and Meta-analysis of Human Deepfake Detection Performance Based on 56 Papers. In: Expert Systems with Applications, 252. URL: https://www.sciencedirect.com (accessed: 10.10.2025). DOI: 10.31219/osf.io/cxv4r.Diel A., Teufel M., Bäuerle A. Inability to detect deepfakes: Deepfake detection training improves detection accuracy, but increases emotional distress and reduces selfefficacy // OSF Preprints. 2024. URL: https://osf.io/preprints/osf (дата обращения: 10.10.2025). DOI: 10.2139/ssrn.5002585.Diel, A., Teufel, M. & Bäuerle, A. (2024). Inability to Detect Deepfakes: Deepfake Detection Training Improves Detection Accuracy, but Increases Emotional Distress and Reduces Selfefficacy. In: OSF Preprints. URL: https://osf.io/preprints/osf (accessed: 10.10.2025). DOI: 10.2139/ssrn.5002585.Mader B., Banks M. S., Farid H. Identifying computer-generated portraits: The importance of training and incentives // Perception. 2017. № 46 (9). P. 1062–1076. DOI: 10.1177/0301006617713633.Mader, B., Banks, M. S. & Farid, H. (2017). Identifying Computer-generated Portraits: The Importance of Training and Incentives. In: Perception, 46 (9), 1062–1076. DOI: 10.1177/0301006617713633.Hulzebosch N., Ibrahimi S., Worring M. Detecting CNN-generated facial images in real-world scenarios // Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. 2020. URL: https://arxiv.org/abs/2005.05632 (дата обращения: 10.10.2025). DOI: 10.1109/CVPRW50498.2020.00329.Hulzebosch, N., Ibrahimi, S. & Worring, M. (2020). Detecting CNN-generated Facial Images in Real-world Scenarios. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. URL: https://arxiv.org/abs/2005.05632 (accessed: 10.10.2025). DOI: 10.1109/CVPRW50498.2020.00329.Nightingale S. J., Farid H. AI-synthesized faces are indistinguishable from real faces and more trustworthy // Proceedings of the National Academy of Sciences. 2022. № 119 (8). URL: https://www.pnas.org. (дата обращения: 10.10.2025). DOI: 10.1073/pnas.2120481119.Nightingale, S. J. & Farid, H. (2022). Ai-synthesized Faces Are Indistinguishable From Real Faces and More Trustworthy. In: Proceedings of the National Academy of Sciences, 119 (8). URL: https://www.pnas.org (accessed: 10.10.2025). DOI: 10.1073/pnas.2120481119.Mohamed N. B., Bogdanel G., Moreno H. G. Is Training Useful to Detect Deepfakes? A Preliminary Study // Royal Society Open Science. 2025. № 12. URL: https://royalsocietypublishing.org. (дата обращения: 10.10.2025). DOI: 10.23919/CISTI58278.2023.10211622.Mohamed, N. B., Bogdanel, G. & Moreno, H. G. (2025). Is Training Useful to Detect Deepfakes? A Preliminary Study. In: Royal Society Open Science, 12. URL: https://royalsocietypublishing.org (accessed: 10.10.2025). DOI: 10.23919/CISTI58278.2023.10211622.Bray S. D., Johnson S. D., Kleinberg B. Testing human ability to detect ‘deepfake’ images of human faces // Journal of Cybersecurity. 2023. № 9 (1). URL: https://academic.oup.com/cybersecurity (дата обращения: 10.10.2025). DOI: 10.1093/cybsec/tyad011.Bray, S. D., Johnson, S. D. & Kleinberg, B. (2023). Testing Human Ability to Detect ‘deepfake’ Images of Human Faces. In: Journal of Cybersecurity, 9 (1). URL: https://academic.oup.com/cybersecurity (accessed: 10.10.2025). DOI: 10.1093/cybsec/tyad011.Somoray K., Miller D. J. Providing detection strategies to improve human detection of deepfakes: An experimental study // Computers in Human Behavior. 2023. № 149 (4). URL: https://www.sciencedirect.com (дата обращения: 10.10.2025). DOI: 10.1016/j.chb.2023.107917.Somoray, K. & Miller, D. J. (2023). Providing Detection Strategies to Improve Human Detection of Deepfakes: An Experimental Study. In: Computers in Human Behavior, 149 (4). URL: https://www.sciencedirect.com (accessed: 10.10.2025). DOI: 10.1016/j.chb.2023.107917.Yegemberdiyeva G., Amirgaliyev B. Study of AI Generated and Real Face Perception // Proceedings of the 2021 IEEE International Conference on Smart Information Systems and Technologies (SIST). 2021. URL: https://ieeexplore.ieee.org (дата обращения: 10.10.2025). DOI: 10.1109/SIST50301.2021.9465908.Yegemberdiyeva, G. & Amirgaliyev, B. (2021). Study Of AI Generated and Real Face Perception. In: Proceedings of the 2021 IEEE International Conference on Smart Information Systems and Technologies (SIST). 2021. URL: https://ieeexplore.ieee.org (accessed: 10.10.2025). DOI: 10.1109/SIST50301.2021.9465908.Васильева И. В., Асафова А. Н., Грищенко Д. С. Коммуникативные характеристики личности и оценка фотографий, сгенерированных искусственным интеллектом // Теоретическая и экспериментальная психология. 2025. Т. 18 (1). С. 27–44. DOI: 10.11621/TEP-25-02.Vasilyeva, I. V., Asafova, A. N. & Grishchenko, D. S. (2025). Communicative Characteristics of Personality and the Evaluation of Photographs Generated by Artificial Intelligence. In: Theoretical and Experimental Psychology, 18 (1), 27–44. DOI: 10.11621/TEP-25-02 (in Russ.).Devi S. P., Mishra A. K. The testing effect: Looking through implicit theories’ perspectives // Psychological Studies. 2023. № 68 (1). P. 92–100. DOI: 10.1007/s12646-022-00710-6Devi, S. P. & Mishra, A. K. (2023). The Testing Effect: Looking Through Implicit Theories’ Perspectives. In: Psychological Studies, 68 (1), 92–100. DOI: 10.1007/s12646-022-00710-6Ng Y.–L. An error management approach to perceived fakeness of deepfakes: The moderating role of perceived deepfake targeted politicians’ personality characteristics // Current Psychology. 2023. № 42 (1). P. 25658–25669. DOI: 10.1007/s12144-022-03621-x.Ng, Y.–L. (2023). An Error Management Approach to Perceived Fakeness of Deepfakes: The Moderating Role of Perceived Deepfake Targeted Politicians’ Personality Characteristics. In: Current Psychology, 42 (1), 25658–25669. DOI: 10.1007/s12144-022-03621-x.Schetinger V., Oliveira M. M., da Silva R. et al. Humans Are Easily Fooled by Digital Images // Computers & Graphics. 2015. № 68. URL: https://arxiv.org/abs/1509.05301 (дата обращения: 10.10.2025). DOI: 10.48550/arXiv.1509.05301.Schetinger, V., Oliveira, M. M. & da Silva, R. et al. (2015). Humans Are Easily Fooled by Digital Images. In: Computers & Graphics, 68. URL: https://arxiv.org/abs/1509.05301 (accessed: 10.10.2025). DOI: 10.48550/arXiv.1509.05301.

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