HUMAN PERCEPTION VERSUS ARTIFICIAL INTELLIGENCE IN DEEPFAKE VIDEO DETECTION: AN EMPIRICAL STUDY

abbaz primbetov; Sarvar Maxmudjanov; Asalbanu Alimbaeva

Авторы

abbaz primbetov tatu
Sarvar Maxmudjanov
Asalbanu Alimbaeva

Ключевые слова:

Deepfake detection, human perception, survey, misinformation, media literacy.

Аннотация

Deepfake technology has become a growing threat to information integrity and digital trust. This study examines how accurately humans can identify deepfake videos through an empirical survey conducted among 492 students of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi. Participants watched five short videos and classified each as real or fake. The results show that respondents achieved an average accuracy of 57%, while 43% misclassified at least one video. Moreover, 46% of participants were unfamiliar with the term “deepfake.”

Библиографические ссылки

Diel, A., Lalgi, T., Schröter, I. C., MacDorman, K. F., Teufel, M., & Bäuerle, A. (2024). Human performance in detecting deepfakes: A systematic review and meta-analysis of 56 papers. Computers in Human Behavior Reports, 16, 100538.

Abbaz, P., & Navruz, A. (2025). A comparative study of activation functions in deep learning models. Al-Farg’oniy avlodlari, 1(3), 80-84.

Primbetov A (2025). Deepfake detection using a hybrid resnext and lstm architecture. Al-farg’oniy avlodlari, 1(2), 87-94.

Primbetov (2025). DEEPFAKE TECHNOLOGY: THREAT LANDSCAPE, DETECTION TECHNIQUES, AND ETHICAL GOVERNANCE. Al-Farg’oniy avlodlari, 1(2), 106-112.

Primbetov A (2025). Students survey list. Google Forms.

M. Tolosana, R. Vera-Rodriguez, J. Fierrez, A. Morales, and J. Ortega-Garcia, “Deepfakes and beyond: A survey of face manipulation and fake detection,” *Information Fusion*, vol. 64, pp. 131–148, 2020.

A. Diel et al., “Human performance in detecting deepfakes: A systematic review and meta-analysis of 56 papers,” *Computers in Human Behavior Reports*, vol. 16, p. 100538, 2024.

F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in *Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)*, 2017, pp. 1251–1258.

M. Tan and Q. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in *Proc. Int. Conf. Machine Learning (ICML)*, 2019, pp. 6105–6114.

S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” in *Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)*, 2017, pp. 1492–1500.

W. van Gansbeke, A. Dhoedt, and J. Van de Weijer, “Temporal awareness in deepfake detection,” *IEEE Trans. Biometrics, Behavior, and Identity Science*, vol. 3, no. 2, pp. 176–187, 2021.