DEEPFAKE CONTENT TYPES AND THEIR GENERATION METHODS
DOI:
https://doi.org/10.65164/7jsgx834Kalit so‘zlar:
Deepfake, Artificial Intelligence, Identity Swapping, Expression Manipulation, Facial Attribute Editing, TTS, Image/Video SynthesisAbstrak
Deepfake technology, powered by artificial intelligence, enables the manipulation of media content in multiple ways, including identity swapping, facial expression alteration, attribute modification, background replacement, and realistic speech or image synthesis. These manipulations can create highly convincing yet artificial content, posing challenges for digital media authentication, forensic analysis, and cybersecurity. Understanding the various deepfake types and their generation techniques is essential for designing robust detection methods and improving the reliability of automated verification systems
Havolalar
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.
2. 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.
3. F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in *Proc. IEEE
Conf. Computer Vision and Pattern Recognition (CVPR)*, 2017, pp. 1251–1258.
4. M. Tan and Q. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,”
in *Proc. Int. Conf. Machine Learning (ICML)*, 2019, pp. 6105–6114.
5. 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. 1492–1500.445
6. 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.
7. S. Tipper, H. F. Atlam, and H. S. Lallie, “An investigation into the utilisation of CNN with LSTM
for video deepfake detection,” *Applied Sciences*, vol. 14, no. 21, p. 9754, 2024.
8. Maxmudjanov Sarvar, Primbetov Abbaz Muratbay Uli, and Naimov Axadjon Tojimirza O‘G‘Li.
"DEEPFAKE DETECTION USING A HYBRID RESNEXT AND LSTM ARCHITECTURE."
Al-Farg’oniy avlodlari 1.2 (2025): 87-94.