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2026-03-04
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Articles
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DETECTION ALGORITHMS AND MODELS FOR SYNTHETICALLY GENERATED VISUAL MEDIA
Khakimbekov Doniyorbek
PhD student of New Uzbekistan University
##semicolon## Artificial intelligence, visual object detection, computer vision, deep learning, neural networks, spatiotemporal analysis, digital security.
सार
The rapid development of artificial intelligence technologies has unprecedentedly expanded the capabilities for the automatic generation and manipulation of visual content. This, in turn, has led to a proliferation of deepfakes and other artificially generated objects that pose a significant threat to the reliability of digital information. This paper analyzes improved algorithms and models that enable the high-accuracy detection of artificially generated visual objects. Within the framework of this study, in contrast to traditional approaches that rely solely on spatial features, a hybrid architecture is proposed that evaluates temporal discontinuities and logical inconsistencies between consecutive video frames. The developed model was tested on prominent open-source datasets, and the results confirmed its superior effectiveness in detecting artificial manipulations compared to baseline methods. The proposed algorithmic solution can be practically implemented to enhance digital security and verify information authenticity across various information systems.
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1. Deressa, D. W., Lambert, P., Van Wallendael, G., Atnafu, S., & Mareen, H. (2024). Improved deepfake video detection using convolutional vision transformer. In 2024 IEEE Gaming, Entertainment, and Media Conference (GEM), 492-497.
2. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
3. Kaddar, B., Fezza, S. A., & Serra-Sagrista, J. (2024). Deepfake detection using spatiotemporal transformer. ACM Transactions on Multimedia Computing, Communications and Applications, 20(11).
4. Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Niessner, M. (2019). Faceforensics++: Learning to detect manipulated facial images. In Proceedings of the IEEE/CVF International Conference on Computer Vision.
5. Thakre, A., et al. (2025). CAST: Cross-Attentive Spatio-Temporal feature fusion for Deepfake detection. arXiv preprint arXiv:2506.21711.
6. Zhang, Z., & Laghari, A. A. (2025). Real-Time deepfake detection via gaze and blink patterns: A transformer framework. Computers, Materials & Continua, 85(1), 1457-1493.
