Open Access
| Issue |
ITM Web Conf.
Volume 88, 2026
The 2026 International Conference on Artificial Intelligence, Big Data and Computer Science (AIBDCS 2026)
|
|
|---|---|---|
| Article Number | 01027 | |
| Number of page(s) | 7 | |
| Section | Artificial Intelligence, Big Data and Computer Science | |
| DOI | https://doi.org/10.1051/itmconf/20268801027 | |
| Published online | 27 July 2026 | |
- Z. Li, X. Zhang, Y. Pu, Y. Wu, S. Ji, A survey of multimodal deepfake and detection technologies. Journal of Computer Research and Development, 2023(06) [Google Scholar]
- W. D. Yao, P. C. Li, Y. Zhao, H. C. Wu, Review of research on face deepfake detection methods. Journal of Image and Graphics, 30(7), 2025 [Google Scholar]
- Y. Li, X. Zhang, Z. Wang, Analyzing temporal coherence for deepfake video detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 9876–9890 (2023) [Google Scholar]
- M. Li, B. Liu, Y. Hu, L. Zhang, S. Wang, Deepfake Detection Using Robust Spatial and Temporal Features from Facial Landmarks, in 2021 IEEE International Workshop on Biometrics and Forensics (IWBF), Rome, Italy, 2021, pp. 1–6 [Google Scholar]
- Y. Jugade, Z. Syed, C. Dcosta, et al., Deepfake detection via spatial–temporal deep networks: leveraging CNNs and LSTMs for enhanced accuracy. Discov Appl Sci, 8, 179 (2026) [Google Scholar]
- Y. Xu, J. Liang, L. Sheng, et al., Learning Spatiotemporal Inconsistency via Thumbnail Layout for Face Deepfake Detection. Int J Comput Vis, 132, 5663–5680 (2024) [Google Scholar]
- Z. Cheng, C. Chen, Y. Zhou, X. Hu, Mining Temporal Inconsistency with 3D Face Model for Deepfake Video Detection, in: Q. Liu, et al. Pattern Recognition and Computer Vision. PRCV 2023, Lecture Notes in Computer Science, vol 14431, Springer, Singapore, 2024 [Google Scholar]
- L. Kuang, Y. Wang, T. Hang, et al., A dual-branch neural network for DeepFake video detection by detecting spatial and temporal inconsistencies. Multimed Tools Appl, 81, 42591–42606 (2022) [Google Scholar]
- X. Wang, W. Song, C. Hao, F. Liu, Deepfake Detection Method Based on Spatio-Temporal Information Fusion. Computers, Materials and Continua, 83(2), 16 April 2025 [Google Scholar]
- Y. Wang, J. Hu, X. Wu, J. Xiao, Y. Chen, X. Liao, Compressed deepfake video detection method based on facial motion inconsistency. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 37(03), 2025 [Google Scholar]
- W. Qin, T. Lu, L. Zhang, S. Peng, D. Wan, Multi-Branch Deepfake Detection Algorithm Based on Fine-Grained Features. Computers, Materials and Continua, 77(1), 31 October 2023 [Google Scholar]
- G. Petmezas, V. Vanian, K. Konstantoudakis, et al., Video deepfake detection using a hybrid CNN-LSTM-Transformer model for identity verification. Multimed Tools Appl, 84, 40617–40636 (2025) [Google Scholar]
- W. Al Kishri, J. H. Yousif, M. Al Bahri, et al., A comparative study of deepfake facial manipulation technique using generative adversarial networks. Discov Artif Intell, 5, 109 (2025) [Google Scholar]
- G. Petmezas, V. Vanian, M. P. Rufete, E. E. I. Almaloglou, D. Zarpalas, A Dual-Branch Fusion Model for Deepfake Detection Using Video Frames and Microexpression Features. J. Imaging, 11, 231 (2025) [Google Scholar]
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