EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 78 (2025) ITM Web Conf., 78 (2025) 01025 References
Error
  • The authentification system partialy failed, sorry for the inconvenience. Please try again later.
Open Access
Issue
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
Article Number 01025
Number of page(s) 14
Section Deep Learning and Reinforcement Learning – Theories and Applications
DOI https://doi.org/10.1051/itmconf/20257801025
Published online 08 September 2025
  1. Qu, Z. M., Yin, Q. L., Sheng, Z. Q., et al.: ‘A review of face deepfake active defense technology.’ Chinese Journal of Image and Graphics, 2024, 29(02): 318–342 [Google Scholar]
  2. Li, J. M.: ‘Research on key technologies of deepfake face image defense.’University of Science and Technology of China, 2024. DOI: 10.27517/d.cnki.gzkju.2024.000632. [Google Scholar]
  3. Zhou, W. B., Zhao, H. Q., Liu, H. G., et al.: ‘Artificial Intelligence.’ 2023 (03):26–39+51.DOI:10.16453/j.2096-5036.2023.03.003 [Google Scholar]
  4. Zhou, W. B., Zhang, W. M., Yu, N. H., et al.: ‘Signal Processing.’ 2021,37(12):2338–2355. [Google Scholar]
  5. Guo, Q., Pang, S., Chen, Z., et al.: ‘Towards robust DeepFake distortion attack via adversarial autoaugment.’ Neurocomputing, 2025, 617 [Google Scholar]
  6. Gu, Y., Zhao, X., Gong, C., et al.: ‘Deepfake Video Detection Using Audio-Visual Consistency.’//International Workshop on Digital-forensics and Watermarking. 2020 [Google Scholar]
  7. Zhao, Y., Gao, M.C.H., Yao, W. D., et al.: ‘Survey of fake face detection technology based on deep learning.’ Computer System Applications, 1–17. 2025 [Google Scholar]
  8. Wang, Y. K., Zhang, B. L., Wang, C. H., et al.: ‘Research on Deep Face Forgery Detection Method Based on Improved ResNet34.’ Journal of Hebei University of Technology. 2024, 53(6): 44–51 [Google Scholar]
  9. Yang, H.Y., Li, X.H., Hu, Z.: ‘Review of deepfake face generation and detection technology.’ Journal of Huazhong University of Science and Technology (Natural Science Edition), 1–19. 2025 [Google Scholar]
  10. Hong, Y.T., Chen, B. J.: ‘Imperceptible Active Defense Algorithm Against Second Face Attribute Editing.’ Journal of Computer-Aided Design and Graphics, 1–10 2025 [Google Scholar]
  11. Han, F.: ‘Research on Robust Federated Learning Algorithm for Local Poisoning Attack.’ Tianjin University of Technology, 2024. [Google Scholar]
  12. Liu, H. G.: ‘Research on face deepfake defense method.’ University of Science and Technology of China, 2024 DOI:10.27517/d.cnki.gzkju.2024.000651 [Google Scholar]
  13. Nasser, M., Arshad, I.N., Ali, A., et al.: ‘A systematic review of multimodal fake news detection on social media using deep learning models. ’ Results in Engineering, 2025, 26 104752–104752 [Google Scholar]
  14. Du, W., Li, J., Zhou, J., et al.: ‘Mitigating the Proliferation of Fake Image-Text Reviews: A Two-Tier Intra- and Inter-Modal Fusion Framework.’ International Journal of Electronic Commerce, 2025, 29 (2): 304–332 [Google Scholar]
  15. Su, X., Yang, J., Wu, J., et al.: ‘Hy-DeFake: Hypergraph neural networks for detecting fake news in online social networks.’ Neural Networks, 2025, 187 107302–107302 [Google Scholar]
  16. Guo, Y., Hu, S., Li, Y., et al.: ‘Dmvae: a dual-stream multi-modal variational autoencoder for multi-task fake news detection. ’ Pattern Analysis and Applications, 2025, 28 (2) [Google Scholar]
  17. Shamayleh, A. S. A., Riasat, H., Alluhaidan, S. A., et al.: ‘Novel transfer learning based acoustic feature engineering for scene fake audio detection.’ Scientific Reports, 2025, 15 (1): 8066–8066 [Google Scholar]
  18. Man, Q., Cho, I. Y.: ‘Exposing Face Manipulation Based on Generative Adversarial Network-Transformer and Fake Frequency Noise Traces.’ Sensors, 2025, 25 (5): 1435 [Google Scholar]
  19. LekshmiAmmal, R. H., Madasamy, K. A.: ‘A reasoning based explainable multimodal fake news detection for low resource language using large language models and transformers. ’Journal of Big Data, 2025, 12 (1): 46–46 [Google Scholar]
  20. Hou, J., Tan, Z., Zhang, S., et al.: ‘Detecting fake review intentions in the review context: A multimodal deep learning approach.’ Electronic Commerce Research and Applications, 2025, 70 101485–10148 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.