Performance of Hybrid Models with VGG16-XGBoost and VGG16-LightGBM on Fake Face Image Recognition
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5504-2_29
Reference10 articles.
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2. Hanady, S.: A detection of deep fake in face images using deep learning. Wasit J. Comput. Math. Sci. 1(4), 60–71 (Dec. 2022). https://doi.org/10.31185/wjcm.92
3. Rafique, R., Gantassi, R., Amin, R., et al.: Deep fake detection and classification using error-level analysis and deep learning. Sci. Rep. 13, 7422 (2023). https://doi.org/10.1038/s41598-023-34629-3
4. Thongsuwan, S., Jaiyen, S., Padcharoen, A., Agarwal, P.: ConvXGB: A new deep learning model for classification problems based on CNN and XGBoost. Nucl. Eng. Technol. , 53(2), 522–531 (2021). ISSN 1738–5733
5. Jia, X., Xiao, B., Zhao, Z., Ma, L., Wang, N.: Bearing fault diagnosis method based on CNN-LightGBM. In: IOP Conf. Series: Materials Science and Engineering (2021). https://doi.org/10.1088/1757-899X/1043/2/022066
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