AAI_2025_Capstone_Chronicles_Combined

25 Soudy, A. H., Sayed, O., Tag-Elser, H., Ragab, R., Mohsen, S., Mostafa, T., Abohany, A. A., & Slim, S. O. (2024). Deepfake detection using convolutional vision transformers and convolutional neural networks. Neural Computing and Applications, 36(31), 19759–19775. https://doi.org/10.1007/s00521-024-10181-7 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929–1958. Talebi, H., Milanfar, P., Talebi, H., & Milanfar, P. (2018). NIMA: Neural Image Assessment. IEEE Transactions on Image Processing, 27(8), 3998–4011. https://doi.org/10.1109/tip.2018.2831899 Wang, S.-Y., Wang, O., Zhang, R., Owens, A., & Efros, A. A. (2020). CNN-generated images are surprisingly easy to spot… for now. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8695–8704). Wang, X., Guo, H., Hu, S., Chang, M., & Lyu, S. (2022). GAN-generated Faces Detection: A survey and new perspectives. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2202.07145 Yu, N., Davis, L., & Fritz, M. (2018, November 20). Attributing fake images to GANs: Learning and Analyzing GAN fingerprints. arXiv.org. https://arxiv.org/abs/1811.08180

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