AAI_2025_Capstone_Chronicles_Combined

23 Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1502.03167 Karki. (2021). Deepfake and real images. https://www.kaggle.com/datasets/manjilkarki/deepfake-and-real-images Ke, J., Wang, Q., Wang, Y., Milanfar, P., & Yang, F. (2021). MUSIQ: Multi-scale Image quality Transformer. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 5128–5137. https://doi.org/10.1109/iccv48922.2021.00510 Khalid, H., & Woo, S. S. (2020). OC-FakeDect: Classifying deepfakes using one-class variational autoencoder. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 2794–2803). https://doi.org/10.1109/CVPRW50498.2020.00336 Kingma, D. P., & Welling, M. (2013). Auto-Encoding variational Bayes. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1312.6114 Le, T., Nguyen, H. H., Yamagishi, J., & Echizen, I. (2021). OpenForensics: Multi-Face Forgery Detection and Segmentation In-The-Wild Dataset [V.1.0.0] [Dataset]. In Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.5528418 Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791

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