AAI_2025_Capstone_Chronicles_Combined

24 Lin, M., Chen, Q., & Yan, S. (2013b). Network in network. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1312.4400 Loshchilov, I., & Hutter, F. (2019). Decoupled weight decay regularization. In 7th International Conference on Learning Representations. https://openreview.net/forum?id=Bkg6RiCqY7 Mittal, A., Soundararajan, R., & Bovik, A. C. (2012). Making a “Completely blind” image quality analyzer. IEEE Signal Processing Letters, 20(3), 209–212. https://doi.org/10.1109/lsp.2012.2227726 Mustak, M., Salminen, J., Mäntymäki, M., Rahman, A., & Dwivedi, Y. K. (2022). Deepfakes: Deceptions, mitigations, and opportunities. Journal of Business Research, 154, 113368. https://doi.org/10.1016/j.jbusres.2022.113368 N, N. V., D, N. P., Bh, N. M. C., Channappayya, S. S., & Medasani, S. S. (2015). Blind image quality evaluation using perception based features. In Proceedings of the Twenty First National Conference on Communications (NCC) (pp. 1–6). https://doi.org/10.1109/ncc.2015.7084843 Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., … Chintala, S. (2019). PyTorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems (Vol. 32, pp. 8024–8035).

381