AAI_2025_Capstone_Chronicles_Combined

22 (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2010.11929 Frank, J., Eisenhofer, T., Schönherr, L., Fischer, A., Kolossa, D., & Holz, T. (2020, March 19). Leveraging frequency analysis for deep fake image recognition. arXiv.org. https://arxiv.org/abs/2003.08685 Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014b). Generative adversarial networks. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1406.2661 Google DeepMind. (2023, August 29). Identifying AI-generated images with SynthID. https://deepmind.google/blog/identifying-ai-generated-images-with-synthid Ham, S., Hoang, V., & Park, C. (2024). Ensemble Approach for Image Recompression Based Forgery Detection. IEEE Access, 12, 196442–196454. https://doi.org/10.1109/access.2024.3521290 He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778). https://doi.org/10.1109/CVPR.2016.90 Heo, Y., Yeo, W., & Kim, B. (2022). DeepFake detection algorithm based on improved vision transformer. Applied Intelligence, 53(7), 7512–7527. https://doi.org/10.1007/s10489-022-03867-9

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