ARCHIVES

1. Heidari, A., Jafari Navimipour, N., Dag, H. and Unal, M., 2024. Deepfake detection using deep learning methods: A systematic and
comprehensive review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 14(2), p.e1520.
2. Y. Li, X. Yang, P. Sun, H. Qi and S. Lyu, "Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics," 2020 IEEE/CVF
Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 3204-3213, doi:
10.1109/CVPR42600.2020.00327. keywords: {Videos;Visualization;Image color analysis;Decoding;YouTube;Training;Detection
algorithms},
3. Rana, M.S., Nobi, M.N., Murali, B. and Sung, A.H., 2022. Deepfake detection: A systematic literature review. IEEE access, 10, pp.25494-
25513.4. Dolhansky, B., Bitton, J., Pflaum, B., Lu, J., Howes, R., Wang, M. and Ferrer, C.C., 2020. The deepfake detection challenge (dfdc)
dataset. arXiv preprint arXiv:2006.07397.
5. Ahmed, S.R., Sonuç, E., Ahmed, M.R. and Duru, A.D., 2022, June. Analysis survey on deepfake detection and recognition with convolutional
neural networks. In 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (pp. 1-
7). IEEE.
6. Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W. and Yu, N., 2021. Multi-attentional deepfake detection. In Proceedings of the IEEE/CVF
conference on computer vision and pattern recognition (pp. 2185-2194).
7. Rafique, R., Gantassi, R., Amin, R., Frnda, J., Mustapha, A. and Alshehri, A.H., 2023. Deep fake detection and classification using error-
level analysis and deep learning. Scientific reports, 13(1), p.7422.
8. Guarnera, L., Giudice, O. and Battiato, S., 2020. Deepfake detection by analyzing convolutional traces. In Proceedings of the IEEE/CVF
conference on computer vision and pattern recognition workshops (pp. 666-667).
9. Alkishri, W.A.S.I.N. and Al-Bahri, M.A.H.M.O.O.D., 2023. Deepfake image detection methods using discrete fourier transform analysis and
convolutional neural network. Journal of Jilin University (Engineering and Technology Edition), 42(2).
10. Raza, A., Munir, K. and Almutairi, M., 2022. A novel deep learning approach for deepfake image detection. Applied Sciences, 12(19), p.9820.
11. Nirkin, Y., Wolf, L., Keller, Y. and Hassner, T., 2021. Deepfake detection based on discrepancies between faces and their context. IEEE
transactions on pattern analysis and machine intelligence, 44(10), pp.6111-6121.