[1] Mas Montserrat, D., Hao, H., Yarlagadda, S. K., Baireddy, S., Shao, R., Horváth, J., ... & Delp, E. J.
(2020). Deepfakes Detection with Automatic Face Weighting. 2020 IEEE/CVF Conference on
Computer Vision and Pattern Recognition Workshops (CVPRW), 2851-2859.
[2] Pashine, S., Mandiya, S., Gupta, P. K., & Sheikh, R. (2021). Deep Fake Detection: Survey of Facial
Manipulation Detection Solutions. ArXiv, abs/2106.12605.
[3] Groh, M., Epstein, Z., Firestone, C., & Picard, R. W. (2021). Deepfake detection by human crowds,
machines, and machine-informed crowds. Proceedings of the National Academy of Sciences of the
United States of America, 119.
[4] Dolhansky, B., Bitton, J., Pflaum, B., Lu, J., Howes, R., Wang, M., & Canton-Ferrer, C. (2020). The
DeepFake Detection Challenge Dataset. ArXiv, abs/2006.07397.
[5] Rössler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Nießner, M. (2019). FaceForensics++:
Learning to Detect Manipulated Facial Images. 2019 IEEE/CVF International Conference on
Computer Vision (ICCV), 1-11.
[6] Ismail, A., Elpeltagy, M. S., Zaki, M. S., & Eldahshan, K. A. (2021). Deepfake video detection:
YOLO-Face convolution recurrent approach. PeerJ Computer Science, 7.
[7] Vidyavathi, B. M., Ahmed, A. F., Ayain, L., & Fatima, S. M. (2024). Deepfake Detection using Deep
Learning. International Journal of Advanced Research in Science, Communication and Technology.
[8] Heidari, A., Navimipour, N. J., Dag, H., & Unal, M. (2023). Deepfake detection using deep learning
methods: A systematic and comprehensive review. Wiley Interdisciplinary Reviews: Data Mining and
Knowledge Discovery, 14.
[9] Shaikh, M. S., Nirankari, L., Pardeshi, V., Sharma, R., & Kale, S. (2023). DEEPFAKE DETECTION
USING DEEP LEARNING (CNN+LSTM). International Journal of Scientific Research in
Engineering and Management.
[10] Naskar, G., Mohiuddin, S. M., Malakar, S., Cuevas, E., & Sarkar, R. (2024). Deepfake Detection using
Deep Feature Stacking and Meta-learning. Heliyon.
[11] Zi, B., Chang, M., Chen, J., Ma, X., & Jiang, Y. G. (2020). WildDeepfake: A Challenging Real-World
Dataset for Deepfake Detection. Proceedings of the 28th ACM International Conference on
Multimedia.
[12] Chen, L., Zhang, Y., Song, Y., Liu, L., & Wang, J. (2022). Self-supervised Learning of Adversarial
Example: Towards Good Generalizations for Deepfake Detection. 2022 IEEE/CVF Conference on
Computer Vision and Pattern Recognition (CVPR), 18689-18698.
[13] Rana, M. S., Nobi, M. N., Murali, B., & Sung, A. H. (2022). Deepfake Detection: A Systematic
Literature Review. IEEE Access, 10, 25494-25513.
[14] Ismail, A., Elpeltagy, M. S., Zaki, M. S., & Eldahshan, K. A. (2021). A New Deep Learning-Based
Methodology for Video Deepfake Detection Using XGBoost. Sensors, 21.
[15] Ramachandran, S., Nadimpalli, A. V., & Rattani, A. (2021). An Experimental Evaluation on Deepfake
Detection using Deep Face Recognition. 2021 International Carnahan Conference on Security
Technology (ICCST), 1-6.
[16] Raza, M. A., & Malik, K. M. (2023). Multimodaltrace: Deepfake Detection using Audiovisual
Representation Learning. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Workshops (CVPRW), 993-1000.
[17] Rekha, G., & Shashi, P. (2023). Deepfake: Creation and Detection using Deep Learning. International
Journal for Research in Applied Science and Engineering Technology.
[18] Amerini, I., Galteri, L., Caldelli, R., & Del Bimbo, A. (2019). Deepfake video detection through optical
flow based CNN. Proceedings of the IEEE/CVF International Conference on Computer Vision
Workshops.
