References
- Cao, X., & Gong, N. (2021). Understanding the Security of Deepfake Detection. Digital Forensics and Cyber Crime, ICDF2C 2021, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Vol. 441, 360-378. Springer, Cham. Available at: https://doi.org/10.1007/978-3-031-06365-7_22.
- Ghodke, S. (2024). Ethical Implications of Deepfake Technology. International Journal for Multidisciplinary Research. Available at: https://doi.org/10.36948/ijfmr.2024.v06i05.28312.
- Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63, 139-144. Available at: https://doi.org/10.1145/3422622.
- Hancock, J., & Bailenson, J. (2021). The Social Impact of Deepfakes. Cyberpsychology, behavior and social networking, 24(3), 149-152. Available at: https://doi.org/10.1089/cyber.2021.29208.jth.
- Heidari, A., Navimipour, N., 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. Available at: https://doi.org/10.1002/widm.1520.
- Khan, S., & Dang-Nguyen, D. (2023). Deepfake Detection: A Comparative Analysis. ArXiv, abs/2308.03471. Available at: https://doi.org/10.48550/arXiv.2308.03471.
- Kharvi, P. (2024). Understanding the Impact of AI-Generated Deepfakes on Public Opinion, Political Discourse, and Personal Security in Social Media. IEEE Security & Privacy, 22, 115-122. Available at: https://doi.org/10.1109/MSEC.2024.3405963.
- Le, T.-N., Nguyen, H.H., Yamagishi, J., & Echizen, I. (2021). OpenForensics: Multi-Face Forgery Detection and Segmentation In-The-Wild Dataset [V.1.0.0] (1.0.0) [Data set]. International Conference on Computer Vision (ICCV), Zenodo. Available at: https://doi.org/10.5281/zenodo.5528418
- Pan, D., Sun, L., Wang, R., Zhang, X., & Sinnott, R. (2020). Deepfake Detection through Deep Learning. 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), 134-143. Available at: https://doi.org/10.1109/BDCAT50828.2020.00001.
- Rana, M., Nobi, M., Murali, B., & Sung, A. (2022). Deepfake Detection: A Systematic Literature Review. IEEE Access, 10, 25494-25513. Available at: https://doi.org/10.1109/access.2022.3154404.
- Sudhakar, K., & Shanthi, M. (2023). Deepfake: An Endanger to Cyber Security. 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS), 1542-1548. Available at: https://doi.org/10.1109/ICSCSS57650.2023.10169246.
- Vaccari, C., & Chadwick, A. (2020). Deepfakes and Disinformation: Exploring the Impact of Synthetic Political Video on Deception, Uncertainty, and Trust in News. Social Media + Society, 6. Available at: https://doi.org/10.1177/2056305120903408.
- Rana, P., & Bansal, S. (2024). Exploring Deepfake Detection: Techniques, Datasets and Challenges. International Journal of Computing and Digital Systems. Available at: https://doi.org/10.12785/ijcds/160156.
- www.kaggle.com/datasets/manjilkarki/deepfake-and-real-images, accessed on November 12, 2024.
- www.kaggle.com/code/mdsifatullahsheikh/deepfake-detection-with-densenet, accessed on November 12, 2024.
- www.kaggle.com/code/kameshrasu/deep-fake-detection-with-efficientnet, accessed on November 12, 2024.
- www.kaggle.com/code/seanstepanek/deepfake-detection-cnn-5-layers, accessed on November 12, 2024.
