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Pengembangan Sistem Deteksi dan Pencegahan Deepfakes Pada Windows: Keamanan Distribusi Konten

Author


Muh. Alif(1Mail), Humaera Hamuddin(2), Rakhmadi Rahman(3),
(1) Sistem Informasi Institut Teknologi Bacharuddin Jusuf Habibie Parepare,Indonesia,
(2) Sistem Informasi Institut Teknologi Bacharuddin Jusuf Habibie Parepare,Indonesia,
(3) Sistem Informasi Institut Teknologi Bacharuddin Jusuf Habibie Parepare,Indonesia,

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Available online: 2024-07-29  |  Published : 2024-07-29
Copyright (c) 2024 Muh. Alif, Humaera Hamuddin, Rakhmadi Rahman
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Abstract


Deepfakes detection and prevention systems are needed nowadays. The publication of content made from AI manipulation is a threat in the digital world. This system is used to minimize the distribution of content that can be used by certain individuals to bring down a person or group. The detection carried out focuses on machine learning as the main system. Blockchain as a system that performs storage and verification based on comparing the original content that has been loaded with content that is considered manipulated. It is transparent and has various advantages that provide security for data and users. With the appropriate dataset in machine learning, detection can be done as needed. Machine learning does not reject many datasets to train the model created to be smarter in detection. Deeepfakes pose a significant threat to information dissemination. It is necessary to do good and correct prevention and detection so that disinformation does not occur and also so that there is no hoax information.


Keywords


Windows, Deepfakes, Machine learning, Blockchain, Detection and prevention systems, Content distribution, Hoax

References


Chen, Y., & Liu, J. (2020). Deepfake Detection: Current Challenges and Next Steps. Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security, 71-76.

Dorri, A., Kanhere, S. S., Jurdak, R., & Gauravaram, P. (2019). Blockchain Meets Deep Learning: Security and Privacy Challenges. IEEE Access.

Learning Techniques. Journal of Information Security and Applications, 54, 102524.

Li, D., Xu, Q., & Wang, C. (2021). DeepFake Detection: A Survey. IEEE Transactions on Information Forensics and Security. doi:10.1109/TIFS.2021.3056956

Qiu, Z., Yao, T., & Mei, T. (2019). Combating Deepfakes: A Survey on Detection Techniques and Challenges. arXiv preprint arXiv:1912.00678.

Ramachandra, R., Mridha, S., Saha, G. K., & Sural, S. (2020). TrustNet: A Decentralized Architecture for Detecting Fake Faces in Videos using Blockchain. In Proceedings of the 2020 IEEE International Conference on Blockchain (Blockchain).

Reddy, K. M. V. P., Ramachandra, M. S., & Usha, M. N. S. (2021). Blockchain-Based Approach for Securing Video Content against Deepfake Attacks. IEEE Access, 9, 130826-130838. doi:10.1109/ACCESS.2021.3114073.

Rssler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Niener, M. (2020). DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM).

Shen, H., Xu, Y., & Xu, Z. (2020). Deep Learning for Fake News Detection Using Machine

Yaga, D., Mell, P., Roby, N., & Scarfone, K. (2019). Blockchain Technology Overview. National Institute of Standards and Technology, U.S. Department of Commerce.


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