Problem Statement
The rise of deepfake technology poses a significant threat to the authenticity of
visual content. Detecting deepfake videos in real-time is crucial for maintaining
trust in media and preventing the spread of misinformation.
Abstract
This project focuses on developing a real-time deepfake detection system.
Leveraging machine learning and computer vision techniques, the system will
analyze video content to identify signs of manipulation or synthetic elements,
providing real-time alerts for potentially deceptive media.
Outcome
An effective real-time deepfake detection system that enhances media
authenticity and helps prevent the spread of misinformation.
Reference
Recent advances in DeepFake face-swapping technology have made it simple to create fake videos that appear remarkably real. Since it has been employed in numerous instances for deceit, extortion, and the falsification of facts, its widespread use has generated a huge social, security, and political risk. Its use on websites and social media has become more widespread. Detecting this crime is becoming more and more important due to the potential harm false videos may inflict on a global scale. This research offers a method for building a deep learning model that really can tell the difference between authentic and false videos. The article describes how to create new models based on the VGG16 neural network, a previously created neural network that does image categorization, using transfer learning in the computer vision field. Deep learning is still becoming better at both producing and spotting DeepFakes. DeepFake detection algorithms are developed using dated public datasets, and as a result, they may become obsolete with time. and require continual updating. The research findings are encouraging, and our results reached an accuracy rate of over 90%.
- Agarwal Shruti, Hany Farid, Yuming Gu, Mingming He, Koki Nagano, and Hao Li, “Protecting World Leaders Against Deep Fakes,” CVPR workshops, vol. 1, pp. 38, 2019.
- Vincent Pascal, Hugo Larochelle, Yoshua Bengio, and Manzagol Pierre-Antoine, “Extracting and composing robust features with denoising autoencoders,” Proceedings of the 25th international conference on Machine learning, pp. 1096-1103, 2008.
- Lyu Siwei, “Detecting DeepFake videos in the blink of an eye,” The Conversation, vol. 29, 2018.
- Lyu Siwei, “DeepFake detection: Current challenges and next steps,” 2020 IEEE international conference on multimedia & expo workshops (ICMEW), pp. 1-6, 2020.
- Mirsky Yisroel and Wenke Lee, “The creation and detection of DeepFakes: A survey,” ACM Computing Surveys (CSUR) 54, no. 1, pp. 1-41, 2021.
https://ieeexplore.ieee.org/document/10087584/