A Deep Learning-Based Approach for Video Authenticity Verification Using CNN and LSTM
Keywords:
Video Authenticity Verification, Deepfake Detection, Deep Learning, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Spatio-Temporal Analysis, Video Forensics, Artificial Intelligence, Face Manipulation Detection, Video Classification.Abstract
The rapid advancement of Artificial Intelligence(AI) and deep learning technologies has enabled the creation ofhighly realistic manipulated videos, commonly known asdeepfakes. These videos pose significant challenges to digital security, social trust, journalism, and personal privacy bygenerating convincing but misleading visual content
References
1. Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Nießner, M., “FaceForensics++: Learning
to Detect Manipulated Facial Images,” Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2019.
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Published
2024-11-20
How to Cite
Dr. T. Prasad,Dr.B.Bhavani. (2024). A Deep Learning-Based Approach for Video Authenticity Verification Using CNN and LSTM . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8039–8046. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5889
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