Deepfake Detection System: A CNN-LSTM Hybrid Approach for Video Forgery Identification

Deepfake technology is a growing threat to digital content integrity and has become a major source of misinformation and violations of personal privacy due to its existence. In this paper, we propose a hybrid Deepfake detection system that utilizes Convolutional Neural Networks (CNN’s) in conjunction with Long Short-Term Memory (LSTM’s) networks to analyze video spatial and temporal properties. The system has been trained on the FaceForensics++ and Celeb-DF datasets and provides over 95% accuracy for identifying real versus fake data, outperforming traditional methods of detection. We will also describe the implementation of the system using Python with TensorFlow, the evaluation metrics for determining performance, and the social media and cybersecurity implications of this work. Future research will include audio-visual multimodal integration.