AI Powered Deepfake Detection For Secure Video Conferencing

The rapid advancement of artificial intelligence has led to the emergence of deepfake technology, which poses significant threats to the security and authenticity of video conferencing systems. This paper proposes an AI-powered deepfake detection framework designed to ensure secure and trustworthy virtual communication. The system utilizes deep learning techniques, including con-volutional neural networks (CNNs) and recurrent neural networks (RNNs), to analyze facial expressions, lip synchronization, and temporal inconsistencies in video streams. By extracting both spatial and temporal features, the model effectively distinguishes between genuine and ma-nipulated video content in real time. Additionally, the framework integrates anomaly detection and metadata analysis to enhance detection accuracy. Experimental results demonstrate that the pro-posed system achieves high precision and recall while maintaining low latency, making it suitable for real-time deployment in video conferencing platforms. This approach strengthens cybersecuri-ty measures and helps prevent identity fraud, misinformation, and unauthorized access during online meetings.