Face Morph Attack Detection Using LSTM-CNN Hybrid Model

Abstract: Deepfake content distribution raises serious challenges to online safety and trust, especially on social media and news websites. This paper suggests a robust deep learning system specifically for precise detection of deepfake videos. The system relies on a new architecture that combines two approaches: EfficientNetB2 for spatial feature analysis of faces in videos and LSTM-CNN layers for analysis of temporal differences in the features. Each video frame is analyzed extensively to ascertain if it is real or fake. In designing the system, we used a balanced dataset of videos clearly labeled as real or fake. To counter instances of possible data imbalance, we used expert techniques like class weighting and performance improvement. Further, we improved the system's ability to detect deepfakes by fine-tuning certain threshold parameters. We also designed a user-friendly interface for the system that is easy to operate, allowing users to upload their videos and get real-time results without the need for technical expertise. This ease of use makes the tool accessible to everyone interested in ascertaining the authenticity of deepfakes. The use of state-of-the-art technologies in feature extraction, sequence modeling, and the easy-to-use interface makes this system a reliable tool for deepfake detection. Tests show that the system is extremely accurate and performs well when dealing with different types of videos. Its reliability and robustness make it suitable for use in real-world applications in digital forensic analysis and media authenticity verification.