Leveraging Deep Learning for End-to-End Deepfake Video Identification

In recent years, the rise of deepfake technology has posed significant risks to media integrity, cybersecurity, and personal privacy. This project, Leveraging Deep Learning for End-to-End Deepfake Video Identification, proposes a robust detection system integrating spatial and temporal analysis with advanced deep learning techniques. The system employs a hybrid architecture, combining ResNet-50 for detecting frame-level artifacts and Long Short-Term Memory (LSTM) networks for capturing temporal inconsistencies across video frames. The methodology involves preprocessing video data, extracting frames, and applying normalization techniques to enhance feature clarity. ResNet-50 identifies anomalies like irregular facial textures and unnatural lighting, while LSTM tracks facial movements and blink rates. Additional techniques like Optical Flow Analysis, Discrete Fourier Transform (DFT), Principal Component Analysis (PCA), and Recursive Feature Elimination (RFE) further optimize the detection process. Performance evaluation is conducted using benchmark datasets with metrics such as accuracy, precision, recall, and AUC-ROC, demonstrating the system's robustness. A Flask-based web interface allows users to upload videos and obtain real-time feedback. Ethical considerations regarding privacy and responsible use are emphasized, promoting transparency and fairness. In conclusion, this project highlights the potential of combining deep learning and quantum computing paradigms to combat digital content manipulation.