Abstract :

Using cutting-edge computer vision and deep learning techniques, this study offers an AI-based system for detecting deepfake videos. Using CNNs, RNNs, and transfer learning models like InceptionV3 and ResNeXt, it uses GRU-based sequence analysis to extract spatiotemporal information from video frames. NLP is used to examine metadata for contextual comprehension, and preprocessing guarantees consistent input. Real and false videos are distinguished by a supervised classifier using softmax-based confidence rating. Real-time, comprehensible forecasts are made possible by the system's integration with a Flask web interface. By improving robustness, data augmentation achieves excellent accuracy and dependability on benchmark datasets for social media and forensic applications.