Deepfake Detection Using Convolutional and Re-current Neural Network

With the rise of deep learning technology, it is becoming easier to control audiovisual content, causing serious problems in terms of broadcast accuracy and security. Deepfakes, which are created using advanced machine learning techniques such as artificial intelligence networks (GANs), are increasingly used for disinformation, cybercrime, and smear purposes. This paper presents a deep learning-based approach to detect deepfake videos using physical and spatial features extracted from videos. We utilize a combination of convolutional neural networks and recurrent neural networks, specifically InceptionV3 as a feature extractor for frame-level analysis, and gated recurrent units (GRUs) to model the connections between video frames. Our model converts video sequences into real-time data and classifies videos as a “real” or “fake” according to learning patterns. The system is trained and tested on the Deepfake Detection Challenge (DFDC) dataset using techniques such as data augmentation, sequential masking, and regular stopping to prevent overload. Performance metrics such as accuracy, precision, recall, and F1 score are used to evaluate the performance of the model. Experimental results show that the proposed method is successful in distinguishing real videos from deepfakes, demonstrating its potential for application in media proof projects around the world. We also discuss the ethical implications of deep-diving technology and the importance of reliable detection methods to reduce risks associated with electronic devices in digital spaces.