Deepfake Video Detection Using Deep Learning

In today’s digital landscape, the rapid advancement of deepfake technology has raised serious concerns due to its ability to generate highly convincing fake videos. These synthetic media artifacts are widely used to spread misinformation, manipulate public opinion, and perpetrate identity fraud, presenting significant challenges across social, political, and legal domains. Traditional detection methods often fall short when addressing the spatial and temporal anomalies introduced by deepfake algorithms. To combat this threat, we propose FakeSpotter, a hybrid deep learning framework combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for accurate deepfake detection. CNNs are employed to extract detailed spatial features from individual video frames, while RNNs—specifically Long Short-Term Memory (LSTM) networks—capture temporal inconsistencies across sequential frames. By utilizing transfer learning with a pre-trained VGG-16 architecture and training on the Celeb-DF dataset, the model achieves strong generalization to real-world deepfake scenarios. The proposed system demonstrates improved detection accuracy by effectively identifying subtle visual artifacts and unnatural motion patterns. Through this hybrid approach, FakeSpotter enhances the reliability of deepfake forensics and contributes to the broader mission of securing digital content authenticity.