Real-Time Deepfake Detection Using a Hybrid Mobile Net-LSTM Model For Image and Video Analysis

The rise of deepfake technology has led to an urgent need for robust detection mechanisms to combat misinformation and digital fraud. Deepfake media, generated using deep learning techniques, has become increasingly sophisticated, making it difficult to distinguish between real and manipulated content. This paper presents a hybrid MobileNet-LSTM model designed for real-time deepfake detection in both image and video formats. MobileNet efficiently extracts spatial features, while LSTM captures temporal dependencies, ensuring higher accuracy in detecting manipulated content. The proposed method is evaluated on benchmark datasets, demonstrating improved accuracy, computational efficiency, and resilience against adversarial manipulations. Our results indicate the model's effectiveness in real-time applications, making it a suitable candidate for social media and digital forensic platforms. Furthermore, a comparative study with state of the art models highlights the advantages of our approach in terms of precision, recall, and robustness against adversarial attacks.