Deepfake Detection Using Multimodal Deep Learning and Explainable AI

Deepfakes, generated using advanced deep learning techniques, pose serious threats to digital security, privacy, and trust. These synthetic media can manipulate images, videos, and audio to create highly realistic but deceptive content. This paper proposes a unified multimodal deepfake detection framework that integrates image, video, and audio analysis using deep learning models. Convolutional Neural Networks (CNNs) are used for spatial feature extraction, while Recurrent Neural Networks (RNNs) are applied for temporal and audio analysis. A fusion mechanism combines outputs from different modalities to improve detection accuracy. Additionally, Explainable Artificial Intelligence (XAI) techniques such as Grad-CAM and saliency maps are incorporated to enhance interpretability. The system is implemented using Python with TensorFlow/PyTorch and deployed using Flask/FastAPI. Experimental results demonstrate improved accuracy and robustness compared to traditional single-modality approaches.