Multi-modal Fake News Detection

The rapid advancement of Artificial Intelligence (AI) has significantly enhanced the ability to generate highly realistic synthetic content, including deepfake images, videos, and misleading textual information. While these technologies offer innovative applications, they also pose serious threats in the form of misinformation and digital manipulation. Detecting such content has become increasingly complex due to the sophistication of modern AI models. This research proposes a comprehensive multimodal framework for detecting fake news and deepfake media by integrating multiple AI techniques. The system utilizes Convolutional Neural Networks (CNNs) for image analysis, frame-based processing for video deepfake detection, and transformer-based Natural Language Processing (NLP) models such as BERT for text classification. Additionally, external fact-checking APIs are incorporated to validate information in real time. To enhance interpretability, the system employs Grad-CAM visualization techniques that highlight manipulated regions within images, enabling users to better understand model decisions. The proposed approach leverages the strengths of each modality to improve detection accuracy and robustness. Experimental results demonstrate that the multi-modal system achieves superior performance compared to traditional single-modality approaches. The system is scalable, efficient, and suitable for real-world deployment in combating the spread of misinformation across digital platforms.