An Integrated Deep Learning Framework for Fake News and Deepfake Video Detection

Abstract

The rapid advancement of digital media technologies has led to a significant increase in the dissemination of misinformation in the form of fake news and deepfake videos. These manipulated contents pose serious threats to public trust, social stability, and information authenticity. This paper presents an integrated deep learning–based framework for detecting fake news and deepfake videos. The proposed system employs a transformer-based Natural Language Processing model to classify textual news content, while a Convolutional Neural Network–based model is used to analyze video frames for detecting facial manipulation. Additionally, a real-time detection module is implemented to analyze live video streams using face detection and temporal smoothing techniques. Experimental evaluation demonstrates that the proposed approach effectively distinguishes real and fake content with reliable accuracy. The system is designed to be modular, scalable, and suitable for real- world deployment in media verification and social networking platforms.