Truth Guard: AI-Powered Fake News and Deepfake Detection with Contextual Analysis

The proliferation of misinformation in the digital age, especially in the form of fake news and deep fakes, poses a serious challenge to societal trust in media. This research explores an AI-powered approach for detecting fake news and deep fake content, utilizing machine learning (ML) and deep learning algorithms, as well as contextual analysis. By integrating natural language processing (NLP) and computer vision techniques, the proposed system aims to enhance detection accuracy across text, audio, and video media. The paper outlines the technologies driving fake news and deep fake generation, existing detection methods, the proposed solutions, and challenges in mitigating the spread of fabricated content. Additionally, it explores future research directions and the potential for more robust detection strategies in the future.