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

TLDR

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.

Abstract

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.