FakeBuster: Leveraging Deep Learning for Deceptive Content Identification

Abstract

The widespread rise of fake news, misleading reviews, forged documents, and deepfake media poses a major challenge to maintaining trust and authenticity in digital communication. This research presents a FakeBuster: Leveraging Deep Learning for Deceptive Content Identification that integrates multiple detection capabilities—textual, visual, and forensic—within a single scalable framework. The system intelligently analyzes linguistic patterns, document integrity, and multimedia inconsistencies to identify deceptive or manipulated content in real time. Experimental evaluation demonstrates high accuracy and strong generalization across diverse data types. By providing an explainable, modular, and real-time solution, the proposed framework significantly enhances the reliability of digital information and contributes to combating misinformation on a broader scale.

KEYWORDS: Natural Language Processing (NLP), Machine Learning, Deep Learning, Computer Vision, Digital Forensics, Real-time Detection.