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@article{191545,
        author = {A. Nivedha and V. Mageswari},
        title = {TRUTH GUARD EMPHASIZES PROTECTION AGAINST MISINFORMATION AND FAKE IDENTITIES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {7615-7617},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191545},
        abstract = {The rapid expansion of digital media platforms has significantly increased the spread of fake news, manipulated content, and fraudulent online profiles. This phenomenon poses serious threats to public trust, social stability, and digital security. Manual verification of online content is often slow, inconsistent, and ineffective due to the massive volume and multi-format nature of digital information. To address this challenge, this paper proposes an intelligent, automated, and multi-modal Fake News and Fake Profile Detection System that analyzes text, images, URLs, documents, and online profile attributes using machine learning (ML) and natural language processing (NLP) techniques. The proposed system integrates advanced preprocessing, feature extraction, and classification models to identify misleading content with high accuracy. A modern web-based architecture is implemented using a Next.js frontend, Python-based backend, and a secure PostgreSQL database powered by Supabase. Experimental evaluation demonstrates reliable performance, real-time detection capability, and strong usability. The system provides classification results with confidence scores and explanations, enhancing transparency and user trust. This research highlights the effectiveness of AI-driven approaches in combating misinformation and improving digital content credibility.},
        keywords = {Fake News Detection, Fake Profile Detection, Machine Learning, Natural Language Processing, Multi-Modal Analysis, Content Verification, Online Misinformation.},
        month = {January},
        }