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AI Based Fake Content Detection System

Sambathkumar N

2026 · DOI: 10.22214/ijraset.2026.78728
International Journal for Research in Applied Science and Engineering Technology · 0 Citations

TLDR

By enabling users to quickly verify the authenticity of digital content, the LASSO system improves digital awareness, enhances online security, and contributes to the broader effort of reducing the spread of fake information in the digital world.

Abstract

The LASSO: AI‑Powered Fake Content Detection System is an intelligent digital application designed to identify and

analyze potentially fake, manipulated, or malicious digital content across multiple formats such as text, images, audio, video,

and URLs. In today's rapidly evolving digital environment, misinformation, deepfakes, phishing links, and AI‑generated content

have become major threats that can mislead individuals, compromise privacy, and cause financial or social harm. Traditional

methods of identifying fake content often rely on manual verification or limited detection tools, which are inefficient and

time‑consuming. This project aims to address these challenges by developing a centralized and automated platform that uses

artificial intelligence to detect suspicious or fake digital content quickly and accurately. The LASSO system provides users with a

simple and interactive interface where they can input or upload different types of media for analysis. Users can paste suspicious

text messages, upload images, audio files, or videos, and even scan URLs to verify their authenticity. The system processes the

input using AI‑based detection techniques and returns a result indicating whether the content is genuine, suspicious, or

potentially fake, along with a confidence score. The application also maintains a scan history and generates downloadable

reports, helping users keep track of previously analyzed content. Technically, the system is developed using the Flutter

framework for the frontend, enabling a modern and responsive cross‑platform mobile application interface. The backend

integrates AI‑based detection services and APIs that analyze the uploaded content and return detection results. The system

architecture is designed to ensure efficient data processing, user‑friendly navigation, and secure handling of uploaded files.

Additional modules such as notification alerts, result visualization, and report generation further enhance the functionality of

the application. In conclusion, the LASSO Fake Content Detection System provides a powerful solution to combat digital

misinformation and fraudulent media by leveraging artificial intelligence and modern mobile technologies. By enabling users to

quickly verify the authenticity of digital content, the system improves digital awareness, enhances online security, and

contributes to the broader effort of reducing the spread of fake information in the digital world.