Digital Media Authenticity Checker for Detecting Manipulated Images and Videos

Abstract

Ensuring the authenticity of digital images and videos has become a major challenge in the era of widespread multimedia sharing and advanced editing technologies. Manipulated media can easily spread misinformation and reduce public trust in digital content. This paper presents a Digital Media Authenticity Checker, an automated system designed to detect potential manipulation in digital images and videos using metadata analysis and digital forensic techniques. The proposed system processes uploaded media files by extracting metadata attributes and analyzing structural inconsistencies that may indicate tampering or editing. Several analytical features, including metadata integrity, file format consistency, and compression artefacts, are evaluated to assess the authenticity of the media content. The system generates an automated authenticity assessment and produces a detailed forensic report describing the analysis results. A web-based interface developed using Python and Streamlit enables users to upload media files and obtain real-time verification results on standard personal computers. Experimental evaluation indicates that the proposed framework can effectively assist in identifying suspicious digital media and support preliminary authenticity verification for images and videos shared across online platforms.