Metadata Extraction and Image Analysis System

Digital images are widely used as evidence in legal, forensic, cybersecurity and social-media investigations. However, the reliability of digital images is increasingly challenged by metadata tampering, editing software, recompression, screenshots and AI-generated content. This research presents MetaForensicAI, a Metadata Extraction and Image Analysis System for Digital Forensics that performs batch image validation, internal metadata extraction, provenance analysis, authenticity-oriented assessment, timestamp checking, evidence correlation and structured report generation in a unified graphical workflow. The proposed system follows a modular Python-based architecture and integrates an interactive forensic chatbox, analysis modules and reporting components to support explainable decision-making. Instead of depending on system-level external tools or a single indicator, the system combines metadata strength, software traces, structural cues, screenshot signals and synthetic-content indicators to derive practical origin classes. The work is positioned as a systems-oriented research contribution and is intended for further strengthening with controlled experiments, validated metrics and formal citations before submission.