A Multi-Layered Forensic Framework for Synthetic Media Detection and Content Authenticity in Digital News Ecosystems
Abstract
This research establishes a standardized Standard Operating Procedure (SOP) for identifying and mitigating the risks associated with AI-generated synthetic media. As deepfake technology becomes increasingly accessible, traditional detection methods are proving insufficient. My work focuses on a multi-layered forensic approach—combining pixel-level artifact analysis with biological consistency checks (such as eye-movement and pulse-rate detection).
Key Research Highlights
Full research details and analytical frameworks can be found in the complete paper. This research examines the critical intersections of technology, policy, and governance within the emerging digital landscape of Bangladesh.
Methodology & Framework
This research establishes a standardized Standard Operating Procedure (SOP) for identifying and mitigating the risks associated with AI-generated synthetic media. As deepfake technology becomes increasingly accessible, traditional detection methods are proving insufficient. My work focuses on a multi-layered forensic approach—combining pixel-level artifact analysis with biological consistency checks (such as eye-movement and pulse-rate detection). By integrating OSINT metadata harvesting into the verification pipeline, this project aims to provide newsrooms and security agencies with a reliable framework to authenticate digital content and preserve the integrity of the information ecosystem.