Digital Image Forensics for Authenticating WhatsApp Screenshots Using Metadata and Error-Level Analysis

Abstract

WhatsApp screenshots are widely used to communicate records of conversations, yet a screenshot is a rendered image that can be edited, recomposed, or generated without preserving the original message database. This technical review examines a layered forensic workflow for assessing screenshot authenticity using acquisition records, cryptographic hashes, metadata examination, error-level analysis, clone detection, compression artifacts, optical character recognition, and interface-consistency checks. The analysis emphasizes that no single method can prove authenticity. Metadata may be absent after screenshot capture or platform transfer, while error-level analysis is sensitive to recompression and should not be interpreted as definitive evidence of manipulation. A defensible workflow begins with preservation of the submitted file and device context, followed by reproducible image analysis and comparison with native WhatsApp artifacts when legally and technically available. The article proposes a confidence-based reporting framework that separates observations, analytical indications, and conclusions. Technical validation should use controlled genuine and manipulated screenshot datasets, report sensitivity and specificity by manipulation type, and test robustness across devices, operating systems, themes, resolutions, and compression levels. The resulting framework positions screenshot analysis as a digital-forensics triage process rather than a stand-alone legal determination.