Digital images serve as a vital source of evidence in forensic investigations, containing metadata that can reveal timestamps, device characteristics, geolocation details, and editing history. However, this metadata is highly vulnerable to alteration, removal, and degradation during routine handling, thereby creating challenges in maintaining authenticity and evidentiary reliability. This study evaluates the integrity and performance of open-source metadata extraction tools to support more accurate and trustworthy forensic analysis. The research process consisted of two stages. The first stage assessed the forensic soundness of five widely used tools by verifying that metadata extraction did not alter the original files. The second stage examined their accuracy, completeness, and resilience across various image transformations using a custom dataset across 10 metadata fields. The result shows that although all tools preserved evidence integrity, their ability to recover metadata varied considerably, with fragile fields such as Unique Image ID and location information poorly recovered. Among the tested tools, Exif Tool demonstrated the most balanced performance across accuracy (95.8%), completeness (64.3%), and efficiency. This study provides practical guidance for investigators, forensic educators, and tool developers by highlighting the strengths and limitations of these solutions. Its findings support enhanced forensic training and the development of more reliable open-source metadata extraction tools and methodologies to strengthen the admissibility and credibility of image-based digital evidence.