Current methods for artifact analysis and understanding depend on investigator expertise. Experienced and technically savvy examiners spend a lot of time reverse engineering applications while attempting to find crumbs they leave behind on systems. This takes away valuable time from the investigative process, and slows down forensic examination. Furthermore, when specific artifact knowledge is gained, it stays within the respective forensic units. To combat these challenges, we present ForensicAF, an approach for leveraging curated, crowd-sourced artifacts from the Artifact Genome Project (AGP). The approach has the overarching goal of uncovering forensically relevant artifacts from storage media. We explain our approach and construct it as an Autopsy Ingest Module. Our implementation focused on both File and Registry artifacts. We evaluated ForensicAF using systematic and random sampling experiments. While ForensicAF showed consistent results with registry artifacts across all experiments, it also revealed that deeper folder traversal yields more File Artifacts during data source ingestion. When experiments were conducted on case scenario disk images without apriori knowledge, ForensicAF uncovered artifacts of forensic relevance that help in solving those scenarios. We contend that ForensicAF is a promising approach for artifact extraction from storage media, and its utility will advance as more artifacts are crowd-sourced by AGP.