Content grouping results at document level.

The increasing prevalence of fake publications created by paper mills poses a significant challenge to maintaining scientific integrity. While integrity analysts typically rely on textual and visual clues to identify fake articles, determining which papers merit further investigation can be akin to searching for a needle in a haystack, as these fake publications have non-related authors and are published on non-related venues. To address this challenge, we developed a new methodology for provenance analysis, which automatically tracks and groups suspicious figures and documents. Our approach groups manuscripts from the same paper mill by analyzing their figures and identifying duplicated and manipulated regions. These regions are linked and organized in a provenance graph, providing evidence of systematic production. We tested our solution on a paper mill dataset of hundreds of documents and also on a larger version of the dataset that deliberately included thousands of documents intentionally selected to distract our method. Our approach successfully identified and linked systematically produced articles on both datasets by pinpointing the figures they reused and manipulated from one another. The technique herein proposed offers a promising solution to identify fraudulent manuscripts, and it could be a valuable tool for supporting scientific integrity.