Over the past few years the popularity of approximate matching algorithms (a.k.a. fuzzy hashing) has increased. Especially within the area of bytewise approximate matching, several algorithms were published, tested and improved. It has been shown that these algorithms are powerful, however they are sometimes too precise for real world investigations. That is, even very small commonalities (e.g., in the header of a le) can cause a match. While this is a desired property, it may also lead to unwanted results. In this paper we show that by using simple pre-processing, we signicantly can in uence the outcome. Although our test set is based on text-based le types (cause of an easy processing), this technique can be used for other, well-documented types as well. Our results show, that it can be benecial to focus on the content of les only (depending on the use-case). While for this experiment we utilized text les, Additionally, we present a small, self-created dataset that can be used in the future for approximate matching algorithms since it is labeled (we know which les are similar and how)
