29 A Review of “Bringing Order to Approximate Matching: Classification and Attacks on Similarity Digest Algorithms”

Fuzzy hashing or similarity hashing (a.k.a. bytewise approximate matching) converts digital artifacts into an intermediate representation to allow for efficient (fast) identification of similar objects, e.g., for deny-listing. Over the past decade, new algorithms have been developed and released to the digital forensics community. When releasing algorithms (e.g., as part of a scientific article), they are frequently compared with other algorithms to outline the benefits and sometimes also the weaknesses of the proposed approach. However, given the wide variety of algorithms and approaches, it is impossible to provide direct comparisons with all existing algorithms. In this paper, we present the first classification of approximate matching algorithms which allows for an easier description and comparisons. Therefore, we first reviewed existing literature to understand the techniques various algorithms use and to familiarize ourselves with the common terminology. Our findings allowed us to develop a categorization relying heavily on the terminology proposed by NIST SP 800-168. In addition to the categorization, this paper presents an abstract set of attacks against algorithms and why they are feasible. Lastly, we detail the characteristics needed to build robust algorithms to prevent attacks. We believe that this paper helps newcomers, practitioners, and experts alike to better compare algorithms, understand their potential, as well as characteristics and implications they may have on forensic investigations