Improving Uncertainty in Chain of Custody for Image Forensics Investigation Applications

Abstract

Cybercrime investigations rely heavily on digital evidence to establish links between suspects and the criminal conduct they are allegedly involved in. As a result, digital evidence must be protected since it is complex, volatile, and susceptible to alteration. In the digital evidence method, the chain of custody (CoC) is essential. As a result of the CoC, it is possible to establish that the evidence was never tampered with. Due to the inherent uncertainty of digital evidence, the trustworthiness of the CoC cannot be judged at this time. It is the duty of forensic examiners to challenge this inclination and publicly admit the inherent ambiguity in whatever evidence they use to make their decisions. This article suggests a new paradigm for maintaining the integrity of digital evidence in order to overcome these challenges. To handle the uncertainty generated by error-prone technologies while dealing with CoC documents, the new paradigm used a fuzzy hash inside the blockchain data structure. Traditional hashing methods are only able to tell whether two inputs are precisely the same or not because they are sensitive to even the smallest input changes. Using fuzzy hash functions, we can figure out how dissimilar two images are by comparing their similarities. As an example of how this paradigm may be applied to computer systems and make digital investigations more successful, we utilize image forensics as the focus of an in-depth look at how it works.