3D models and applications are of utmost interest in both science and industry. With the increment of their usage, their number and thereby the challenge to correctly identify them increases. Content identification is commonly done by cryptographic hashes. However, they fail as a solution in application scenarios such as computer aided design (CAD), scientific visualization or video games, because even the smallest alteration of the 3D model, e. g. conversion or compression operations, massively changes the cryptographic hash as well. Therefore, this work presents a robust hashing algorithm for 3D mesh data. The algorithm applies several different bit extraction methods. They are built to resist desired alterations of the model as well as malicious attacks intending to prevent correct allocation. The different bit extraction methods are tested against each other and, as far as possible, the hashing algorithm is compared to the state of the art. The parameters tested are robustness, security and runtime performance as well as False Acceptance Rate (FAR) and False Rejection Rate (FRR), also the probability calculation of hash collision is included. The introduced hashing algorithm is kept adaptive e. g. in hash length, to serve as a proper tool for all applications in practice

Typical tasks in a forensic investigation are data acquisition, checksum calculation, file recovery, or content identification. These tasks can be performed mostly without user interaction but are still time-consuming, especially when a large amount of data has to be processed. Individual tasks (or sub-tasks they have in common) often do not perform efficiently and the corresponding implementations could be improved. In this paper we present stream carving, an approach to speed up tasks that are typically performed in a forensic investigation. By identifying and combining similar or identical subtasks and parallelizing most data processing, we are able to decrease the overall processing time significantly. We implemented a stream carving tool that is able to copy, recover, and identify known visual content. The general idea behind stream carving can help developing forensic multi-purpose tools that run several tasks very efficiently