Semester of Graduation
Spring, 2026
Degree
Master of Science in Computer Science (MSCS)
Department
Division of Computer Science & Engineering
Document Type
Thesis
Abstract
File reassembly is one of the most fundamental tasks in digital forensics, enabling recovery of data from potentially damaged storage media even when file system metadata is unavailable. This thesis reviews more than two decades of work in the realm of file carving, with a particular focus on fragmented file carving, which remains a focus of research, and file fragment classification, a principal component of fragmented file carving. This thesis serves a literature review of both file carving and fragmented file carving, surveys the massive amounts of data needed for the task of fragment classification and the datasets that serve in this role, and covers automated classification techniques for file fragments, including MoDiCo, an in-progress technique that already outperforms the previous state-of-the-art techniques on several metrics. Also introduced here is Tesserae, a to-be-released labeled dataset for digital forensics research -- the largest to date.
Date
3-24-2026
Recommended Citation
Hildebrand, Samuel, "Large-Scale File Fragment Classification via Multi-View Learning" (2026). LSU Master's Theses. 6315.
https://repository.lsu.edu/gradschool_theses/6315
Committee Chair
Ghawaly Jr., James M.
LSU Acknowledgement
1
LSU Accessibility Acknowledgment
1
Included in
Artificial Intelligence and Robotics Commons, Computer and Systems Architecture Commons, Cybersecurity Commons, Databases and Information Systems Commons, Data Storage Systems Commons, Digital Communications and Networking Commons, Information Security Commons, OS and Networks Commons, Software Engineering Commons, Systems Architecture Commons