Automated reassembly of file fragmented images using greedy algorithms.

Area of Science:

• Computer Science
• Digital Forensics
• Data Recovery

Background:

• File fragmentation is common on digital storage media, complicating deleted file recovery.
• Digital forensic analysts often face challenges reconstructing fragmented files, especially images, due to missing file system information.
• Reconstructing heavily fragmented digital images without file table data presents a significant forensic challenge.

Purpose of the Study:

• To develop and evaluate algorithms for reconstructing heavily fragmented digital images in the absence of file table information.
• To address the forensic challenge of reassembling deleted image files from scattered data fragments.
• To provide a method for accurate image reconstruction in digital forensics investigations.

Main Methods:

• Formulating the image reassembly problem as a k-vertex disjoint graph problem.
• Developing and applying greedy heuristic algorithms for fragment ordering and image reconstruction.
• Implementing techniques for comparing image fragments to determine their correct sequence.

Main Results:

• High accuracy in reconstructing digital images even with thousands of fragments.
• Successful reassembly of multiple images involved in the fragmentation process.
• Demonstrated effectiveness of greedy heuristics in solving the image reassembly problem.

Conclusions:

• The proposed methods enable accurate reconstruction of fragmented deleted images without file table data.
• The developed algorithms offer a viable solution for digital forensic analysts dealing with fragmented image files.
• This research enhances capabilities in digital evidence recovery and analysis from damaged or fragmented storage media.