Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
The increasing prevalence of cyber-crime has led to a surge of new forensics tools aimed at collecting digital evidence from a suspect’s computer. A suspect’s hard drive can be the largest source of collected information, but the task of collection can be made significantly more difficult when the contents of a hard drive are deleted or damaged. In these circumstances the information needed to read files normally may be missing, leaving only the raw, often fragmented, data behind. If we were able to reliably reconstruct files from this raw data, then it would be more difficult for suspects to destroy potential evidence. In this paper, we focus on the reconstruction of an image from a set of fragments. This research contributes a novel image reconstruction method which utilizes pre-stitch data extraction on individual data sectors. We show that, when certain attributes are successfully extracted from the data sectors, this method yields a high reconstruction accuracy even when used with a naive stitching algorithm on heavily fragmented image files.
Recommended Citation
Montambault, Kevin and Kul, Gokhan, "Image Attribute Estimation for Forensic Image Reconstruction from Fragments" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 5.
https://aisel.aisnet.org/hicss-56/st/digital_forensics/5
Image Attribute Estimation for Forensic Image Reconstruction from Fragments
Online
The increasing prevalence of cyber-crime has led to a surge of new forensics tools aimed at collecting digital evidence from a suspect’s computer. A suspect’s hard drive can be the largest source of collected information, but the task of collection can be made significantly more difficult when the contents of a hard drive are deleted or damaged. In these circumstances the information needed to read files normally may be missing, leaving only the raw, often fragmented, data behind. If we were able to reliably reconstruct files from this raw data, then it would be more difficult for suspects to destroy potential evidence. In this paper, we focus on the reconstruction of an image from a set of fragments. This research contributes a novel image reconstruction method which utilizes pre-stitch data extraction on individual data sectors. We show that, when certain attributes are successfully extracted from the data sectors, this method yields a high reconstruction accuracy even when used with a naive stitching algorithm on heavily fragmented image files.
https://aisel.aisnet.org/hicss-56/st/digital_forensics/5