Identification of fragmented JPEG files in the absence of file systems

Abdul Kadir, Nur Fasihah, Abd Razak, Shukor and Chizari, Hassan ORCID: 0000-0002-6253-1822 (2016) Identification of fragmented JPEG files in the absence of file systems. In: 2015 IEEE Conference on Open Systems (ICOS), 24-26 Aug. 2015, Bandar Melaka, Malaysia.

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Abstract

Identifying fragmented and deleted files from scattered digital storage become crucial needs in computer forensic. Storage media experience regular space fragmentation which gives direct consequence to the files system series. This paper specifies a case where the jpeg files are heavily fragmented with absent file header which contains maximum information for the stored data can be easily retrieved. The problem is formulated using statistical byte frequency analysis for identifying the group of jpeg file fragments. Several related works have addressed the issue of classifying variety types of file format with high occurrence of being fragmented such as avi, doc, wav file and etc. These files have been tagged as among the larger file format. We provide techniques for identifying the pattern of file fragments distribution and describe roles of selected clustering attributes. Finally, we provide experimental results presenting that the jpeg fragments distribution can be retrieved with quite small gap differences between the groups.

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Statistical methods; Byte frequency; JPEG file format; Image processing; File carving; File identification
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Schools and Research Institutes > School of Business and Technology > Technical & Applied Computing
Research Priority Areas: Applied Business & Technology
Depositing User: Susan Turner
Date Deposited: 14 Feb 2018 13:49
Last Modified: 03 Jul 2018 02:33
URI: http://eprints.glos.ac.uk/id/eprint/5378

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