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Error Level Analysis Technique for Identifying JPEG Block Unique Signature for Digital Forensic Analysis
Universiti Teknologi Malaysia (UTM), MYS.
Zayed University, ARE.
Universiti Teknologi Malaysia (UTM), MYS.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. (Secure Distributed Systems)ORCID iD: 0000-0003-4071-4596
2022 (English)In: Electronics, E-ISSN 2079-9292, Vol. 11, no 9, article id 1468Article in journal (Refereed) Published
Abstract [en]

The popularity of unique image compression features of image files opens an interesting research analysis process, given that several digital forensics cases are related to diverse file types. Of interest has been fragmented file carving and recovery which forms a major aspect of digital forensics research on JPEG files. Whilst there exist several challenges, this paper focuses on the challenge of determining the co-existence of JPEG fragments within various file fragment types. Existing works have exhibited a high false-positive rate, therefore rendering the need for manual validation. This study develops a technique that can identify the unique signature of JPEG 8 × 8 blocks using the Error Level Analysis technique, implemented in MATLAB. The experimental result that was conducted with 21 images of JFIF format with 1008 blocks shows the efficacy of the proposed technique. Specifically, the initial results from the experiment show that JPEG 8 × 8 blocks have unique characteristics which can be leveraged for digital forensics. An investigator could, therefore, search for the unique characteristics to identify a JPEG fragment during a digital investigation process.

Place, publisher, year, edition, pages
MDPI, 2022. Vol. 11, no 9, article id 1468
Keywords [en]
digital forensics; file fragment identification; JPEG fragment; file-carving; error level analysis; JPEG signature
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:bth-22902DOI: 10.3390/electronics11091468ISI: 000795263600001Scopus ID: 2-s2.0-85129454133OAI: oai:DiVA.org:bth-22902DiVA, id: diva2:1657341
Note

open access

Available from: 2022-05-10 Created: 2022-05-10 Last updated: 2022-05-30Bibliographically approved

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