Digital forensics of microscopic images for printed source identification

When trying to identify a printed forged document, examining digital evidence can prove to be a challenge. In this study, microscopic images are used for printed source identification due to their high magnification properties resulting in detailed texture and structure information. Prior research i...

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Bibliographic Details
Published in:Multimedia tools and applications 2018-04, Vol.77 (7), p.8729-8758
Main Authors: Tsai, Min-Jen, Yuadi, Imam
Format: Article
Language:English
Subjects:
Computer Communication Networks
Computer forensics
Computer Science
Data Structures and Information Theory
Digital imaging
Digitization
Discrete Wavelet Transform
Electromagnetic wave filters
Forensic computing
Forensic sciences
Identification
Identification methods
Image classification
Image processing
Multimedia Information Systems
Printers
Special Purpose and Application-Based Systems
Texture
Wavelet transforms
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Summary:When trying to identify a printed forged document, examining digital evidence can prove to be a challenge. In this study, microscopic images are used for printed source identification due to their high magnification properties resulting in detailed texture and structure information. Prior research implemented a scanner as a digitizing technique to resolve very fine printed identification, but this technique provided limited information on the resolution and magnification of the sample. In contrast, the performance of microscopy techniques can retrieve the shape and surface texture of a printed document with differing micro structures among printer sources. To explore the relationship between source printers and images obtained by the microscope, the proposed approach utilizes image processing techniques and data exploration methods to calculate many important statistical features, including: Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), Spatial filters, the Wiener filter, the Gabor filter, Haralick, and SFTA features. Among the different set of features, the LBP approach achieves the highest identification rate and is significantly superior to other methods. As a result, the proposed technique using microscopic images achieves a high classification accuracy rate, which shows promising applications for real world digital forensics research.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-017-4771-1