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.
- Digital image forensics
- Feature filters
- Local binary pattern (LBP)
- Microscopic images
- Support vector machines (SVM)