Analysis of Modern Document Paper for Identification of Counterfeit Document ( I ) : Filler Composition

Choi, Kyoung-Hwa; Lee, Jae‐Hoon; Ryu, Jeong-Yong

doi:10.7584/jktappi.2018.06.50.3.28

Cited by 2 publications

(3 citation statements)

References 2 publications

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“…Han et al [10] used Py-GC/MS to analyze 309 sheets of traditional Korean paper and examine their characteristics. Choi et al [11,12] used SEM-EDS to analyze fillers for 188 types of copy paper and further determined their mix ratios of fiber components by dissociation tests. Kim et al [13,14] reported that it was possible to identify three types of the copy paper and four types of Korean, Chinese, and Japanese traditional paper by performing principal component analysis (PCA) of their IR/NIR spectra.…”

Section: Previous Workmentioning

confidence: 99%

Identification of document paper using hybrid feature extraction

Lee

Kim

Yook

et al. 2023

Journal of Forensic Sciences

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Document forgery is a significant issue in Korea, with around ten thousand cases reported every year. Analyzing paper plays a crucial role in examining questionable documents such as marketable securities and contracts, which can aid in solving criminal cases of document forgery. Paper analysis can also provide essential insights in other types of criminal cases, serving as an important clue for solving cases such as the source of a blackmail letter. The papermaking process generates distinct forming fabric marks and formations, which are critical features for paper classification. These characteristics are observable under transmitted light and are created by the forming fabric pattern and the distribution of pulp fibers, respectively. In this study, we propose a novel approach for paper identification based on hybrid features. This method combines texture features extracted from images converted using the gray‐level co‐occurrence matrix (GLCM) approach and a convolutional neural network (CNN), with another set of features extracted by the CNN using the same images as input. We applied the proposed method to classification tasks for seven major paper brands available in the Korean market, achieving an accuracy of 97.66%. The results confirm the applicability of this method for visually inspecting paper products and demonstrate its potential for assisting in solving criminal cases involving document forgery.

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Section: Previous Workmentioning

confidence: 99%

Identification of document paper using hybrid feature extraction

Lee

Kim

Yook

et al. 2023

Journal of Forensic Sciences

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“…In 2020, the country recorded 2,217 cases of document forgery related to official documents and 7,604 cases concerning private documents. Over a three-year period starting in 2018, the statistics indicate that nearly ten thousand criminal cases of document forgery are reported annually (Choi et al 2018a;2018b). Hence, the development of robust forensic capabilities for detecting forgery becomes paramount (Kim et al 2016;Lee et al 2023).…”

Section: Introductionmentioning

confidence: 99%

“…Ganzerla et al (2009) conducted a comprehensive study on the characteristics of ancient documents produced in Palazzo Ducale, Italy, employing various analytical tools, including scanning electron microscopyenergy dispersive X-ray spectrometry (SEM-EDX), high-performance liquid chromatography mass spectrometry (HPLC-MS/MS), and pyrolysis-gas chromatography and mass spectrometry (Py-GC/MS). Choi et al (2018a;2018b) utilized SEM-EDX to analyze fillers in 188 types of copy paper and further determined the mix ratios of fiber components through dissociation tests.…”

Section: Introductionmentioning

confidence: 99%

Classification analysis of copy papers using infrared spectroscopy and machine learning modeling

Lee,

Kim

2023

BioRes

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The evaluation and classification of chemical properties in different copy-paper products could significantly help address document forgery. This study analyzes the feasibility of utilizing infrared spectroscopy in conjunction with machine learning algorithms for classifying copy-paper products. A dataset comprising 140 infrared spectra of copy-paper samples was collected. The classification models employed in this study include partial least squares-discriminant analysis, support vector machine, and K-nearest neighbors. The key findings indicate that a classification model based on the use of attenuated-total-reflection infrared spectroscopy demonstrated good performance, highlighting its potential as a valuable tool in accurately classifying paper products and ensuring assisting in solving criminal cases involving document forgery.

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Analysis of Modern Document Paper for Identification of Counterfeit Document ( I ) : Filler Composition

Cited by 2 publications

References 2 publications

Identification of document paper using hybrid feature extraction

Identification of document paper using hybrid feature extraction

Classification analysis of copy papers using infrared spectroscopy and machine learning modeling

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