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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Analog documents and scanned digitized files are now considered equivalent in legal contexts, and the widespread supply of multi-functional printers has led to a surge in the use of scanned documents. With image editing tools, there has been more cases of forgery involving scanned files.

Hwatu is a popular card game widely played in both Korea and Japan. This study examined hwatu cards used in the fraudulent gambling in Korea. Fraudsters used the hwatu cards, with hidden marks printed on the back using special ink to identify the cards so that they could deceive unknowing game players. These marks are invisible to the naked eye under normal conditions and are only visible when wearing special lenses. When suspicious hwatu cards are sent to the forensic laboratory, detection of the hidden mark conducted by the microscope, video spectral comparator (VSC), and radiography, which are laborious. In this study, we developed visualization of the hidden mark on the hwatu cards by utilizing a set of the algorisms including color splitting, histogram normalization, FFT denoising, These algorithms were applied to mobile applications affording convenience and accessibility to help prevent fraud or to help law enforcement conduct an immediate investigation. The proposed method was confirmed to be simple and effective in detecting hidden marks on hwatu cards without the need for costly equipment.

We investigated pattern‐modified marked cards used in fraudulent gambling cases in Korea. These cards are printed with modifications to some of the repeated marks on the back, revealing the hand on the front and enabling fraudsters to deceive their victims. We proposed a method for identifying the modified part by first enhancing the card's color difference using an image processing technique and then calculating the similarity between the repeated basic patterns with a Siamese network. This method is fast and convenient, as it can determine the deformation with only 1 or 2 cards and can be implemented in mobile applications, allowing law enforcement officers to investigate quickly. The proposed method serves as a useful tool to aid document examiners in making judgments, as it does not require expensive equipment and effectively visualizes the alterations.