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The altered document identification proficiency test of the China National Accreditation Service for Conformity Assessment (CNAS), initiated by the China Academy of Forensic Science in 2022, provided a specially prepared questioned document sample. This challenging case sample rendered conventional morphological examination methods virtually ineffective. As a result, only a low percentage of judicial appraisal institutions received the “satisfactory” rating. This work examined the characteristics of the inkjet printers used in the proficiency test from two independent perspectives, by combining volatile solvent composition analysis of printing inks using GC–MS creatively with conventional morphological examination. This work not only efficiently determined whether there was appended content via a secondary printing pass in the case sample, but also further identified the brands of the inkjet printers used to prepare the sample of the proficiency test. Finally, precautions for identifying the brands of inkjet printers were summarized. We hope this work will underscore the importance of incorporating physical and chemical analytical methods in questioned document examination and draw forensic examiners’ attention to its necessity. •Presents results of China Academy of Forensic Science 2022 altered document proficiency test.•Combined chemical and morphological analysis improves robustness.•GC–MS analysis of volatile solvents recommended and demonstrated.•Discusses limitations of morphological methods under current training system.

Paper differentiation can play a critical role in forensic document examination along with examinations of handwriting identification, impressed writing, and ink and printer toner analyses. If reference database to compare was constructed, paper analyses are also useful in terms of examining when document paper was produced. In this study, two datasets were utilized for principal component analysis (PCA) and t-SNE, and each dataset was constructed for the manufacturer discrimination and document paper dating tasks. A database for the angle and step data of periodic marks at top 10 intensity respectively was established by a two dimensional lab formation sensor. Model performance was evaluated using clustering indexes, i.e., the silhouette index, the normalized mutual information, the Calinski–Harabasz index, and the Davies–Bouldin index. Periodic marks analysis using an unsupervised machine learning model was performed to differentiate the manufacturers and investigate the production date in the case of forming fabric alteration. We found that forensic differentiation of paper is feasible using a combined PCA and t-SNE model on test document data and two datasets because the forming fabric of paper-making machines inevitably leaves periodic marks on the surface of the paper. Our findings demonstrate that these periodic marks can play a key role in forensic feature extraction. As a result, the combined PCA and t-SNE model has demonstrated high performance on the target tasks. •Combined PCA and t-SNE effectively perform forensic examination of document paper.•Periodic marks enable manufacturer discrimination and production date identification.•2D-F sensor surpasses video spectral methods in distinguishing document paper types.•Distance-based classifiers demonstrate complementary clustering capabilities.•Case study validates the model with unknown samples and practical forensic applications.

The examination of stamp impressions is a routine task for questioned document examiners (QDEs) in determining the authenticity of disputed documents. Some studies have provided methods for date determination of stamp impressions, but each single method has its own limitations, so date determination of stamp impressions remains a challenge in case work. This study reports on the real case of a forged contract, in which pre-inked stamp impressions were made on blank paper without the knowledge of the stamp owner. The combination of Raman and video spectroscopy is demonstrated to be a novel method for the date determination of these impressions. In addition to conventional morphological examination methods, video spectroscopy (VS) images were combined with Raman spectroscopy to identify ink components and the distribution of pre-inked impressions. These characteristics vary with the time of stamping, especially after re-inking, and can thus be used to date forged documents. Specifically, spectral analysis of the ink provided a regular representation of temporal variations based on reference stamp samples, which were mutually confirmed by morphological analysis. This approach combines multiple analysis methods to provide diverse evidence for characterizing the tampering and dating the impressions. The results suggest the included stamp impressions were genuine and made on a blank sheet of paper prior to printing the content of the agreement. The five questioned impressions were not entirely consistent in terms of generation time, and none of them were stamped on the nominated date. [Display omitted] •Multiple analysis methods (Video spectrocopy, Raman spectroscopy, and morphological method) were used to provide diverse evidence.•Reporting the variation in inks distribution of pre-inked stamp impressions before and after re-inking.•The combination of Video and Raman spectroscopy is a potential method beneficial to dating the impressions.

The paper presents the possibilities of using the ToF-SIMS technique in the examination of a range of samples as forensic evidence. These include the analysis of documents, the examination of writing media, the analysis of crossing lines, the analysis of cosmetics, hair analysis, the examination of automobile paints, and the analysis of fingerprints and their contamination with exogenous substances. The advantages and disadvantages of this method were analysed with reference to the information that any forensic investigator would wish to obtain when examining highly significant evidence. •ToF-SIMS provides an important imaging capability for forensic science.•The ability to analyse a wide range of forensic trace types.•The non-destructive technique with high sensitivity and resolution.•The technique with new applications, including the analysis of evidences.

Forensic document examination has long focused on elucidating the classification and identification paradigms of laser-printed documents. Toner establishes critical evidentiary linkages between printed documents and their corresponding printing devices, making toner analysis a significant part of document examination. This systematic review synthesizes contemporary advances in laser printer forensics through tripartite analytical dimensions: morphological analysis, physical characterization, and chemical profiling. The emerging paradigm demonstrates heightened adoption of quasi-nondestructive and nondestructive testing methodologies to maintain evidentiary integrity, concurrently with the integration of chemometric workflows and machine learning architectures to address operational demands for rapid, high-fidelity analysis. Our methodological framework facilitates comparative evaluation of analytical techniques' merits and limitations, supported by bibliometric analysis of peer-reviewed studies (2018–2024) that reveals emergent trends. Crucially, we identify a critical research gap in explainable artificial intelligence (XAI) frameworks for forensic algorithm validation, underscoring the imperative for interpretable computational models in judicial contexts. •This paper reviews laser printer classification and identification from physical, chemical, cryptography, and morphology perspectives.•This paper summarizes the strengths and weaknesses of various techniques used to classify and identify the laser printer.

Laser-induced breakdown spectroscopy (LIBS) was tested for all of the relevant issues in forensic examinations of commercial inks, including classification of pen inks on one paper type and on different paper types, determination of the deposition order of layered inks, and analysis of signatures and toners on one questioned document. The scope of this work was to determine the potential of a single LIBS setup that is compatible with portable instruments for different types of ink analysis, rather than building a very large database for inks and papers. We identified up to seven metals characteristic for the examined inks, which allowed to fully discriminate all eight black inks on one type of printing paper. When the inks were tested on ten different papers, the correct classification rates for some of them were reduced for reasons thoroughly studied and explained. The replicated tests on three crossing points, each one involving a pair of blue or black inks, were successful in five cases out of six. In the test simulating documents of forensic interest (questioned documents), LIBS was able to correctly identify the differences in three inks used for signatures on one of the three pages and the use of different printing inks on each page of the document.

Dynamic signatures are a digitalized form of handwritten signatures. Their use has seen a steep increase for important transactions, such as life insurance and telecommunication contracts, sales and banking operations. A dynamic signature can be disputed and a forensic handwriting examiner may be hired to help determine whether it is genuine or not. Such a conflict may emerge years after the questioned signature has been affixed. Since reference material contemporary to the relevant period may not be made available to the expert, it can be questioned whether time influences dynamic signature data, which could affect the expert’s results. This study was designed to explore this possible influence. Dynamic signatures of three participants were collected over a duration of 18 months, during 44 acquisition sessions. Based on this sample, the goals of describing variation of dynamic features over short and longer time periods, defining adequate sample collection strategies and sampling time frames, as well as laying down the foundation for using the time information for comparative analysis of dynamic signatures, were pursued. Both the relative stability and the slow ‘drift’ of signatures over time were illustrated by our results. The findings of this study lead to recommendations for sampling in casework, validate statements previously made by forensic scientist through an empirical investigation on dynamic signatures and strengthen the statistical basis for forensic signature comparison. •Investigation of dynamic signature characteristics over time.•Recommendation of good practices in reference material collection.•Data-driven, empirical look into dynamic signatures.

Printer source prediction is an important task when examining questioned documents. While some research has provided methods to predict the source printer of documents, with the advent of compatible consumables, printer prediction could become more complex and difficult. Predicting the source printer after replacing cartridges and identifying the source of printer cartridges are unresolved issues that are rarely addressed in current research. Herein, we introduce a novel technique to predict the manufacturer, model, and cartridges of laser printers (i.e., compatible, and original cartridges) used to produce a given document. Document samples produced using eight laser printers and 247 cartridges were collected to establish a dataset. Common manufacturers included HP, Canon, Lenovo, and Epson. After obtaining white-light images and three-dimensional profile images of printed characters, a morphological analysis was conducted by questioned document examiners (QDEs) using microscopy. Microscopic image features across a series of images were also extracted and analyzed using algorithms. Then, six high-dimensional reduction algorithms were used to obtain between- and within-printer variations as well as between- and within-cartridge variations. Finally, we conducted principal component analysis (PCA) and discriminant analysis. For 40 % of the samples, mixed discrimination analysis (MDA) and fixed discrimination analysis (FDA) were employed to predict the manufacturer, model and cartridge of laser printers used to produce the questioned printed document; the remaining 60 % samples comprised the training dataset. In the prediction of manufacturer, model and cartridge, our method achieved mean accuracies of 95.5 %, 97.5 %, and 90.2 %, respectively. Hence, this technique could reasonably aid in predicting the manufacturer, model, and cartridge of a laser printer, even if different cartridges are loaded into printers. [Display omitted] •A novel technique was provided to predict the manufacturer, model, and cartridges of laser printers.•Three-dimensional profiling combined with white-light microscopic images helps to improve the performance of the prediction.•This paper expands document examination from two dimensions to three dimensions.

The methods for perpetrating forgery and alteration of documents are becoming increasingly more sophisticated. Forensic examinations of questioned documents routinely involve physical and chemical analysis of inks. Raman spectroscopy is a very attractive technique for ink analysis because it combines chemical selectivity with ease and fast analysis and it does not require sample preparation nor leads to destruction of the evidence. However, some limitations of this technique include low sensitivity and the overwhelming phenomenon of fluorescence, which can be solved by resonance Raman spectroscopy and surface-enhanced Raman spectroscopy. This article aims to demonstrate the great potential of the Raman-based techniques by providing an overview of their application to forensic examinations of ink evidence from pens and printers. Moreover, it is also addressed the chemistry of ink-paper interactions and the problematic of intersecting lines.

Morphology-based classification of inkjet documents has the characteristics of low cost and high efficiency, but this method usually requires measurement and analysis of a large number of printed characters. This paper proposes a novel method for detecting the source of printed documents using a few printed letters. A dataset containing data pertaining to various inkjet printers, including 27 models of inkjets from HP, Canon, and Epson, and their printed documents were gathered. The specifications of the various brands and models of inkjets are summarised, and the characteristics of the microscopic appearance of the printheads are presented. Principal component analysis (PCA) of the variables was applied to describe the proximity between the specimens, and a two-dimensional kernel density estimation was used to describe the variation between and within printer brands and models. Then, specific cases were simulated by random sampling based on the collected inkjet dataset. Multivariate kernel density estimation was used to estimate the numerator and denominator probability distribution for computing the likelihood ratio (LR). The result of K-nearest neighbour analysis showed classification accuracy as high as 98%. The evaluation of the LR presented a significant result (EER=0, RMEP=0, RMED=0.07). This method helps to find a specific inkjet from even a few letters in the printed document for tactical purposes. •Summary of inkjet specifications were presented, such as printhead width and number of nozzles, ink/printhead units, print speed, ink droplet size. Additionally, different types of nozzles were sorts out in a microscopic view.•The variation of within- and between-printer helps forensic experts to understand the morphological features from examination.•KNN classified inkjets in an accuracy as high as 98% based on a few letters, for an instance, the fragmented printed documents from suspect printer.•Simulation cases generated based on the collected inkjet dataset, and the score-based log-likelihood ratio was employed to estimate the strength of the evidence.•Performance validation was employed to measure the accuracy, discriminating power and calibration of the likelihood ratio calculation method presented in this paper.