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.

A novel technique is introduced to predict the printer model used to produce a given document. Samples containing only a few letters printed under varying conditions (i.e., different printing modes, letter types, fonts) were collected to establish a dataset of 41 inkjet printer models from common manufacturers, such as HP, Canon, and Epson. Morphological features were analyzed by extraction of image features using several algorithms in a series of microscopic images and a Wilcoxon test was used to measure the significance of variations between printed samples. Significant differences between various printing conditions might post potential challenge to questioned document examination. Discriminant analysis and the k-nearest neighbor (KNN) algorithm were also employed for source printer prediction under varying printing condition on 30% images with the rest images as training dataset. The results of a validation experiment demonstrated that while quadratic discriminant analysis (QDA) achieved an accuracy of 96.3%, a combination of KNN and QDA reached 98.6%. As such, this technique could aid in the forensic examination of printed documents.

High-quality counterfeit stamp impressions made by inkjet printers remain challenging in questioned document examination and forensic analyses. A dataset comprised of various printed stamp impressions, using ten options of conditions and materials, and hand stamped impressions was generated. In this paper, we report printed impressions in pure color and high-quality printing mode are very similar to hand stamped impressions in terms of their microscopic characteristics. These similarities may lead to incorrect conclusions via traditional identification methods. Here, we proposed a method for identifying counterfeit stamp impressions via texture features and image quality parameters extracted from impressions. First, the statistical analysis methods were used to verify a significant difference between the printed and hand stamped impressions. Principal component analysis (PCA) was used to show the variation between the impressions, and the differences between printed and hand stamped impressions were obvious in the three-dimensional plot. After filtering the background of the stamp impressions, image processing analysis was introduced to extract features of gray level co-occurrence matrix (GLCM), segmentation-based fractal texture analysis (SFTA), local binary pattern (LBP), and image quality metrics (IQM), which were used to characterize the stamp impressions. Finally, specific cases were simulated by random selection, based on the dataset of stamp impressions, and an evaluation system for stamp evidence was established to calculate the likelihood ratios (LRs) under two alternative hypotheses. The likelihood ratio interprets calibrated evaluations on the strength of stamp impressions as evidence. We can also balance these LRs against the rates of misleading evidence with a reasonable performance (equal error rate = 0.048). This paper provides a system to differentiate high-quality printed and hand stamped impressions with reasonable performance.(c) 2023 Elsevier B.V. All rights reserved.

Ink classification is the ability to distinguish unknown inks into different groups, and ink source prediction is the ability to predict the manufacturer or model of an unknown ink. These are regular tasks in forensic analysis. The latter is more challenging than the former, as ink source prediction has expanded beyond ink classification. In this work, we reported on an approach to predict the source of black inks based on direct analysis in real time mass spectrometry and assess the strength of black ink source prediction conclusion via the likelihood ratio, using a dataset that included 39 inks from three manufacturers with a high market share. Most of these inks contain similar or identical chemical components. Dimensionality reduction based on the principal component analysis and unified manifold approximation and projection algorithms was implemented, and subsequently, the distribution plots illustrated the variations between and within the inks. Unified manifold approximation and projection showed significant priority in avoiding overcrowding of cluster representation versus principal component analysis, with results as high as 99.83% for the prediction of the ink source using 41,432 spectra data (70% data for training and 30% data for testing) after dimensionality reduction. A likelihood ratio was used to evaluate the strength of ink evidence, and the pool-adjacent-violators algorithm and logistic algorithms were used to calibrate the likelihood ratio. The results showed that the pool-adjacent-violators algorithm and logistic algorithms both had an excellent equal error rate of 0.004 but slightly different results in the rates of misleading evidence in favor of the prosecutor's hypothesis, rates of misleading evidence in favor of the defense's hypothesis, log likelihood ratio costs after calibration (Cllrcal$$ {C}_{\mathrm{llr}}<^>{\mathrm{cal}} $$), and minimum log likelihood ratio costs (Cllrmin$$ {C}_{\mathrm{llr}}<^>{\mathrm{min}} $$). A blind test validated the robustness of the methods.

目的 研究不同盖印条件对光敏印文特征造成的具体影响及其程度，总结印文特征变化规律和特点。方法 以盖印压力、印油量、印面与纸张接触时间长短、连续多次盖印这四个盖印条件作为研究因素，采用控制变量法进行实验。结果 在盖印压力大、印油浓重、印面与纸张接触时间长的情况下，章面上的固定特征能较好地反映在印文上，可变性特征较为明显；在盖印压力小、印油浅淡、印面与纸张接触时间短的情况下，印文上曝光疵点、缺陷磨损等章面固定特征反映程度相对较差，且变化性特征较少。结论 印油量减少是影响连续盖印印文特征的主要因素，缺陷特征及附着物特征保持相对稳定。

This paper presents mass spectrometry imaging (MSI) as an intuitive method of showing the differences between inks, and provides a novel method of ink source prediction based on direct analysis in real-time mass spectrometry (DART-MS) spectral analysis. The ink database comprised 106 types of ink from three brands (Canon, Epson, and HP) with high market share. High-dimensionality reduction methods, such as principal component analysis (PCA), non-negative matrix factorization (NMF), and probabilistic latent semantic analysis (pLSA), were used to reduce the dimensions of the mass spectral data. Characteristics and statistical descriptions of the mass spectra of each ink are given in this paper. Then, MSI was used to visualize the results after PCA, NMF, and pLSA. Finally, a convolutional neural network (CNN) was applied to the data after high-dimensionality reductions, which helped predict the ink source. Results showed efficient and excellent performance of machine-learning analysis in ink source prediction using one pixel in MSI (100% for black, magenta, and yellow inks; 99.6% for cyan ink). In addition, a blind test was used to validate the performance of the CNN model, with results showing that the more pixels used, the more accurate the results. Therefore, we strongly recommend wide-area detection of ink to ensure accurate results. (c) 2022 Elsevier B.V. All rights reserved.

目的探究不同盖印条件下光敏印章印文线条宽度的变化规律,为印章印文检验鉴定提供指导性建议。方法实验通过控制变量,在不同盖印条件下盖印印文样本并测量印文线条宽度,采用平均指标和变异指标相结合的统计方法,对实验数据进行统计分析

[目的/意义]电子邮件是一种互联网基础通讯工具，电子邮件的真实性是电子邮件可以作为证据被采信的前提条件。[方法/过程]首先，对电子邮件真实性鉴定的现有研究进行总结；其次，对真实性鉴定的时间信息、发件人信息、隐藏信息、结果评断等检验角度进行分析；最后，结合电子邮件真实性鉴定相关的发展和趋势情况，对电子邮件安全问题提出建议。[结果/结论]在后疫情时代，电子邮件这一重要的基础互联网交流工具，需要弥补投递链路中多个协议之间衔接薄弱这一短板，才能更好地维护网络空间安全。

目的 朱墨时序鉴别是文件检验领域的难题之一，其判定受色料组合等多种因素影响。本文旨在研究书写字迹与印文形成交叉后，时间因素对显微法检验结果存在的影响，为朱墨时序鉴别提供理论支撑。方法 对光敏印文、原子印文、印泥印文与书写字迹形成不同组合的朱墨时序上的同一个交叉部位进行两次显微检验，前后间隔30个月，研究前后两次检验图片间的变化。结果 光敏印文、原子印文、印泥印文与黑色签字笔、蓝色圆珠笔、蓝黑钢笔、衬垫式复写纸书写字迹形成不同组合的朱墨时序，除蓝黑钢笔书写字迹的颜色加深、变黑以及印泥印文常伴有极少部分脱落外，两次检验图片无较明显变化；光敏印文、原子印文、印泥印文与无碳复写纸书写字迹形成不同组合的朱墨时序，两次检验图片发生较大变化，无碳复写字迹变淡或消失。结论 光敏印文、原子印文、印泥印文与黑色签字笔、蓝色圆珠笔、蓝黑钢笔、衬垫式复写纸、无碳复写纸书写字迹形成不同组合的朱墨时序样本中，除无碳复写字迹与印泥油形成不同组合的朱墨时序有显著变化外，其他组合均无明显变化，对朱墨时序判定影响较小；无碳复写字迹与印泥油形成不同组合的朱墨时序前后两次检验变化显著，在无碳复写字迹变淡或消失的第二次检验时更无法判定其先后顺序，对朱墨时序的判定影响较大。

建立了气相色谱-质谱法（GC-MS）分析市售3种常见品牌喷墨打印机所使用的84种墨水制备的墨迹样品中的挥发性溶剂成分,并对不同墨迹中的溶剂成分的组成进行了归类总结。用不同型号的A、B品牌打印机的原装墨水打印A4纸,将C品牌打印机不同型号的墨水滴在A4纸上制备墨迹样本,第二天用打孔器取样,用含5 mg·L-1苯甲酸乙酯的甲醇溶液提取其中的挥发性溶剂成分,提取液不分流进样,在HP-INNOWAX色谱柱上用程序升温分离其中的溶剂成分,再用配有电子轰击离子源（EI）的质谱仪进行检测,以保留时间和定性离子定性。结果显示:84种墨迹样本中的溶剂有二甘醇、三甘醇、2-吡咯烷酮、N-羟乙基-2-吡咯烷酮、甘油、1,5-戊二醇、3-甲基-1,5-戊二醇、2-乙基-2-（羟甲基）-1,3-丙二醇、1,6-己二醇、2-甲基-1,3-丙二醇、1,2-己二醇、三甘醇单丁醚等12种,且不同品牌、同种品牌不同型号的样本中挥发性溶剂组成具有一定特征性,以此可以将不同品牌乃至型号的墨迹样本进行区分,可为司法鉴定中喷墨打印文件真伪的鉴定提供技术参考。