Search Results (541)

Background/Objectives: Age estimation is of fundamental importance in forensic investigations. When traditional methods based on gross bone morphology or morphometric analysis cannot be applied, forensic experts must rely on multidisciplinary approaches. Histomorphometry has consistently proven to be reliable in cases of highly fragmented or incomplete skeletal remains, particularly in older individuals. Building on the foundational study of Amprino and Bairati, this study evaluated the correlations between bone microstructural features in femoral cross-sections and the age and sex of individuals. Methods: The sample comprised 95 femoral mid-diaphyseal thin sections obtained from autopsy specimens housed at the Institute of Legal Medicine, University of Bari (Italy), representing both male and female individuals aged 18 to 92 years. The numbers and densities of primary, intact secondary, and fragmentary secondary osteons, together with osteon circularity and the mean osteonal area, were measured to investigate age-related variation. Statistical analyses included t-tests, Mann–Whitney tests, Spearman’s rank correlation, and General Linear Models (GLMs). Results: No significant differences in histomorphometric variables were observed between males and females. However, the number of intact secondary osteons and osteon population density increased with age, while the mean osteonal area and osteon circularity decreased with age. Although some variables displayed significant correlations with age, residual analysis indicated a lack of heterogeneity in variance, which limited the development of a robust predictive model. Conclusions: The findings highlight both the potential and the limitations of histomorphometry in forensic age estimation. While certain microstructural variables correlate with age, inter-individual variability reduces predictive accuracy. Further research is needed to refine models that account for biological and biomechanical variability, particularly in older adults. Full article

Background/Objectives—Traditional forensic investigations often analyze digital, physical, and criminological evidence separately, leading to fragmented timelines and reduced accuracy in reconstructing complex events. To address these gaps, this study proposes the Digital Stratigraphy Framework (DSF), inspired by archaeological stratigraphy, to integrate heterogeneous evidence into structured, temporally ordered layers. DSF aims to reduce asynchronous inconsistencies, minimize false associations, and enhance interpretability across digital, behavioral, geospatial, and excavation evidence. Methods—DSF employs Hierarchical Pattern Mining (HPM) to detect recurring behavioral patterns and Forensic Sequence Alignment (FSA) to synchronize evidence layers temporally and contextually. The framework was tested on the CSI-DS2025 dataset containing 25,000 multimodal, stratified records, including digital logs, geospatial data, criminological reports, and excavation notes. Evaluation used 10-fold cross-validation, Bayesian hyperparameter tuning, and structured train-validation-test splits. Metrics included accuracy, precision, recall, F1-score, and Stratigraphic Reconstruction Consistency (SRC), alongside ablation and runtime assessments. Results—DSF achieved 92.6% accuracy, 93.1% precision, 90.5% recall, 91.3% F1-score, and an SRC of 0.89, outperforming baseline models. False associations were reduced by 18%, confirming effective cross-layer alignment and computational efficiency. Conclusions—By applying stratigraphic principles to forensic analytics, DSF enables accurate, interpretable, and legally robust evidence reconstruction. The framework establishes a scalable foundation for real-time investigative applications and multi-modal evidence integration, offering significant improvements over traditional fragmented approaches. Full article

Background: Adoption in Italy has evolved significantly over the years, becoming a critical legal, social, and ethical issue. Recent data indicates a decline in the number of adoptions, reflecting a broader European trend. Historically, adoption was primarily pursued by couples unable to have biological children due to infertility. Today, however, the adoption process in Italy has expanded to include single individuals, following recent legislative reforms that aim to accommodate diverse family structures. Methods: We have analyzed the relevant literature through a PubMed search and studied the current regulations considering recent Italian developments. This paper examines the current state of adoption in Italy, focusing on the increasing need for interdisciplinary collaboration between law, forensic medicine, ethics, and psychology. Results: The evolving adoption landscape requires a comprehensive approach that addresses legal issues, medical conditions, ethical considerations, and psychological support for both adoptive parents and children. The integration of these fields is essential for ensuring the well-being of the adopted child and the success of the adoption process. Conclusions: By exploring the dynamics of adoption, this article highlights the importance of a multidisciplinary framework in meeting the needs of children awaiting adoption and fostering the development of new family structures in Italy. In particular, forensic medicine plays a central role in evaluating parental suitability, detecting potential risk factors, and supporting judicial decisions through technical expertise and medico-legal assessments. Full article

Background/Objectives: Forensic dental determination plays a central role in human identification, age estimation, and trauma analysis in medico-legal contexts. Traditional approaches—including clinical examination, odontometric analysis, and radiographic comparison—remain essential but are constrained by examiner subjectivity, population variability, and reduced applicability in fragmented or degraded remains. Recent advances in cone-beam computed tomography (CBCT), three-dimensional surface scanning, intraoral imaging, and artificial intelligence (AI) offer promising opportunities to enhance accuracy, reproducibility, and integration with multidisciplinary forensic evidence. The aim of this review is to synthesize conventional and emerging approaches in forensic odontology, critically evaluate their strengths and limitations, and highlight areas requiring validation. Methods: A structured literature search was performed in PubMed, Scopus, Web of Science, and Google Scholar for studies published between 2015 and 2025. Search terms combined forensic odontology, dental identification, CBCT, 3D scanning, intraoral imaging, and AI methodologies. From 108 records identified, 81 peer-reviewed articles met eligibility criteria and were included for analysis. Results: Digital methods such as CBCT, 3D scanning, and intraoral imaging demonstrated improved diagnostic consistency compared with conventional techniques. AI-driven tools—including automated age and sex estimation, bite mark analysis, and restorative pattern recognition—showed potential to enhance objectivity and efficiency, particularly in disaster victim identification. Persistent challenges include methodological heterogeneity, limited dataset diversity, ethical concerns, and issues of legal admissibility. Conclusions: Digital and AI-based approaches should complement, not replace, the expertise of forensic odontologists. Standardization, validation across diverse populations, ethical safeguards, and supportive legal frameworks are necessary to ensure global reliability and medico-legal applicability. Full article

Background/Objectives: The purpose of this study is twofold: first, to construct and evaluate a deep-learning model for automated age estimation from lateral cephalograms spanning early childhood to older adulthood; and second, to determine whether sex-specific training improves predictive accuracy. Methods: This retrospective study examined 600 lateral cephalograms (ages 4–63 years; 300 female, 300 male). The images were randomly divided into five cross-validation folds, stratified by sex and age. An ImageNet-pretrained DenseNet-121 was employed for age regression. Three networks were trained: mixed-sex, female-only, and male-only. Performance was evaluated using mean absolute error (MAE) and the coefficient of determination (R²). Grad-CAM heatmaps quantified the contributions of six craniofacial regions. Duplicate patients were excluded to minimize sampling bias. Results: The mixed-sex model achieved an MAE of 2.50 ± 0.27 years, an R² of 0.84 ± 0.04, the female-only model achieved an MAE of 3.04 ± 0.37 years and an R² of 0.82 ± 0.04, and the male-only model achieved an MAE of 2.29 ± 0.27 years and an R² of 0.83 ± 0.04. Grad-CAM revealed dominant activations over the frontal bone in the mixed-sex model; the occipital bone and cervical soft tissue in the female model; and the parietal bone in the male model. Conclusions: A DenseNet-121-based analysis of lateral cephalograms can provide a clinically relevant age estimation with an error margin of approximately ±2.5 years. Using male-only model slightly improves performance metrics, and careful attention to training data distribution is crucial for broad applicability. Our findings suggest a potential contribution to forensic age estimation, growth and development research, and support for unidentified deceased individuals when dental records are unavailable. Full article

Microbial forensics involves analyzing biological evidence to evaluate weaponized microorganisms or their toxins. This study aimed to detect and type Yersinia pestis from four simulated forensic samples—human plasma diluted in phosphate-buffered saline (#24-2), tomato juice (#24-5), grape juice (#24-8), and a surgical mask (#24-10). Notably, samples #24-10 may have contained live bacteria other than Y. pestis. A real-time polymerase chain reaction confirmed the presence of Y. pestis in all samples; however, whole-genome sequencing (WGS) coverage of the Y. pestis chromosome ranged from 0.46% to 97.1%, largely due to host DNA interference and low abundance. To address these limitations and enable strain-level identification, we designed a hybridization-based target enrichment approach focused on multilocus variable number tandem repeat analysis (MLVA). Next-generation sequencing (NGS) using whole-genome amplification revealed that the accuracy of the 25 MLVA profiles of Y. pestis for samples #24-2, #24-5, #24-8, and #24-10 was 4%, 100%, 52%, and 0%, respectively. However, all samples showed 100% accuracy with target-enriched NGS, confirming they all belong to the same strain. These findings demonstrate that a targeted enrichment strategy for MLVA loci can overcome common obstacles in microbial forensics, particularly when working with trace or degraded samples where conventional WGS proves challenging. Full article

Terminal forensics in large mobile networks is a vital activity for identifying compromised devices and analyzing malicious actions. In contrast, the study described here begins with the domain of terminal forensics as the primary focus, rather than the threat itself. This paper proposes a new multi-criteria decision-making (MCDM) model that integrates complex picture fuzzy sets (CPFS) with the combinative distance-based assessment (CODAS), referred to throughout as complex picture fuzzy CODAS (CPF-CODAS). The aim is to assist in forensic analysis for detecting mobile botnet command and control (C&C) systems. The CPF-CODAS model accounts for the uncertainty, hesitation, and complex numerical values involved in expert decision-making, using degrees of membership as positive, neutral, and negative values. An illustrative forensic case study is constructed where three mobile devices are evaluated by three cybersecurity professionals based on six key parameters related to botnet activity. The results demonstrate that the model can effectively distinguish suspicious devices and support the use of the CPF-CODAS approach in terminal forensics of mobile networks. The robustness, symmetry, and advantages of this model over existing MCDM methods are confirmed through sensitivity and comparison analyses. In conclusion, this paper introduces a novel probabilistic decision-support tool that digital forensic specialists can incorporate into their workflow to proactively identify and prevent actions of mobile botnet C&C servers. Full article

This review explores the current landscape of neural network-based methods for digital image noise processing. Digital cameras have become ubiquitous in fields like forensics and medical diagnostics, and image noise remains a critical factor for ensuring image quality. Traditional noise suppression techniques are often limited by extensive parameter selection and inefficient handling of complex data. In contrast, neural networks, particularly convolutional neural networks, autoencoders, and generative adversarial networks, have shown significant promise for noise estimation, suppression, and analysis. These networks can handle complex noise patterns, leverage context-specific data, and adapt to evolving conditions with minimal manual intervention. This paper describes the basics of camera and image noise components and existing techniques for their evaluation. Main neural network-based methods for noise estimation are briefly presented. This paper discusses neural network application for noise suppression, classification, image source identification, and the extraction of unique camera fingerprints through photo response non-uniformity. Additionally, it highlights the challenges of generating reliable training datasets and separating image noise from photosensor noise, which remains a fundamental issue. Full article

Cannabis sativa L. (2n = 2x = 20) is a widely recognized species within the Cannabaceae family. Despite its utilization for medicinal, recreational, and industrial purposes, alongside its extensive historical background, the number of genetic and biotechnological studies of this plant species has decreased due to legal ramifications and prohibition campaigns associated with its use and cultivation. For many years, the development of novel varieties has been pursued solely by cultivators, as domestic growers have transitioned their work from cultivation to breeding Cannabis lineages. Recently, the application of genomics has facilitated a surge in methodologies aimed at marker-assisted selection, germplasm management, genetic differentiation, authentication of cultivated varieties or cultivars, and forensic applications such as safeguarding intellectual property rights. Nevertheless, the utilization of molecular markers for the advancement of commercial varieties through marker-assisted breeding (MAB) frameworks remains rare. This investigation was designed to evaluate a previously established informative microsatellite (SSR) array for the genotyping of drug-type Cannabis sativa cultivars derived from seeds of European origin. A total of 171 samples from 20 varieties were collected from European distributors and analyzed for genetic uniformity and population structure. The results were then compared with previously analyzed hemp samples and drug-type samples of Canadian origin, revealing the identification capabilities of our SSR genotyping method. Full article

Face swapping is commonly assumed to act locally on the face region, which motivates placing watermarks away from the face to preserve the integrity of the face. We demonstrate that this assumption is violated in practice. Using a region-aware protocol with tunable-strength visible and invisible watermarks and six face-swap families, we quantify both identity transfer and watermark retention on the VGGFace2 dataset. First, edits are non-local—generators alter background statistics and degrade watermarks even far from the face, as measured by background-only PSNR and Pearson correlation relative to a locality-preserving baseline. Second, dependencies between watermark strength, identity transfer, and retention are non-monotonic and architecture-dependent. Methods that better confine edits to the face—typically those employing segmentation-weighted objectives—preserve background signal more reliably than globally trained GAN pipelines. At comparable perceptual distortion, invisible marks tuned to the background retain higher correlation with the background than visible overlays. These findings indicate that classical robustness tests are insufficient alone—watermark evaluation should report region-wise metrics and be strength- and architecture-aware. Full article

This study evaluated the feasibility of including a computerized dynamic test of analogical reasoning in standard neuropsychological assessments in a heterogeneous psychiatric population. The participants were 40 adult patients (M_age = 33.15 ± 12.27, range 19–68; 60% male) enrolled in specialized mental health and forensic care programs in The Netherlands, who were randomly assigned into either a training, a passive, or a control group. A pretest–training–posttest paradigm was used for the training group, and the dynamic test consisted of 26 items of the A:B::C:? type. In terms of practical use, it was found that the administration time varied largely, and 22% of the data was lost due to drop out or technical malfunctions. Test–retest reliability was acceptable for the training group (r = 0.61) and good for the practice and control groups (resp. r = 0.88 and 0.80). A statistical trend was observed for the training vs. practice group (Z = −1.598, p = 0.055), but not for the training vs. control group (Z = −0.839, p = 0.201). It was concluded that an indication of training effectiveness was found; however, in this clinical sample, the applicability of the current form of the dynamic test is still limited. Several modification options are discussed. Full article

As deepfake technology matures, its risks in spreading false information and threatening personal and societal security are escalating. Despite significant accuracy improvements in existing detection models, their inherent opacity limits their practical application in high-risk areas such as forensic investigations and news verification. To address this gap in trust, explainability has become a key research focus. This paper provides a systematic review of explainable deepfake detection methods, categorizing them into three main approaches: forensic analysis, which identifies physical or algorithmic manipulation traces; model-centric methods, which enhance transparency through post hoc explanations or pre-designed processes; and multimodal and natural language explanations, which translate results into human-understandable reports. The paper also examines evaluation frameworks, datasets, and current challenges, underscoring the necessity for trustworthy, reliable, and interpretable detection technologies in combating digital misinformation. Full article

As 5G and beyond networks grow in heterogeneity, complexity, and scale, traditional Intrusion Detection Systems (IDS) struggle to maintain accurate and precise detection mechanisms. A promising alternative approach to this problem has involved the use of Deep Learning (DL) techniques; however, DL-based IDS suffer from issues relating to interpretation, performance variability, and high computational overheads. These issues limit their practical deployment in real-world applications. In this study, CiNeT is introduced as a novel DL-based IDS employing Convolutional Neural Networks (CNN) within a bijective encoding–decoding framework between network traffic features (such as IPv6, IPv4, Timestamp, MAC addresses, and network data) and their RGB representations. This transformation facilitates our DL IDS in detecting spatial patterns without sacrificing fidelity. The bijective pipeline enables complete traceability from detection decisions to their corresponding network traffic features, enabling a significant initiative towards solving the ‘black-box’ problem inherent in Deep Learning models, thus facilitating digital forensics. Finally, the DL IDS has been evaluated on three datasets, UNSW NB-15, InSDN, and ToN_IoT, with analysis conducted on accuracy, GPU usage, memory utilisation, training, testing, and validation time. To summarise, this study presents a new CNN-based IDS with an end-to-end pipeline between network traffic data and their RGB representation, which offers high performance and enhanced interpretability through revisable transformation. Full article

Background/Objectives: Epigenetics refers to heritable modifications in gene expression that do not involve changes to the DNA sequence. Among these, DNA methylation, histone modifications, and non-coding RNAs play a key role in regulating gene activity and are influenced by environmental factors, including exposure to psychoactive substances. In recent years, it has been hypothesized that such alterations may serve as molecular markers with forensic relevance. This systematic review aims to evaluate whether current evidence supports the use of drug-induced epigenetic changes as potential toxicological fingerprints in human subjects. Methods: A systematic literature search was conducted following PRISMA guidelines, including articles published on PubMed between 1 January, 2010, and 31 December, 2025. Only studies conducted on human samples and published in English were considered; animal studies and articles lacking epigenetic data were excluded. Results: Forty-two studies met the inclusion criteria. The most commonly investigated substances (alcohol, cocaine, methamphetamine, cannabis, and opioids) were found to induce specific and, in some cases, persistent epigenetic changes. These include alterations in CpG methylation in promoter regions, variations in miRNA expression, and modulation of epigenetic enzymes. Such changes were observed in brain tissue, blood cells, and semen, with evidence of persistence even after drug cessation. Conclusions: Current evidence confirms that psychoactive substance use is associated with specific epigenetic modifications. However, forensic application remains limited due to confounding factors such as age, co-exposures, and post-mortem interval. Further standardized research is necessary to validate their use as forensic biomarkers. Full article

Accurate sex estimation is critical in forensic anthropology for developing biological profiles, with the mandible serving as a valuable alternative when crania or pelvic bones are unavailable. This study aims to enhance mandibular sex estimation using deep learning on 3D models in a southern Croatian population. A dataset of 254 MSCT-derived 3D mandibular models (127 male, 127 female) was processed to generate 4096-point clouds, analyzed using an adapted PointNet++ architecture. The dataset was split into training (60%), validation (20%), and test (20%) sets. Unsupervised analysis employed an autoencoder with t-SNE visualization, while supervised classification used logistic regression on extracted features, evaluated by accuracy, sensitivity, specificity, PPV, NPV, and MCC. The model achieved 93% cross-validation accuracy and 92% test set accuracy, with saliency maps highlighting key sexually dimorphic regions like the chin, gonial, and condylar areas. A user-friendly Gradio web application was developed for real-time sex classification from STL files, enhancing forensic applicability. This approach outperformed traditional mandibular sex estimation methods and could have potential as a robust, automated tool for forensic practice, broader population studies and integration with diverse 3D data sources. Full article