Forensic Field Research Articles

The misidentification of the deceased person beyond visual recognition within the pre-trial investigation: problematic issues and legal consequences

Objective. The purpose of the article is to highlight the issues of misidentification of a person within the framework of pre-trial investigation of criminal proceedings on the facts of discovery of corpses of unidentified persons which are not subject to visual identification. The author focuses on the stages and procedure for identification of a deceased person, as well as on some legal consequences of misidentification and ways to eliminate them. Methodology. The methodological basis of the study is the results of the practical experience of one of the authors of the article during his practical work in the pre-trial investigation unit of the National Police of Ukraine during 2015-2021. Results. The author concludes that it is inadmissible for investigators and experts to make mistakes in the identification of the dead in situations where identification of the body is impossible due to various factors, including force majeure, because incorrect identification of a person within the pre-trial investigation may lead to confusion in the transfer of the deceased's body to the family, incorrect execution of title documents, and other consequences that will seriously affect the fate of the deceased's family in terms of moral and material (social) security. This situation is typical for the discovery of bodies of deceased persons (military and civilian) in places of spontaneous burials, including mass graves, as a result of hostilities, temporary occupation of certain regions, which were recorded after de-occupation and stabilization measures in certain regions of Ukraine. Scientific novelty. The work of legal experts has hardly been devoted to the study of the identification of the bodies of the dead during the pre-trial investigation, partly limited to the analysis of certain rules on the legal grounds and tactics of conducting a crime scene inspection or exhumation, while forensic scientists have been more concerned with this issue. Therefore, the scientific novelty of the authors' work is based on practical developments and supported by knowledge in the field of criminal procedure and forensics, which is timely and extraordinary. Practical significance. The conducted research and analysis of the problem, supported by practical experience, will contribute to a deeper understanding of the problem of misidentification of the deceased person beyond visual recognition and acquires a recommendation content for synergy in the work of pre-trial investigation and forensic medicine specialists.

Technical Evaluation of a Paper-Based Electrochemical Strip to Measure Nitrite Ions in the Forensic Field.

Nitrite is a compound used as a food additive for its preservative action and coloring capability, as well as an industrial agent for its antifreezing action and for preventing corrosion, and it is also used as a pharmaceutical in cyanide detoxification therapy. However, even recently, because of its high toxicity, it has been used as a murder and suicidal agent due to its affordability and ready availability. In this technical report, we describe an electrochemical paper-based device for selectively determining nitrite in complex biofluids, such as blood, cadaveric blood, vitreous humor, serum, plasma, and urine. The approach was validated in terms of the linearity of response, selectivity, and sensitivity, and the accuracy of the determination was verified by comparing the results with a chromatographic instrumental method. A linear response was observed in the micromolar range; the sensitivity of the method expressed as the limit of detection was 0.4 μM in buffer measurements. The simplicity of use, the portability of the device, and the performance shown make the approach suitable for detecting nitrite in complex biofluids, including contexts of forensic interest, such as murders or suicides in which nitrite is used as a toxic agent. Limits of detection of ca. 1, 2, 4, 5, 3, and 4 μM were obtained in vitreous humor, urine, serum and plasma, blood, and cadaveric blood, also highlighting a satisfactory accuracy comprised between 91 and 112%.

Fostering an “investigating mindset”: Why is it important in digital forensic science education?

AbstractThe importance of the field of digital forensics (DF) is growing, where digital evidence is increasingly recognized as a crucial part of many investigations. As a result, criminal justice systems rely on DF practitioners to conduct robust investigations of digital devices and their data, and interpret and present these results in a way that can be relied upon. Undertaking this task appropriately requires a practitioner to have a range of skills; however, focus is often placed on the need for and importance of technical competency. Technical skills are vital in this role, that cannot be in dispute; however, this work discusses the need for practitioners to also have an “investigative mindset.”This article is categorized under: Digital and Multimedia Science > Cybercrime Investigation

Genetic landscape of forensic DNA phenotyping markers among Mediterranean populations

Forensic DNA Phenotyping can reveal the appearance of an unknown individual by predicting the External Visible Characteristics (EVC) from DNA obtained at the crime scene. Our aim is to characterize the genetic landscape of Human identification markers responsible for EVC among Mediterranean populations compared to other worldwide groups. We conducted an exhaustive search for genes involved in EVC variation. Then, variants located on these genes were extracted from public genotypic data of Mediterranean, American, African and East Asiatic populations. The genetic landscape of these Human identification markers, their allelic distribution and admixture analyses, were determined using plink, R and ADMIXTURE softwares. Our results showed that the Mediterranean populations appear close to the Mexican populations and distinguished from sub Saharan African populations living in the USA and from East Asiatic populations. We highlighted a total of 103454 common variants shared between the studied populations and among them, 25 common variants associated with EVC. Interestingly, genotype frequencies results showed that the rs17646946, rs13016869, rs977588, rs1805008 and rs2240751 variants located respectively in the TCHH, PRKCE, OCA2, MC1R and MFSD12 genes are significantly different between the Mediterranean and Asiatic populations. The genotype frequencies of the variants rs977589 and rs7179994 located in the OCA2 gene, and of rs12913832 and rs2240751 located respectively in HERC2 and MFSD12 genes are significantly different between the Mediterranean and American populations. Our work generates a large number of EVC variants that could be a valuable resource for future studies in the forensic field.

Enhancing Digital Forensic Investigation: A Focus on Compact Electronic Devices and Social Media Metadata

The rise of portable electronic devices and social media has led to new criminal activities, necessitating advancements in digital forensics. This paper introduces Small-Scale Digital Device Forensics (SSDDF), focusing on the forensic examination of miniature digital devices often used in crimes. SSDDF addresses the challenges posed by these devices, particularly in extracting and analyzing data from them. A key aspect of this research is exploring ontology in social media forensics, particularly within the Android operating system. This involves extracting digital evidence like user accounts, messages, and images from social media platforms. While the paper primarily focuses on social media data, it acknowledges the importance of the devices used in crimes. The integration of SSDDF and the analysis of Android system structures are highlighted as key advancements in digital forensic methodologies. These enhancements are expected to improve the process of collecting and analyzing digital evidence from both compact electronic devices and social media platforms. The study offers significant contributions to the field of digital forensics. It provides new strategies for more efficient and effective forensic investigations, especially in the context of extracting and analyzing digital evidence from small electronic devices and social media, thus paving the way for more robust digital evidence handling in future forensic inquiries.

Software para recolección de evidencias digitales rápidas en sistemas Windows

In the field of Digital Forensics, there are numerous criteria to consider when determining which elements are relevant for obtaining useful information to clarify a case. During a search and seizure procedure, when there are numerous technological items that could be candidates for confiscation for in-depth analysis in the laboratory, it becomes necessary to discern which elements would be of interest, and which would not provide useful information. This is because the analysis times grow exponentially as more equipment and mass storage devices are added to the list of items to be seized. In this paper, we present an "on-site" application software for obtaining digital forensic evidence during search and seizure procedures, following the current protocol of action. This software will also be used as an "indicator" of the content of the equipment under analysis, allowing the acting expert to make decisions (such as whether to confiscate a specific piece of equipment or not) at the location where the procedure is being carried out.

The Boundary of Artificial Intelligence in Forensic Science

Artificial intelligence (AI) is poised to revolutionize forensic science by enhancing evidence analysis and aiding in crime-solving. This paper explores AI applications in forensic fields, focusing on DNA mixture analysis, the cause of death diagnosis, and the challenges AI faces in forensic science. It addresses the limitations of AI in forensic science, emphasizing the necessity for transparent AI systems used ethically. Forensic errors are often not due to misclassification by forensic scientists but stem from incompetence, fraud, weak scientific foundations, or organizational deficiencies, resulting in wrongful convictions. Systemic issues like dependence on presumptive tests and inadequate defense contribute to errors. Inconsistencies plague various domains of forensic science, including fingerprint and DNA analysis. Establishing standardized analytical procedures can enhance consistency and credibility in forensic science. AI applications in forensic anthropology, pathology, genetics, and statistics are explored, with a focus on their potential and challenges.

Interpreting digital traces:- 8 foundational pillars to support the formation of opinion in digital forensics

The field of digital forensics (DF) is facing increasing scrutiny of the quality of the work it produces. Fundamental to it is the need for its practitioners to be able to accurately determine the meaning of potentially relevant digital traces found during an examination of a device. As the reliance on digital evidence continues to grow, so does the importance of digital trace-interpretation. It is therefore imperative that this task is conducted robustly, where this work describes ‘eight pillars’ that should underpin how a practitioner has gone about interpreting any given digital trace.

The Internet of Things (IoT) Forensic Investigation Process

The Internet of Things (IoT), a rapidly evolving network of connected devices, is expected to grow to an astounding 41.6 billion units by 2025. This exponential growth, while beneficial in terms of data collection and exchange, has also increased the vulnerability of these devices to sophisticated cyberattacks, notably the Mirai botnet malware. This paper centers on the distinctive challenges posed in the field of IoT forensics. These challenges are primarily due to the intricate and diverse nature of IoT devices and ecosystems, which complicate the application of standard forensic tools and methodologies. One of the most significant hurdles in IoT forensics is data acquisition, considering the vast diversity of devices and the lack of specialized forensic tools tailored to these unique environments. The paper conducts a thorough literature review to explore these challenges in depth, aiming to not only provide a comprehensive understanding of the current state of IoT forensics but also to identify potential avenues for future research and development. It also highlights key strategies and solutions to enhance the security of IoT devices and to support forensic investigators in navigating the complexities of IoT ecosystems. Through this exploration, the paper contributes valuable insights and guidelines, poised to shape the advancement of IoT device security and forensic investigation techniques.

A Comprehensive Survey on Methods for Image Integrity

The outbreak of digital devices on the Internet, the exponential diffusion of data (images, video, audio, and text), along with their manipulation/generation also by Artificial Intelligence (AI) models, e.g., Generative Adversarial Networks (GANs), have created a great deal of concern in the field of forensics. A malicious use can affect relevant application domains, which often include counterfeiting biomedical images, and deceiving biometric authentication systems, as well as their use in scientific publications, in the political world, and even in school activities. It has been demonstrated that manipulated pictures most likely represent indications of malicious behavior, such as photos of minors to promote child prostitution or false political statements. Following this widespread behavior, various forensic techniques have been proposed in the scientific literature over time both to defeat these spoofing attacks as well as to guarantee the integrity of the information. Focusing on Image Forensics, which is currently a very hot topic area in Multimedia Forensics, this paper will discuss the whole scenario in which a target image could be modified. The aim of this comprehensive survey will be 1) to provide an overview of the types of attacks and contrasting techniques and 2) to evaluate to what extent the former can deceive prevention methods and the latter can identify counterfeit images. The results of this study highlight how forgery detection techniques, sometimes limited to a single type of real scenario, are not able to provide exhaustive countermeasures and could/should therefore be combined. Currently, the use of neural networks, such as CNNs, is already heading, synergistically, in this direction.

Selecting mRNA markers in blood for age estimation of the donor of a biological stain

RNA has gained a substantial amount of attention within the forensic field over the last decade. There is evidence that RNAs are differentially expressed with biological age. Since RNA can be co-extracted with DNA from the same piece of evidence, RNA-based analysis appears as a promising molecular alternative for predicting the biological and hence inferring the chronological age of a person. Using RNA-Seq data we searched for markers in blood potentially associated with age. We used our own RNA-Seq data from dried blood stains as well as publicly available RNA-Seq data from whole blood, and compared two different approaches to select candidate markers. The first approach focused on individual gene analysis with DESeq2 to select the genes most correlated with age, while the second approach employed lasso regression to select a set of genes for optimal prediction of age. We present two marker lists with 270 candidate markers for each approach.

Camera model identification based on dual-path enhanced ConvNeXt network and patches selected by uniform local binary pattern

With the rapid advancement of multimedia technologies, there is a growing demand for reliable methods to verify image integrity. Camera model identification, a passive approach aiming to determine the specific capturing device model, has garnered considerable attention in the field of source camera forensics. In this paper, we first propose a novel patch selection method that enhances the diversity of training data by utilizing the uniform local binary pattern operator to reveal spatial textual information. Secondly, we introduce a complex dual-path enhanced ConvNeXt network for camera model identification, effectively leveraging the multi-frequency information present in the image. Notably, our network demonstrates the ability to learn camera model-related features without relying on a residual prediction module. Finally, extensive experimental results on both Dresden and Vision datasets shown that the proposed network outperforms several state-of-the-art methods in both teams of identification accuracy and computational efficiency.

Intelligent Eye-Tracker-Based Methods for Detection of Deception: A Survey

Over the last few years, a large number of studies have been conducted on the monitoring of human behavior remaining beyond conscious control. One area of application for such monitoring systems is lie detection. The most popular method currently used for this purpose is polygraph examination, which has proven its usefulness in the field and in laboratories, but it is not without its drawbacks. Technological advances in data acquisition and automated analysis have ensured that contactless tools are in high demand in security fields like airport screening or pre-employment procedures. As a result, there has been a shift in interest away from traditional polygraph examinations toward the analysis of facial expressions, voice, and speech patterns, as well as eye-tracking signals to detect deceptive behavior. In this paper, we focus on the last aspect, offer a comprehensive overview of two distinct lie detection methodologies based on eye tracking, and examine the commonly used oculomotor feature analysis. Furthermore, we explore current research directions and their results within the context of their potential applications in the field of forensics. We also highlight future research prospects, suggesting the utilization of eye tracking and scan path interpretation methodologies as a potential fully functional alternative for the conventional polygraph in the future. These considerations refer to legal and ethical issues related to the use of new technology to detect lies.

Anthropometric Study of the Cephalic Index of Anambra State Students in Madonna University Nigeria

Background: Cephalometry is a subfield of anthropometry that involves the estimation of the cephalic index. The cephalic index is a significant metric used to ascertain the race and sex of an unidentified individual. Materials and Methods: This study was descriptive and cross-sectional and was conducted on Anambra State students at Madonna University. Elele campus, Rivers State, aged 16–24 years. The cephalic index was determined by applying the following formula: the maximum breadth of the head divided by the maximum length of the head, multiplied by 100. Hundred subjects, comprising 150 males and 150 females from Anambra State at Madonna University, were measured. Anthropometrical parameters measured were maximum head breadth and maximum head length. Results and Discussion: The analysis shows that there is no significant difference in the age groups with the cephalic indices of males and females. The prevalence of hyperbrachycephaly is significantly higher in males and females, while dolicocephaly is observed infrequently. Conclusion: This study shows that the males and females showed no differences on the basis of the cephalic index. It also reveals that the students in this study share similarities with Urhobo males and females based on their head types. The findings of this study will hold significant implications for the fields of forensics, medicine, and biometrics.

Analysis of the fluorescent properties of vaginal fluid upon ageing

Detection and identification of body fluids are crucial aspects of forensic investigations, aiding in crime scene reconstructions and providing important leads. Although many methods have been developed for these purposes, no method is currently in use in the forensic field that allows rapid, non-contact detection and identification of vaginal fluids directly at the crime scene. The development of such technique is mainly challenged by the complex chemistry of the constituents, which can differ between donors and exhibits changes based on woman’s menstrual cycle. The use of fluorescence spectroscopy has shown promise in this area for other biological fluids. Therefore, the aim of this study was to identify specific fluorescent signatures of vaginal fluid with fluorescence spectroscopy to allow on-site identification. Additionally, the fluorescent properties were monitored over time to gain insight in the temporal changes of the fluorescent spectra of vaginal fluid. The samples were excited at wavelengths ranging from 200 to 600 nm and the induced fluorescence emission was measured from 220 to 700 nm. Excitation and emission maps (EEMs) were constructed for eight donors at seven time points after donation. Four distinctive fluorescence peaks could be identified in the EEMs, indicating the presence of proteins, fluorescent oxidation products (FOX), and an unidentified component as the dominant contributors to the fluorescence. To further asses the fluorescence characteristics of vaginal fluid, the fluorescent signatures of protein and FOX were used to monitor protein and lipid oxidation reactions over time. The results of this study provide insights into the intrinsic fluorescent properties of vaginal fluid over time which could be used for the development of a detection and identification method for vaginal fluids. Furthermore, the observed changes in fluorescence signatures over time could be utilized to establish an accurate ageing model.

Determination of Telomere Length to Infer Forensic Age

Accurate age information is crucial for the identification of criminal cases in the field of forensics. Although the forensic age can be roughly estimated via bone age detection, there was no mature and reliable method for forensic age inference utilizing biological materials such as soft tissue fragments and no other markings left at the scene of the crime. Telomeres are cap-like structures found at the end of chromosomes in numerous tissues and cells and they are made up of repetitive sequences and binding proteins. This paper discussed the possibility of using telomere length to confirm the forensic age of a victim by concerning the biological characteristic of telomere and relevant techniques of length measuring. The structure of telomere enables it to prevent gene loss, fuse with other chromosome ends. Comparisons concentrating on the pros and cons of the existing measure of investigating the telomere are provided in this study by referring to those possible realistic scenes of the crime. As telomere length typically declines with every cell division as a result of the lagging strand's unsuccessful replication, the relationship between the shortening of telomere length and forensic are also shown for various types of cells. Further research in the field of investigating outliers in the pattern of shortening in telomere should be discovered and listed for various types of cells in order to make this method perfect and generalize it. This essay can provide some ideas for the study of telomere length inference of forensic age.

Age-related changes in sperm DNA methylation and their forensic and clinical implications.

As a link between a stable genome and a dynamic environment, epigenetics is a promising tool for mapping age-related changes in human DNA. Methylated cytosine changes at specific loci are generally less studied in sperm DNA than in somatic cell DNA. Age-related methylation changes can be connected to various reproductive health problems and multiple disorders in offspring. In addition, they can be helpful in forensic fields, where testing of specific loci in semen samples found at sexual assault crime scenes can predict a perpetrator's age and narrow down the police investigation. This review focuses on age-related methylation changes in sperm. It covers the biological role of methylation, methylation testing techniques and the implications of methylation changes in forensics and clinical practice.

Enhanced Adaptive Over-Segmentation Technique for Image Forgery Detection under Noise Attacks

The problem of picture fraud has become more pervasive in the current digital era as a direct result of the ease with which complex image manipulation programs can be accessed. This research study provides a comprehensive analysis of several methodologies and approaches that are used to detect and localize cases of picture fraud. The primary objective is to provide a clear understanding of the most cutting-edge methods that are now being utilized, as well as the problems and opportunities that lie ahead. In the field of picture forensics, the focus of this article is on several distinct types of image fraud, such as copy-move, splicing, retouching, and inpainting forgeries. We study the underlying principles that lie behind frequently used detection methods, such as block matching, feature-based analysis, and approaches that are anchored in deep learning. In addition, we go over the benefits and drawbacks of each strategy, focusing on how they apply to a variety of settings. In addition, this article takes a look at the datasets that are typically utilized for the training and evaluation of forgery detection algorithms, highlighting both the benefits and the limitations of those datasets. In addition to this, we examine the numerous assessment metrics that are utilized in order to evaluate the performance of the various methods, with a particular focus on the requirement for standardized benchmark datasets and evaluation methodologies. In addition, we discuss the obstacles that are presented when attempting to identify picture fraud in the real world. These issues include the need to deal with photos that have been compressed, images that have varying resolutions, and the existence of post-processing effects. In this article, we will discuss the significance of multi-modal analysis and the fusion of information obtained from a variety of sources in order to improve the reliability of counterfeit detection systems. The approaches that are used to detect picture counterfeiting are the topic of the next section of this review. We look at several techniques, such as those based on segmentation, texture analysis, and deep neural networks, in order to determine the precise position of forged sections inside a picture. We go through their precision, the amount of computing complexity they have, and the possible uses they have. In conclusion, we discuss the potential of the future. In order to identify and localize forged images, it is necessary to keep up with the latest innovations in image editing software and build forgery detection systems that are more technologically advanced and effective. This is because picture editing techniques are always being refined and improved. We offer prospective research avenues, such as Explainable AI, Generative Adversarial Networks (GANs) for forgeries production, and hybrid techniques to combine the strengths of various detection methods. Specifically, we focus on hybrid approaches to combine the strengths of diverse detection methods. In conclusion, the purpose of this study is to offer academics and industry professionals in This article provides a comprehensive review of the present state of identifying and localizing picture forgeries, which falls under the umbrella of image forensics as a discipline. It encompasses the most recent developments in forgery detection and localisation, making it possible to gain an in-depth comprehension of the subject matter. It is possible to pave the way for more effective and reliable forgery detection systems to protect the integrity of digital pictures in a variety of applications if we grasp the existing approaches as well as the obstacles that they provide.

Real-Time Detection and Identification of Suspects in Forensic Imagery Using Advanced YOLOv8 Object Recognition Models

Rapid advancements in artificial intelligence, machine learning, deep learning, coupled with easy access to high-capacity processing hardware, expansive organized datasets, and the evolution of artificial intelligence algorithms, have extensively influenced numerous fields. Digital Forensics is one such discipline where the application of artificial intelligence has been significantly amplified in recent years. The analysis of extensive image and video files derived from forensic evidence presents challenges in terms of time efficiency and accuracy. To surmount these challenges, artificial intelligence models can be employed to perform identification and classification processes on these data, thus expediting the resolution of forensic cases with enhanced precision. In the current study, state-of-the-art pre-trained YOLOv8 object recognition models - nano, small, medium, large, and extra-large - were utilized. These models were trained on the Wider-Face dataset with the objective of identifying suspects from images and videos sourced from digital materials in the field of digital forensics. The models achieved mean Average Precision (mAP) values of 97.513%, 98.569%, 98.763%, 98.775%, and 99.032% respectively. The YOLOv8 architecture demonstrated superior performance, outperforming the YOLOv5 architecture by a margin of 7.1% to 8.8%. To aid digital forensic experts in the detection and identification of suspicious individuals, a desktop application capable of real-time image analysis was developed.

Age estimation in the combined long bones and ribs by histomorphometry: Past, present, and future.

Numerous age estimation methods in unidentified bone have been a long time developing for application in forensic anthropology. The histomorphometric technique is one of the alternative methods that relied upon the evaluation of the cortical bone microstructure over the lifespan as a result of the remodeling process in bone. Remodeling is a sophisticated event occurring from the coupled function of bone formation and resorption cells for maintaining mineral homeostasis and repairment of microdamage in bone tissue. Products derived from remodeling are primary changes in the osteon or haversian system in various regions in the cortical bone, including periosteum, endosteum, and trabecular bone. Throughout life, bone remodeling rate with osteon alteration can be predictable. In the forensic field, histological methods are getting more attention due to the unavailability of macroscopic methods. Histomorphometry approach can be accomplished in fragmentary or incomplete bone remains indicating the limited use of gross morphological methods. In addition, the microscopic methods can aid to increase the more accuracy of analyses and diminish the biased subjective assessment for determining age. Most histomorphometry method utilizes a cross-section of the midshaft of the long bones including the mandible, rib, and clavicle. This review provides the basic knowledge of bone biology and anatomy, several age-estimating methods of histology, and crucial factors for age methods. Studies regarding overall age determination methods from the past until now contribute to obtaining more benefits for developing methods of histomorphometry using human bone in forensic identification.