Forensic Field Research Articles

Nails as optimal source of DNA for molecular identification of 5 decomposed bodies recovered from seawater: from Y-ancestry to personal identification.

Molecular identification of extremely compromised human remains in forensic field is usually performed from DNA typing of bones, which are a difficult sample to work with. Moreover, autosomal STR profiles do not always result in the identification of the donor due to lack of comparisons or non-hit throughout database searching. An attempt to overcome these issues is represented by fingernails as an alternative DNA source and Y-STRs typing to infer both geographical and familial ancestry of the unknown donor. In this study, we analyzed both 24 autosomal and 27 Y-chromosome STRs from unidentified human remains (UHRs) of five males recovered from the water near the southwestern coast of Sardinia by the Italian Harbor Master's Office. Nail clippings provided an optimal source of autologous DNA for molecular identification in a very short time, producing complete autosomal and Y-STR profiles even under conditions of high body degradation. Unfortunately, no match neither compatibility occurred using autosomal STRs (aSTRs), initially. Upon analyzing the Y-haplotypes, we found out they had already been observed in northern Africa, providing us important investigative leads. This prompted the International Criminal Police Organization (INTERPOL) to provide us with references of alleged relatives that were then confirmed to be related. The use of fingernails represents an excellent DNA source especially for genetic identification of decomposed bodies recovered in seawater environment. Notably, DNA extracted from nails gave high-quality Y-STR haplotypes by which predicting paternal ancestry of the unidentified donors may result fundamental in the forensic investigative context.

A Study of Original and Tempered Images for Real-Fake Image

The widespread use of image editing technologies in the digital age has raised concerns about the authenticity of visual content. This study delves into the field of image forensics, specifically analyzing original and tempered photos to determine their graphical behavior. The major goal is to develop solid algorithms for distinguishing between authentic and fraudulent photos based on an in-depth assessment of their visual properties. The study makes use of a large data collection that includes both original and manipulated photographs from a variety of sources and contexts. To reveal small differences between authentic and modified pictures, image processing techniques such as noise analysis, color profile investigation, and geometric feature extraction are used. Machine learning algorithms are critical in automating the analysis process and increasing the efficiency and scalability of the proposed methodology. Picture security is an issue for every company that employs digital images. Suspect data has long been used in forensics and public safety pictures, images from crime scenes, biometric photos, and other types of images. In this discipline, the usage of digital photographs has increased dramatically with the advancement of digital imaging. Digital image processing has made picture manipulation easier, but it has also aided in the development of several novel techniques in forensic investigation. Digital picture authenticity is becoming an issue due to the public availability of several programs for cropping and manipulating images. It serves as compelling evidence in many different types of crimes write for a variety of purposes. This development is picture processing or edits of two are also simplifies and editing photos. The most typical kinds of Conducted.

Fingerprint Recognition in Forensic Scenarios.

Fingerprints are unique patterns used as biometric keys because they allow an individual to be unambiguously identified, making their application in the forensic field a common practice. The design of a system that can match the details of different images is still an open problem, especially when applied to large databases or, to real-time applications in forensic scenarios using mobile devices. Fingerprints collected at a crime scene are often manually processed to find those that are relevant to solving the crime. This work proposes an efficient methodology that can be applied in real time to reduce the manual work in crime scene investigations that consumes time and human resources. The proposed methodology includes four steps: (i) image pre-processing using oriented Gabor filters; (ii) the extraction of minutiae using a variant of the Crossing Numbers method which include a novel ROI definition through convex hull and erosion followed by replacing two or more very close minutiae with an average minutiae; (iii) the creation of a model that represents each minutia through the characteristics of a set of polygons including neighboring minutiae; (iv) the individual search of a match for each minutia in different images using metrics on the absolute and relative errors. While in the literature most methodologies look to validate the entire fingerprint model, connecting the minutiae or using minutiae triplets, we validate each minutia individually using n-vertex polygons whose vertices are neighbor minutiae that surround the reference. Our method also reveals robustness against false minutiae since several polygons are used to represent the same minutia, there is a possibility that even if there are false minutia, the true polygon is present and identified; in addition, our method is immune to rotations and translations. The results show that the proposed methodology can be applied in real time in standard hardware implementation, with images of arbitrary orientations.

A study of ear biometrics in autopsied cases at the Universiti Kebangsaan Malaysia Medical Centre

BackgroundThe ears have increasingly been recognized as one of the supportive tools in forensics, based on the identification of landmark variations of ear biometrics in living persons. However, no studies on the reliability of such comparisons have been done on the deceased.MethodsThe study aimed to investigate the correlation between ear biometrics and the age, sex, and stature of the deceased. The study was conducted on 181 deceased persons, aged between 18 and 70 years old on cases received by the Forensic Unit of Universiti Kebangsaan Malaysia Medical Centre. Documentation of age, sex, race, and height was recorded, and photographs of bilateral ears were taken. Measurements of twelve ear biometrics based on the Iannarelli method and ear length and ear width were taken from the photographs.ResultsResults showed that there was a significant difference between males and females in six ear biometrics. There was also a significant correlation between ear biometrics, that is, ear length and ear width with the age and height of an individual.ConclusionsIn brief, there exists a significant difference between males and females in ear biometrics with good correlations between ear biometrics and the height and age of an individual. Hence, the ear can be used for personal identification in the forensic field.

Exploring the Effectiveness of Machine Learning Algorithms in Image Forgery Detection

This study investigates the efficacy of various machine learning algorithms for detecting image forgery, a prevalent issue in the realm of digital media manipulation. The research focuses on assessing the performance of these algorithms in accurately identifying instances of image tampering, aiming to contribute valuable insights to the field of digital forensics. The evaluation encompasses a diverse set of machine learning techniques, including but not limited to convolutional neural networks (CNNs), support vector machines (SVMs), and decision trees. Through rigorous experimentation and comparative analysis, the research aims to discern the strengths and limitations of each algorithm in the context of image forgery detection. The findings of this study hold significance for enhancing the capabilities of digital forensics tools, thereby aiding in the mitigation of fraudulent activities, and ensuring the integrity of visual content in the digital' domain.

Synthesis of polypyrrole (PPY) functionalized halloysite nanotubes (HNTs): An electrochemical sensor for ibuprofen

Over-the-counter availability of non-steroidal anti-inflammatory drugs (NSAIDs) without prescription, for medicinal use has led to abuse in accidental and intentional suicides. In this context, the present study accentuates on the development of simple and reliable electrochemical sensor based on the HNTs coated with in-situ polymerized polypyrrole (HNT/PPY) for detection of ibuprofen (IBP). Surface functionalization of HNTs with PPY increased the electron rich centres on the electrode surface with an evident ∼8 fold increase in electrochemical active surface area (EASA) as compared to bare electrode, facilitating electron transfer rate. The analytical and electrochemical characterization of the sensor was carried out with TEM, EDX-mapping, FTIR, XPS, XRD, CV and DPV. The proposed sensor exhibited a sensitive response towards oxidation of IBP with linear dynamic range (LDR) of 0.1–100 µM and offers a fairly good limit of detection (LOD) of 0.9 µM. The practical utility of the sensor was tested for real time detection of IBP from biological matrix (urine). Sensitive, selective and stable response of HNT/PPY towards electrochemical oxidation of IBP suggested it is a promising platform for IBP detection and can be used in forensic field and clinical set ups.

Facial reconstruction of a deformed skull from the Roman period of Juliopolis

The use of digital technologies in archaeological research has become considerably more widespread in recent years. While the earliest records of the use of electronic data in archaeology date from the 1950 and 1960s, the field has kept growing with the increasing interest in digital tools such as digital photography, three-dimensional imaging, GIS, virtual and artificial reality applications, CAD and facial reconstruction. Such digitization practices are used frequently for documenting, record keeping, and preserving and representing the data recovered from archaeological contexts, and are linked to other fields, such as cultural heritage studies and museology. Facial reconstruction is used to recreate the faces of individual from the past using their skeletal remains. It has been practiced for different reasons in the fields of forensics, anthropology, and archaeology. This study focuses on the facial reconstruction of a skull unearthed from Juliopolis. The reason for choosing this skull in this study is that it is the first and only known deformed skull dated to the Roman period in Anatolia. For the facial reconstruction application, authors created a 3D modelling of the skull by photogrammetry. Then, the missing parts of the skull are completed using the virtual donor library. The results are presented in different forms, as grayscale, in colour with eyes closed or open. Also, the type of deformation is demonstrated in the skull. This study adopts a digital process and is more strongly linked to data interpolation related to the external surface of the face. The prominent advantage of the methodology is being strongly supported by information obtained from computed tomography (CT) scans of living individuals, leaving a little room for the artistic issue in relation to the basic aspect resulting from the interpolation of data. The benefit of facial reconstruction is especially important since it is a way of preserving and presenting archaeological and anthropological data.

Determination of Δ9-Tetrahydrocannabinol and Cannabidiol by Voltammetry using Low-Cost Screen-Printed Electrodic Platforms: A Significant Potential for Application in Clinical and Forensic Fields

Cannabis sativa has been utilized for centuries due to its psychoactive and therapeutic effects resulting from the presence of cannabinoids, which are exclusively found in plants of the Cannabis genus. The methods currently used by law enforcement and pharmaceutical industries to detect and measure cannabinoids include colorimetric testing, as well as instrumental methods such as HPLC and GC-MS. The aim of this work is to create a new method that can overcome the limitations of existing methods. To achieve this goal, a voltammetric method was developed using CV and LSV techniques. The method used a 3D-printed electrodic platform that was chemically modified with carbon paste and silver ink. The developed electrode was successful in detecting and measuring the amounts of Δ9-THC and CBD. The analytical parameters and average concentrations obtained were similar to those of popular commercial electrodes such as the platinum disk electrode and SPPE. The values for the limits of detection (LOD) and quantification (LOQ) were in the range of µmol L-1. The developed method and electrode show promise for detecting and quantifying Δ9-THC and CBD. The method was compared to the GC-MS technique, yielding relative error values ranging from 2.27% to 18.66%. A miniaturized, portable, versatile, and cost-effective method was developed with significant potential for clinical and forensic applications.

Advances in biosensors for poison detection applications

This paper discusses the applications and challenges of biosensors in drug and toxin detection, highlighting the practical value of biosensors and their role in forensic investigations, medical research, and environmental monitoring. Because drug abuse poses a major threat to human health and society, there is an urgent need for technologies that can quickly, portable, and sensitively detect drugs in common biological samples. This paper introduces the detection of animal and plant toxins, including snake venom, spider toxins, cyanide in plants, etc., and discusses the importance of detection of these toxins in medical and forensic fields. The development of biosensor technology, including the application of optical biosensors and electrochemical biosensors based on enzyme inhibition, is also summarized, and the role of these sensors in drug research and environmental monitoring is introduced. In addition, the paper highlights the potential of nanotechnology to improve the sensitivity and specificity of biosensors, as well as the future applications of biosensors in health, environmental monitoring, and agriculture. Finally, the paper points out the limitations of current biosensor research, and puts forward the direction of future research, emphasizing the need to develop simpler, more sensitive and more specific biosensors to broaden their practical applications.

Discrimination of Diptera order insects based on their saturated cuticular hydrocarbon content using a new microextraction procedure and chromatographic analysis.

The nature and proportions of hydrocarbons in the cuticle of insects are characteristic of the species and age. Chemical analysis of cuticular hydrocarbons allows species discrimination, which is of great interest in the forensic field, where insects play a crucial role in estimating the minimum post-mortem interval. The objective of this work was the differentiation of Diptera order insects through their saturated cuticular hydrocarbon compositions (SCHCs). For this, specimens fixed in 70 : 30 ethanol : water, as recommended by the European Association for Forensic Entomology, were submitted to solid-liquid extraction followed by dispersive liquid-liquid microextraction, providing preconcentration factors up to 76 for the SCHCs. The final organic extract was analysed by gas chromatography coupled with flame ionization detection (GC-FID), and GC coupled with mass spectrometry was applied to confirm the identity of the SCHCs. The analysed samples contained linear alkanes with the number of carbon atoms in the C9-C15 and C18-C36 ranges with concentrations between 0.1 and 125 ng g-1. Chrysomya albiceps (in its larval stage) showed the highest number of analytes detected, with 21 compounds, while Lucilia sericata and Calliphora vicina the lowest, with only 3 alkanes. Non-supervised principal component analysis and supervised orthogonal partial least squares discriminant analysis were performed and an optimal model to differentiate specimens according to their species was obtained. In addition, statistically significant differences were observed in the concentrations of certain SCHCs within the same species depending on the stage of development or the growth pattern of the insect.

Improving uranium oxide pathway discernment and generalizability using contrastive self-supervised learning

In the field of Nuclear Forensics, there exists a plethora of different tools to aid investigators when performing analysis of unknown nuclear materials. Many of these tools offer visual representations of the uranium ore concentrate (UOC) materials that include complimentary and contrasting information. In this paper, we present a novel technique drawing from state-of-the-art machine learning methods that allows information from scanning electron microscopy images (SEM) to be combined to create digital encodings of the material that can be used to determine the material’s processing route. Our technique can classify UOC processing routes with greater than 96% accuracy in a fraction of a second and can be adapted to unseen samples at similarly high accuracy. The technique’s high accuracy and speed allow forensic investigators to quickly get preliminary results, while generalization allows the model to be adapted to new materials or processing routes quickly without the need for complete retraining of the model.

Quantization Step Estimation of Color Images Based on Res2Net-C With Frequency Clustering Prior Knowledge

The quantization step is a crucial parameter in JPEG compression, that can reveal the compression history of a JPEG image. Estimating the quantization steps for single compressed and recompressed images is attracting considerable interest in the field of image forensics and steganalysis. Several effective methods have been proposed, but the performance of these methods still needs to be improved on small-sized and low-quality images. To solve the above problems, feature enrichment is performed on images in the frequency domain, resulting in clustering discrete cosine transform (DCT) coefficients of the same frequency. Then, we construct a hierarchical connection within the residual blocks of the network to represent multi-scale features, enabling the network to learn deep features of the image. At the same time, we use multiple small-sized convolution kernels instead of one large-sized convolution kernel to minimize the impact of block artifacts. Based on the above two ideas, we construct a network model, Res2Net-C, to discover information about the quantization steps in the frequency domain. The integration of multi-channel information of color images is achieved by multi-channel convolution, and the quantization steps of the chrominance and luminance channels of the color images are estimated. The experimental results show that the accuracy of the proposed method for estimating the quantization steps is 29.97% better than that of the existing algorithm with a single compressed dataset and 4.87% better than that of the existing algorithm with a recompressed image dataset. In addition, the method has good performance with mixed datasets that contain both single compressed and recompressed images.

Fusion of Forensic Analysis of Mobile Devices: Integrating Multi-Criteria Decision Methods and Case Study Insights

This study employed a Multi-Criteria Decision Analysis (MCDM) approach, utilizing the DEMATEL and TOPSIS methodologies, to assess the effectiveness of forensic tools designed for mobile devices, with a specific emphasis on Android and iOS platforms. The investigation evaluated technologies used for collecting, retrieving, and validating data in the Cyber Forensic Field Triage paradigm, with a focus on rapidly identifying and interpreting digital evidence. The study incorporated several factors and expert preferences, concluding that the Android Triage and Andriller tools were the most efficient.

Analysis of the Correlation between Cholesterol Levels in Blood Using Clinical Data and Hair Using Mass Spectrometry Imaging.

Mass spectrometry imaging (MSI) is a technique that visualizes the distribution of molecules by ionizing the components on the surface of a sample and directly detecting them. Previously, MSI using hair has primarily been used in the forensic field to detect illegal drugs. On the other hand, there are few examples of using this technology for health monitoring. In this study, hair and clinical data were collected from 24 subjects, and the correlation between blood cholesterol levels and cholesterol detected from cross-sectional hair slices was analyzed. As a result, a positive correlation with a correlation coefficient of 0.43 was observed between blood cholesterol and cholesterol detected from hair. Furthermore, when comparing the results of fluorescence staining (FS) of hair cholesterol with Filipin III and the MSI results, it was found that while FS could visualize detailed hair structures, there were cases where the results differed from MSI, possibly due to some cholesterol loss during the staining process. In the future, if various disease biomarkers can be detected using hair MSI, it could potentially become a non-invasive diagnostic method.

A Static Approach for Malware Analysis: A Guide to Analysis Tools and Techniques

Abstract: Malicious code presents a severe risk to computer systems, making work difficult for information security and cyber experts. Malware analysis is in great demand because of its importance and function in digital forensics and cyber security. Malware, often known as malicious software, is purposefully written software that harms or damages people, computers, servers, or networks. An overview of malware analysis methods and techniques in the fields of digital forensics and cyber security is given in this article. The study examines several malware types, their characteristics, and analysts' challenges in locating and analyzing them. It also highlights the importance of continuing this field's research and development to stay ahead of evolving malware threats. Trojan horses, worms, backdoors, rootkits, and adware are examples of malware. There are several methods for analyzing malware, but one of the most well-known is static analysis. This article will look at several methods for doing malware analysis and detection on corporate systems, as well as the resources available to assist with sample inspection to reduce the impact of malware assaults on an organization's operations. The investigator must first choose which methods and instruments to use for analysis. Static analysis, which includes malware scanners and detectors, is the first line of defense against malware. As technology advances, malware creators use various techniques to conceal their source code from scanners and detectors that search for strings, pattern matching, and other similar patterns to determine hash values that may be used to identify the infection. Malware experts decompress the packed file into unpacked one to examine obfuscated malware. This study examines efforts to investigate the many techniques and instruments malware uses in the real world.

The triple oxygen isotope signature of uranium oxides in the nuclear fuel cycle

Uranium oxides, in the form of U3O8 and UO2, are principal compounds in the uranium nuclear fuel cycle and attract significant interest in the general field of nuclear forensics, for the development of new and credible signatures to support attribution. This study presents, for the first time, the analytical method and high-precision measurements of the triple oxygen isotope composition of U3O8 and UO2. We show that Δ’17O can be measured at the accuracy and reproducibility needed for nuclear forensics. Δ’17O exhibits a wide range, from -450 to -120 ppm, for both commercial and synthetic uranium oxides. The presentation of the data in Δ’17O-δ’18O space provides a two-dimension identification of uranium oxides for nuclear forensic characterization of samples. The relation between Δ’17O and the process from which the oxide was formed or manipulated, has the potential to trace the chemical history of the material. We determined the change in Δ’17O of the system U3O8 – atmospheric O2 resulting from isotope exchange at the temperature range of 298 K to 973 K. The dependency of the triple isotope exponent, θ, on temperature was calculated for this exchange reaction, concluding that the phase transition from pseudo-hexagonal to hexagonal structure plays a major role in determining Δ17O and θ. A set of reduction reactions of U3O8 to UO2 indicate that Δ’17O undergoes minimal change during this process.

An association between Landsteiner-Rhesus typing of blood and cheiloscopy among female students in Bengaluru Rural: Forensic Medicine perspective

Introduction: Cheiloscopy is the term used for the study and analysis of lip impressions. In the field of forensics, identification is of utmost importance.Objective: To study the relationship between lip prints and blood groups in female students in rural Bengaluru, Karnataka, India.Materials: Lip prints and blood groups were obtained and recorded using cotton swabs, lipstick, antisera for slide agglutination test, slides, lancelets, sanitizer, and hand towels.Methods: 189 students from the MVJ Medical College and Research Hospital participated after giving informed written consent. By applying cello tape to lips that had been painted with lipstick and then affixing the tape to the proforma sheet, the lip prints were collected. Suzuki and Tsuchihashi classification was used to categorize the same. The slide agglutination method was used to determine the blood group. Statistical analysis was done using the chi-square test. P value of 0.05 was considered of statistical significance.Results: Type II or branching type was the most common lip print in our study. Most individuals (181 out of 189 participants) belonged to the Rh-positive blood group. The study participants with blood type O were the most commonly seen. Association between type II lip print and blood group O were found to be the most common.Conclusions: This research shows the relationship between cheiloscopy and blood groups. These are frequently discovered evidence at a scene of the crime and are crucial for identification.

Highly luminescent heteroatom codoped carbon dots for multitudinous applications

Carbonaceous nanomaterials have shown great potential in the analytical and environmental domains due to their natural intriguing properties. Rich in quantum yield and reliable performances are necessary for carbon dots to be effectively used in the real-world applications. Carbon dots (CDs) exhibits high blue-emission with a quantum yield (ϕ) of 11% were synthesized through a single-step superficial pyrolysis technique. In this work, the derived carbon dots were employed as reducing and stabilizing agents in a unique one-step approach for the synthesis of AgNPs. The fluorescence of CDs was found to be quenched in the presence of dopamine with greater sensitivity, the minimum limit of detection was determined as 0.35 μM and possible mechanism could be the inner filter effect along with photoinduced electron transfer. In addition to this, the outstanding optical properties of CDs enable them to use as invisible ink in loading secret information and provide enhanced counterfeit protection. The solid-state quenching is successfully prevented when the CDs were dispersed in a polymer matrix for solid-state films preparation. The CDs-polymer composite is packed on top of a 365 nm UV LED chip, which emits blue light, to fabricate blue LED. Hence, we suggest that the synthesized CDs may serve as a future trend setter by as a single molecular probe for multifarious applications such as optoelectronics and forensic fields. Previously, we utilized these CDs for fingerprint detection and now apply them for new emerging applications.

A mini review on biosensor advancements-emphasis on quantum dots

The biosensor is an analytical device that targets the macromolecules and small molecules using a biological recognition system and later converts it into a measurable signal using a transducer. These biosensors are broadly used in various domains, enhancing the existing form and making it more specific for several applications. Presently, it is used for quantifying and detecting the biological-based components in the fields of forensics, medicine, food quality, pharmaceutical and environmental tracking, etc. These biosensors are cost-effective and produce safe and assured treatment for patients by examining the contagious disease biomarkers in the early stage without any side effects. This review has discussed the five essential components of biosensors and the upgraded form of these biosensors. Depending on the detection method, biosensors were classified into different types. Amongst various types, electrochemical and optical biosensors have more sensitivity and are widely used, and are briefly discussed here. Nanomaterials play a significant role in modern medical devices due to their excellent selectivity, high surface-to-volume ratio, strong chemical activity, increased specificity, and sensitivity that pave the way for more discoveries. Several nanomaterial-based biosensors are already on the market. This review mainly focuses on the fabrication methods of metal and metal oxide nanoparticles for sensing applications.

Body Farms: A Field of Opportunity

Human Taphonomic Facilities (HTF)—or as they are more commonly known, body farms—have grown in number since the first facility opened in 1980. As recognition of their utility grows, it is worth reconsidering their origins in light of the generative value they provide and the challenges yet to overcome. This paper examines the first HTF in depth, tracing how it came about and the advances it has made to the field of forensics. I address the ethical implications that body farms pose, but also the solutions they have to offer.