In recent years, telecommunications fraud cases have become increasingly prevalent, with criminals continuously refining their fraud tactics. From initially exploiting mainstream instant messaging platforms like WeChat and QQ, perpetrators have shifted to luring victims into specially crafted apps. To streamline development and reduce costs, criminals embed third-party SDK interface codes into these illicit apps, with IM (Instant Messaging) services being a common type. In these novel fraud scenarios, conducting both dynamic and static analyses of the chat-focused apk files involved can yield valuable forensic leads regarding IM services and access databases containing crucial information for investigation and prosecution. This article, taking the Android system as an example, meticulously outlines the process of mining forensic clues from instant messaging apps and subsequent data analysis, encompassing technical principles, analysis and processing steps, and case applications. It emphasizes techniques such as extracting key values through apk static analysis, validating those keys via dynamic packet capture, and utilizing SQL queries to sift through and analyze chat logs, thereby offering a professional methodological reference for evidence gathering in related cases.

The presence of close non-matches poses inherent risks to fingerprint identification, and exploring their occurrence patterns in different regions of the fingerprint can enhance the risk awareness of identification personnel. To investigate this, 30 fingerprints from the central and branch regions of loops were selected as research subjects. Each fingerprint was queried 15 times within a database of ten million individuals to examine the occurrence of homologous fingerprints and close non-matches when 6 to 20 minutiae were marked. The results revealed that close non-matches could be found in both the central and branch regions of loops when 6 to 20 minutiae were marked, and notably, the number of close non-matches in the branch region was significantly higher than in the central region. The proportion of homologous fingerprints ranked higher than close non-matches in the central and branch regions of loops, with respective percentages of 98.4% and 60.9%. Even when 20 minutiae were marked, there were still instances where homologous fingerprints did not appear in the branch region of the loop. Frequency analysis of similar regions indicated that the side of the central region facing the loop and the side of the branch region facing the branch were more susceptible to having close non-matches. Additionally, high-level close non-matches with over 10 matching points were identified in both the central and branch regions of loops. Therefore, fingerprint identification personnel must handle close non-matches with caution to prevent them from interfering with accurate identification.

As digital currencies, notably, Bitcoin, gain widespread adoption, the detection of illicit transactions poses a pressing challenge that requires prompt resolution. This research introduces a novel approach for detecting illicit Bitcoin transactions that integrates diverse features to enhance both detection efficiency and accuracy. Firstly, a comprehensive feature set is constructed by amalgamating conventional data features with those uniquely derived from LSTM, RandomWalk, and PageRank algorithms, enabling the capture of intricate patterns within transaction data. Secondly, to address the class imbalance inherent in Bitcoin transaction datasets, FocalLoss is adopted as the loss function, strengthening the model's ability to discern minority classes (i.e., illicit transactions). Finally, the model is validated on the Elliptic dataset using a multilayer perceptron (MLP) architecture with a single hidden layer, and its performance is compared with current mainstream Bitcoin illegal transaction detection models (GAT, GCN). Experimental results demonstrate that the proposed method achieves significant improvements in crucial metrics such as F1 score and recall rate compared to traditional methods, validating the effectiveness of the multi-feature fusion strategy and the utilization of FocalLoss in tackling the challenge of illegal Bitcoin transaction detection.

Human body odor arises from the secretion of various glands on the skin's surface, which, when acted upon by microorganisms, evaporate to produce a distinct scent. This odor contains valuable biological information, with certain compounds exhibiting strong stability and individual specificity, serving as “odor fingerprinting” that can distinguish between different populations. Machine learning is an important method for human odor research, which can not only explore the characteristic components of odor in different populations, but also investigate the differences between different individuals. This paper discusses the application of “odor fingerprinting” in individual identification and feature characterization, drawing upon recent literature. It outlines the data processing procedures involved in human odor analysis, highlights the challenges encountered, and explores current research trends. Finally, the application trends of the recognition of human odor are discussed in order to provide reference for odor recognition research.

In recent years, Chinese patent medicines and health care products have become more popular, but some businessmen have added illegal additions in them for profit. At present, there are many kinds of illegal additions. The objects of this study mainly include seven categories: medicines for regulating blood pressure, medicines for blood sugar regulation, medicines for blood lipid regulation and so on. These drugs may lead to a series of problems in cardiovascular, hepatic/renal function, which can be life-threatening in serious cases. In order to protect public health and rights, China has issued many standards and relevant guidelines. The main detection techniques are: immunoassay, spectroscopy, chromatography and the combination of various technologies and real-time direct analysis mass spectrometry. This paper summarized the categories of illegal additions in recent years, the commonly used sample pretreatment technology and the corresponding detection technology, and briefly introduced the characteristics of each technology. Finally, an outlook is given about the management of new illicitly added chemicals, the direction of development on related detection technologies, and the measures to deal with new illicitly added chemicals.

This paper aims to confirm the new psychoactive substance 1-(4-fluorobenzyl)-4-methylpiperazine (4F-MBZP) through a combination of multiple technologies. After ultrasonic extraction with methanol and separation by preparative liquid chromatography, the target was analyzed using a combination of gas chromatography-mass spectrometry (GC-MS), liquid chromatography-quadrupole/time-of-flight tandem mass spectrometry (LC-Q-TOF-MS/MS), Fourier transform infrared spectroscopy (FTIR) and nuclear magnetic resonance hydrogen spectroscopy (¹H-NMR), respectively. Through GC-MS analysis, the main characteristic fragment ions of the unknown compound with a retention time of 9.78 min were m/z 208.1, 164.1, 109.1 and 99.1; By means of LC-Q-TOF-MS/MS detection, the quasi-molecular ion peak of the unknown compound was determined to be m/z 209.286 4, and the main characteristic ion fragments obtained by MS² were m/z 189.183 0, 109.176 9, 99.452 0 and 83.387 1; The infrared characteristic absorption peaks of the target measured by FTIR analysis were 3 517.1, 2 981.5, 2 943.4, 1 603.9, 1 517.3, 1 232.0, 1 168.6, 1 080.2, 827.8 and 766.9 cm^-1, indicating that the molecular structure of the suspicious compound contains functional groups including benzene ring, methyl, methylene, and C-N; After further analysis of proton attribution in the unknown compound structure by ¹H-NMR and comparison with relevant literature that has been publicly reported, it was confirmed that the unknown compound is a new psychoactive substance called 4F-MBZP. This method is expected to be used in qualitative analysis of 4F-MBZP in seized drugs with simple operation and high accuracy, while providing reference for the detection and identification of new drugs.

This study investigates the method of using coaxial light to differentiate between laser printer and detect the addition of printed documents. Documents printed by laser printers of different brands and models are inspected using coaxial light, with the reflection of the printed text and graphics, specifically the degree of brightness or darkness, serving as a means to distinguish the printer models. The same method is used to inspect documents that have undergone additional printing on the same printer model as well as on different printer models, and the differences in the coaxial light response after the additional printing are analyzed. The experimental results show that the coaxial light response of documents printed by the same model in a single run and at different times remains basically stable, with minimal influence from paper quality and toner amount, making it a viable feature for differentiating laser printers. The coaxial light response varies significantly among documents printed by different models, with a high degree of differentiation; Moreover, the differences in the coaxial light response of the text and graphics in the added printed documents are even more pronounced, enhancing discrimination further. Overall, the study concludes that the coaxial light inspection method is a non-destructive, fast, and efficient method for forensic analysis of laser printed documents.

The human ear possesses a complex three-dimensional structure and exhibits relatively high stability. Research on the morphological characteristics of ear images holds significant importance for individual identification in cases of occluded or incomplete facial images in videos and photographs. This paper categorizes the visible ear features in frontal and profile facial images into three levels: global feature, local feature, and detailed feature. Based on a self-compiled ear image dataset from the Institute of Forensic Science, Ministry of Public Security, P.R.C. (comprising 2 078 individuals and 44 826 images), the study further subdivides each feature type, providing illustrations and reference markers to depict the corresponding characteristics, thereby establishing a relatively comprehensive feature system. Using morphological comparison and statistical analysis methods, the frequencies of global features (36 feature items), local features (58 feature items), and detailed features were separately analyzed. The statistical results indicate that morphological features such as the earlobe and crus of the helix are the most frequently observed in ear images and remain relatively stable under varying lighting conditions, angles, and resolutions. Some features are relatively rare (e.g. multiple helical notches or an antitragus that is narrower at the top and wider at the bottom), and their presence can provide valuable support for individual identification.

The handwriting characteristics of machine imitative signature in terms of writing style, layout, writing method are almost completely consistent with the imitated signature. A general analysis of the differences between machine imitative signatures and handwritten signatures in terms of various handwriting features makes little sense. On the contrary, there are more detailed differences on some handwriting characteristic like writing strength, writing speed and so on. In this experiment, the characteristics of stroke mark are taken as the research object, and the samples of human handwritten signatures are collected and the corresponding machine imitative signatures are made. The similarities and differences between them in stroke marks are observed and summarized by microscope. The results show that there are morphological differences between machine imitative signature and handwritten signature in ink dots, indentations, stroke width and thickness and so on. Machine-writing has strong repeatability and similarity in these features, and the same features always appear in other handwriting. Because of the different structure and function of the writing robot, the writing characteristics of the machine itself also affect the stroke characteristics of the machine imitative signature. For example, ink and white line features are not observed in machine imitative handwriting. In conclusion, the stroke marks of machine imitative signature have strong regularity and high similarity, while the stroke marks of handwritten signatures have strong randomness and rich changes. Affected by machine itself, the machine imitative signature and the human writing signature have obvious and fixed differences in stroke mark. It is a feasible handwriting inspection idea to identify machine-writting handwriting from the perspective of stroke mark.

In order to improve the development effect of latent blood fingerprint, this paper attempts to introduce fluorescent labeling technology into latent blood fingerprint development. By taking advantage of the characteristics of strong esterification activity of N-hydroxy-succinimide ester, easy reaction with the amino group of protein to form amide chemical bond and stable structure, a new fluorescence derivative 5-carboxyfluorescein-N-hydroxy-succinimide ester (CFSE) was synthesized and applied to latent blood fingerprint development area for the first time. In this paper, the developing ability of CFSE on latent blood fingerprint will be systematically explored. By setting a series of gradients, using comparative experiments, hypothesis testing and other methods, the operation method and optimal reagent formula of the fluorescein to develop latent blood fingerprints were determined. CFSE can be widely used in the development of latent blood fingerprints on non-permeable complex backgrounds and dark background objects, and has little effect on sample DNA extraction, and compared with acid yellow, the results showed that the fineness of the lines and the contrast with the background were higher. In addition, the study also determined the order of using CFSE in combination with the common methods for developing latent blood fingerprints. CFSE has the advantages of strong fluorescence brightness, high fineness, obvious contrast with the background, less fluorescence stains, and easy washing, and the overall effect is better, which provides a new method for latent blood fingerprints development.

Forensic SNP genealogy can infer distant kinship based on SNP chip data. In order to clarify the ability of genealogical inference technology based on SNP chip data to detect trace DNA in forensic field, in this study Illumina CGA microarray was used to detect the samples. The samples were preliminarily evaluated based on the DNA input, detection rate, sample heterozygosity and other indicators, then the IBS and IBD algorithms were used for forensic SNP genealogy. The classification consistency was compared with the reference samples, and the factors affecting the prediction accuracy were analyzed, the detection ability of the technology system for different input amounts of DNA were determined, and finally the accurate SNP typing data in the low-quality data were screened based on the signal ratio and other indicators, so as to improve the use value of the trace DNA detection data. The results show that when the DNA input was higher than 1.95 ng, the IBS algorithm had an average confidence interval accuracy of 94.33% and 91.96% for IBD, and when the input was 488－781 pg, the IBS algorithm had an accuracy of 23.11% for the average confidence interval for 1－5 kinship, while the IBD algorithm reached 30.13%. When the DNA input is less than 488 pg, both the IBS and IBD algorithms are unable to make genealogical inferences. Allele insertion is a major factor affecting the accuracy of pedigree inference, and when the homozygous error reaches 22.5%, the sample cannot be used for pedigree inference. By screening the signal ratio, the heterozygous SNP loci with a signal ratio greater than 1.5 can be removed, which can improve the genealogical inference ability of low-input samples. Based on the real family data of Illumina CGA chips, this study analyzed the influence of sample input on the accuracy of genealogical inference, and optimized the SNP data by signal ratio, so as to improve the application value of low-input samples in genealogical inference.

In the practice of document examination, the number of cases involving the determination of crossing sequence of printed and written strokes is increasing year by year, but there is a lack of national and industry technical standards for the identification of cross-timing between printed strokes and handwriting. This study created crossed stroke samples formed by random combination of black inkjet printing devices (categorized into dye-based ink and pigment-based ink) and ballpoint pen with different brands and models in different orders, and a VF20 three-dimensional high-magnification video fluorescence microscope was used to examine those samples. Additionally, through the control variable method and the single variable method, the study investigated the effects of factors such as paper, time interval between two kinds of ink marks, writing cushion and printing mode on the determination results of the crossing sequence between inkjet printer ink and ballpoint pen ink. The results showed that when the ink from two types of inkjet printers and ballpoint pen intersect in the same crossing sequence, there were some differences in microscopic performances. And except the printing mode factor, other variables had no significant influence on the result judgment. Therefore, for different types of crossed stroke samples, sequencing the superimposed lines with black inkjet printing ink versus ballpoint pen ink by VF20 High Mag Fluorescence Microscope is feasible. This research can provide important reference for the examination of related cases, and meet the increasing demand for sequencing the intersections of printed strokes with handwriting.

With the advancement of paperless offices, handwritten electronic signatures have become widely adopted across various sectors, including banking, government affairs, and commerce. These signatures refer to the textual impressions created by fingers or stylus pens on electronic screens, such as smartphones and digital tablets. Concurrently, dynamic attributes like writing duration, stroke sequence, and pen pressure are captured and stored electronically on computers. From an implementation perspective, handwritten electronic signatures fall within the broader category of electronic signatures. However, they differ from digital signatures in terms of implementation technology, presentation format, verification methods, and technological requirements. Given the distinct verification methodologies, handwritten electronic signatures should not be subject to the authentication framework outlined in The Electronic Signature Law. Instead, their unique handwritten characteristics should be analyzed from a morphological angle. Nevertheless, shifts in writing devices and mediums have diminished certain static characteristics, often resulting in imperfections in the identification process. This complexity is further compounded by difficulties in leveraging dynamic features for identification, challenges in distinguishing copied and pasted handwriting, and a scarcity of experienced authenticators. Consequently, during the identification process, it is crucial to: collect comparable original specimens, specify the writing devices used, gather samples from identical or similar sources as the specimens, and utilize identification expertise to identify stable traits with individual variations. By adopting a comprehensive quantitative analysis approach and corroborating with electronic data, the accuracy of identification conclusions can be significantly enhanced.

Hair matrix reference material is a class of reference material in which the target substance is combined with the biological matrix of hair and the concentration of the target substance is determined. Hair matrix reference material has a higher consistency with the real case samples involved in the field of forensic toxicology, reducing errors in calibration and validation by using this material during sample pre-treatment and instrument detection processes, thereby enhancing the accuracy and reproducibility of detection data, and its application has a positive effect on improving the testing ability of laboratories, advancing scientific and technological progress in the field of forensic toxicology, and enhancing judicial impartiality. This paper focuses on the hair matrix samples which are commonly analyzed in forensic toxicology, specifically addresses the structure of hair, the mechanism through which drug are incorporated into the hair, main preparation methods of hair matrix reference material, and provided a comprehensive evaluation of theoretical basis and development status of hair matrix reference materials for analysis in forensic toxicology. It is intended to serve as a reference for the development and application of hair matrix reference material.

Obtaining a single male DNA profile from aged mixed stains composed of sperm and vaginal epithelial cells, which are common challenging forensic biological materials, is difficult due to severe cell degradation and trace sperm content. This paper presents the testing process and successful experience of a sexual assault case, providing a promising solution for handling aged mixed stains with limited sperm content in practical scenarios. The sample was a vaginal swab from the victim, which yielded an unidentified single male DNA profile by DNA analysis in 2004. In the identification process in 2023, the PSA test on the sample was confirmed to be positive. Only the supernatant part yielded a single female DNA profile and an incomplete Y-STR profile using the conventional differential lysis method, and no DNA was detected in the precipitate part. In contrast, by utilizing commercial immunomagnetic bead kits and optimized experimental procedures, sperm cells were successfully isolated from the seminal and vaginal mixed-stain samples under enhanced incubation conditions, resulting in the acquisition of a single male DNA profile. At the end of this paper, the application value and precautions of the method for sperm cell separation based on immunomagnetic beads in the analysis of mixed stains are discussed.

As technology advances, mobile device forensics becomes increasingly challenging in the context of constantly updated operating systems and enhanced data encryption techniques. This paper, taking the TikTok application data extraction from a Huawei mobile phone in an actual case as an example, delves into the difficulty of extracting data from specific applications in new Android phones. To tackle this challenge, this study proposes a method utilizing a root-privileged phone to clone data from the source device and subsequently extract the required information, thereby achieving successful data retrieval. Furthermore, recognizing the inefficiencies and time-consuming nature of traditional manual timestamp conversion methods during targeted database analysis, this study has developed a novel database retrieval tool. This tool automates the process of swiftly retrieving and analyzing data from key time periods across multiple databases within a predefined directory, significantly enhancing processing speed and efficiency. Thus, our study not only offers a solution to the challenges of data extraction and analysis but also serves as a valuable methodological reference for mobile device forensics in similar cases.

The forensic science is undergoing a paradigm shift from the traditional paradigm to the features LR paradigm and the similarity scores LR paradigm, in an era of parallel three paradigms. Given the advantages and development opportunities brought by Bayesian LR paradigm, paradigm shift has become a major trend in forensic science in the global. However, paradigm shifts have not yet been fully realized on a global scale, and the development of the forensic paradigm is imbalance in various forensic discipline and in different countries and regions. The main reasons that hinder the paradigm shift of forensic science include limitation of the technology methods of new paradigm, misconceptions of some personnel about the new paradigms, insufficient abilities related the new paradigms, and legal application issues. Except for DNA evidence identification, the application of new paradigms and paradigm shift in China are relatively lagging behind. This article proposes an implementation path for the paradigm shift of forensic science in China, including scholars and practitioners conducting scientific research on new paradigms, regulatory authorities making paradigm shift strategies and plans, forensic lab developing and confirming LR methods, developing LR verbal scales, collecting data, training examiner and taking proficiency tests, decision-makers receiving relevant education, and legislators adjusting relevant regulations.

In the monitoring of the crime scene, recording can comprehensively record all kinds of sounds at the scene, including verbal conversations, abnormal sounds and other sounds that may be related to the case. The recording evidence can play a role in restoring the incident and revealing the facts of the crime. This article takes the audio recordings from the central scene of a shooting incident as the main analysis object. It introduces the method of forensic speaker recognition into the analysis of abnormal verbal cries and air gunshots related to the case. The audio-acoustic-phonetics analysis method is used to examine the cries and gunshots. A total of 142 shouts and 2-suspected shooting sounds appeared in the on-site recording of the case. Through inspection, analysis and confirmation, the shouts were all from the victim, and the two-suspected shooting sounds were air gun shots. Afterwards, combined with the on-site investigation and post-mortem examination results, the incident process was reconstructed and the sequence of events was reproduced, and the time of the incident and the license plate of the suspect’s vehicle were accurately inferred, which provided scientific basis and important clues for finding out the nature of the case, determining the direction of investigation and locking the suspect.

For the first time, a general ¹H-quantitative nuclear magnetic resonance spectrometry (¹H-qNMR) method for the simultaneous quantification of heroin hydrochloride, cocaine, cocaine hydrochloride, and 7 adulterants was established after the discussion of some key quantitative parameters. According to the solubility and stability of heroin hydrochloride, cocaine, and cocaine hydrochloride in different deuterated solvents, and the spectra comparison of seized samples and reference materials, dimethylsulfoxide-d6 was selected as the solvent, and 1,3,5-trimethoxybenzene was selected as the internal standard. The signals at δ_H 6.09 ppm for internal standard 1,3,5-trimethoxybenzene; at δ_H 6.87, 6.71, 2.24 ppm for heroin hydrochloride; at δ_H 7.90, 7.64, 5.12, 3.61 ppm for cocaine; and at δ_H 7.88, 5.49, 2.79 ppm for cocaine hydrochloride were selected as quantitative peaks．The established ¹H-qNMR method was applied for the quantification of 168 seized samples, the content of heroin and cocaine were basically consistent with the results of High-performance liquid chromatography. This method is simple, fast, accurate, does not require standard samples, and shows good tolerance and high reproducibility. It can provide new ideas for the quantification and profiling analysis of seized drug samples.

With the wide application of surveillance systems, there is an increasing concern about public safety and security issues. Among them, the rapid detection and recognition of fighting behavior is very important for maintaining social order and security. However, traditional monitoring systems often face many challenges when dealing with large-scale video streams, including high computational complexity and resource-limited environments. In order to cope with these challenges, this paper proposes an improved fighting behavior detection model based on YOLOv5s, which reduces the number of parameters of the model and the computational complexity, so that the model can operate more efficiently in the resource-limited environment and detect various fighting behaviors more accurately. First of all, the open source interactive markup tool Labelimg was used to annotate the data set and train the network model with a large amount of data. Secondly, considering the need for rapid and accurate solutions in public security practice, lightweight network MobileNetv3 is used as the backbone network by comparing various convolutional structures to replace the original backbone network of YOLOv5s model, so as to reduce the number of parameters and calculation amount of the model and improve the model detection accuracy. By setting ablation experiments, the improved model is compared with other models and the original model. The experimental results show that compared with the original network, the detection accuracy of the improved model is increased from 92% to 94.4%, the computational load is reduced from the original 15.8 G to 3.1 G, and the detection speed of the algorithm can reach 0.153 s at the fastest, meeting the real-time requirements. And the detection accuracy is the highest among the three models. This model is suitable for public security practical application scenarios with high precision and limited memory and computing power.

In order to further improve the accuracy of speaker recognition and avoid the complicated process of manual feature extraction required by traditional speaker recognition methods, this paper proposes an end-to-end speaker recognition method based on CBAM attention mechanism and deep neural network. CBAM, a lightweight general module, is introduced into the deep neural network structure and seamlessly integrated into the network architecture. After it is added into the first layer of deep neural network convolution in this paper, the features of speech signals first pass through the CBAM channel attention module to strengthen the model’s attention to the channel dimension of speech features. Then CBAM spatial attention module is used to improve the model’s attention to the spatial dimension of speech features, further improve the model’s sensitivity to important feature information, and use the end-to-end loss function to train the whole model as a whole. At the same time, an embedded court speaker recognition method based on generalized end-to-end loss function training is proposed, and the likelihood ratio is obtained by using the embedded cosine similarity score trained by the improved network model, so as to intuitively and accurately judge whether it is the same speaker, thus providing intuitive and powerful evidence for the court. Finally, taking deep neural network BILSTM and GRU as examples, the mainstream data set CN-Celeb was used to train the model to ensure that the model can achieve better combat effects in a complex and rich voice environment. Zhaishell, a subset of Zhvoice, and the audio of actual combat cases collected by ourselves were used for combat test, to ensure that the model in this paper has a good recognition effect for both Mandarin and dialect. The results show that the method proposed in this paper can effectively improve the recognition accuracy, quickly construct the model and improve the generalization ability.

The identification of the characteristics of shooting bullet trace image is the main content of gunshot trace inspection, and also one of the challenges. This article introduces an advanced automatic annotation method for shooting bullet trace features based on the High-resolution networks (HRNet) framework, which can achieve automatic labeling of the land-engraved trace area, groove-engraved trace area, and slippage trace area. A database of 5 985 images containing seven different sizes of shooting bullet traces extracted by BalScan (3D trace image scanning system) was constructed and divided into training, validation, and testing datasets at a ratio of 7:1.5:1.5. The training dataset was manually annotated to identify the land-engraved trace area, groove-engraved trace area, and slippage trace area, which were used to train the high-resolution network model. Then, the unlabeled testing dataset was input into the trained model for automatic annotation of the feature areas. Finally, the annotation results were manually reviewed and the accuracy was recorded. The results showed that the proposed method achieved an average accuracy of 94.1% in the automatic annotation task, demonstrating its effectiveness. This annotation algorithm for shooting bullet trace images without manual annotation can significantly reduce the workload of inspectors and provide a feasible new approach to improve the efficiency of firearm trace inspection.

With criminal methods becoming increasingly sophisticated, traditional crime investigation models are facing significant challenges. This paper proposes the construction of a comprehensive integrated collaborative workshop system for crime investigation technology based on multidimensional data coupling, aiming to consolidate diverse forensic techniques and transform them into effective tools for data mining, thereby enhancing the precision and efficiency of criminal investigations. The study provides an in-depth analysis of the multidimensional data coupling mechanism and its application value, while thoroughly examining the architectural framework, core components, and critical technical implementation pathways of the proposed system. Specifically, the coupling mechanism is designed to harmonize heterogeneous data sources, including forensic records, biometric databases, geospatial information, and real-time surveillance feeds, enabling dynamic cross-referencing and pattern recognition. Furthermore, the paper explores the system’s application prospects and identifies practical challenges such as data privacy concerns, interoperability barriers between legacy systems, and the need for specialized training programs to bridge the gap between technical outputs and investigative decision-making. Corresponding solutions, including blockchain-based encryption protocols, standardized data exchange interfaces, and adaptive human-machine collaboration frameworks, are proposed. Notably, the system’s ability to integrate advanced machine learning models with domain expertise allows for real-time scenario simulation and risk assessment, significantly reducing investigative blind spots. The establishment of this system is expected to dismantle data silos, facilitate the fusion of multi-source heterogeneous data, and foster synergistic collaboration between technological resources and expert judgment. By integrating artificial intelligence-driven analytics with domain-specific investigative expertise, the system supports a paradigm shift from “evidence-centric case processing” to “holistic case management,” driving innovation in forensic methodologies. This system is anticipated to revolutionize the way criminal investigations are conducted by providing a unified platform for data analysis and expert collaboration, ultimately leading to more accurate and timely case resolutions. This transformation not only accelerates case resolution through predictive policing models but also establishes a robust technical foundation for safeguarding national security and social stability in the era of digital criminology.

Fingerprints have long been regarded as the gold standard for individual identification due to their uniqueness and stability. However, the increasing complexity of crime scenes and the advancement of anti-forensic techniques have significantly raised the difficulty of extracting complete and clear fingerprints, posing challenges to traditional identification methods relying on minutiae features. Starting from the limitations of existing fingerprint features (Level 1, Level 2, and Level 3), this paper discusses their shortcomings in donor attribution, individual identification, and donor analysis, proposing ridge curvature features as a supplementary method. Subsequently, it reviews the mathematical definition and computational models (geometric methods, IPAN99 algorithm) of ridge curvature, as well as research progress in computational science and forensic science. The potential of ridge curvature as an individualizing feature is demonstrated from three aspects: feature specificity, stability, and discriminative capability. Future research directions are suggested, including optimization of curvature calculation algorithms, stability analysis of distorted fingerprint features, and fidelity preservation under different development conditions, to facilitate the transition of this feature from theoretical exploration to practical forensic applications.

Dried blood spot (DBS) is a sampling technique in which a small volume of blood is collected on a specific type of filter paper. Since the 1960s, DBS has been primarily used for the newborn screening of metabolic diseases. With the development of highly sensitive analytical instruments, the dried blood spot technique allows for accurate quantification of ethanol, stimulants, drugs of abuse and heavy metal elements in biological samples. Due to its obvious advantages, the application of DBS in forensic toxicology has witnessed significant growth in the past decade, which is highlighted and presented in this article. Some challenges and suggestions of dried blood spot applications were summarized for the further research as well.

Footprint features, as one of the biological features of the human body, play an important role in the field of personal identification. At present, most research on footprint recognition focuses on footprint images as experimental data, using deep learning algorithms as the foundation and relying on auxiliary algorithms to complete high-precision footprint recognition tasks. However, there is a problem with models built on footprint images. Due to the similarity of footprints of different people, as the number of samples increases, the differences between the features of footprints of different people will continue to decrease, leading to an increasing false detection rate of the model. In order to reduce the interference of similarity between footprints on model recognition ability, this paper takes dynamic footprints as the research object and proposes a dynamic footprint retrieval method based on multi-class feature fusion. The proposed method uses a spatio-temporal fusion module to integrate the spatio-temporal information of footprints, so that the footprint recognition method is not limited to the apparent information of footprints. Firstly, the convolutional neural network is used to extract the frame level features of dynamic footsteps, and then the feature fusion module calculates the complete apparent features of the fused dynamic footprints through a trainable weight matrix and frame level features. Secondly, the temporal aggregation branch of the spatio-temporal feature fusion module is used to extract long-term temporal features within frame level features, and then the long-term temporal features are fused with frame level features through orthogonal fusion calculation method to form spatio-temporal features. Finally, the visual features and spatio-temporal features are fused for dynamic footprint retrieval. A comparative experiment is conducted on a dynamic footprint dataset of 200 people with existing deep learning algorithms, and the experimental results shows that this method achieved better performance, with Rank1 and mAP being 85.39% and 55.28%, respectively.

Postmortem interval (PMI) refers to the interval between the discovery or examination of the body and the occurrence of death. Estimation of postmortem interval is one of the important research contents in forensic pathology，and it has always been the focus and hot spot of research work. Different techniques are used to evaluate and analyze the changes in the human body after death to estimate postmortem interval. The traditional methods of estimation of postmortem interval are based on postmortem phenomena such as algor mortis, rigor mortis, livor mortis, etc. These methods rely on the subjective experience of forensic pathologists, and the estimated time of death is a relatively wide range, and the estimated result is susceptible to subjective judgment. In recent years, postmortem computed tomography (PMCT) has become increasingly influential in the field of forensic pathology. Postmortem computed tomography is a non-invasive, rapid, and objective auxiliary means of autopsy, which can significantly improve the quality and efficiency of autopsy and can find imaging features that may not be observed in traditional autopsy. After the death of the human body, the body will undergo a series of postmortem changes according to a certain time law. A series of characteristic imaging changes in cadaver organs and tissues with the passage of time of death can be observed through the application of postmortem computed tomography, and these imaging changes can be quantitatively and objectively described, The potential ideal imaging indicators can be used to estimate postmortem interval and the correlation between these indicators and the postmortem interval can be analyzed. This paper systematically reviewed the research on the correlation between postmortem imaging features of organs and tissues (such as brain, heart, aorta, lung, liver, etc.) after postmortem computed tomography of various parts of the cadaver (head, chest, abdomen) and the estimation of postmortem interval, in order to provide new ideas for the study of the estimation of postmortem interval and further expand the application of virtual anatomy technology in the field of forensic pathology.

This paper aims to validate the multiplex amplification system of 9 CpG sites reported in the literature, and explore its applicability in the Chinese population. The SNaPshot multiplex amplification system was validated in terms of accuracy, analysis of the initial amount of converted DNA templates, and detection of mixed samples. A total of 236 samples of five types of body fluids including saliva, semen, blood, vaginal secretion, and menstrual blood were selected. The SNaPshot multiplex amplification system was used to detect the methylation values of 9 CpG sites. The detection threshold of CpG sites was that the methylation value is greater than 0.1. Analyze the starting amount of transformed DNA templates in this system after converting DNA using sodium bisulfite (template amount ranging from 0.5 ng to 10 ng). DNA extracted from four body fluids, including saliva, semen, blood, and vaginal secretion, were mixed in the following ratios: 1∶1, 1∶5, 1∶10, and 1∶20. Finally, the detection data set of 232 samples of the five types of body fluids was used to optimize the existing body fluid source determination method. The train set (n=162) was used to construct a random forest model, and the test set (n=70) was used to predict the body fluid type and evaluated the predictive performance of the model. Furthermore, an external data set (n=40) was added to validate the prediction model. In saliva, semen, blood, vaginal secretion samples, and menstrual blood, the body fluid type was determined directly based on the specific sites of body fluids, and the accuracy rates of body fluid identification were 100%, 98%, 98%, 94% respectively. Due to the influence of the menstrual cycle, some sites were missing, and the average accuracy of menstrual blood identification was 21%. This system could effectively detect the amount of transformed DNA from 1 ng to 10 ng. Among the mixed sample, both body fluid sources were correctly identified in all 1∶1 mixed samples. The main components could be detected in the other mixed samples (ratio 1∶5, 1∶10 and 1∶20), while there was a significant difference in the detection of secondary components. A random forest model was built from 232 samples, and the accuracy of identifying the five fluid sources in both the test and validation sets was 100%. The above results show that the multiplex amplification system has high accuracy for the identification of saliva, semen, blood and vaginal secretion, and is suitable for the identification of trace samples, mixed samples (ratio 1∶1) or main components of other ratios. Compared with direct interpretation based on body fluid specific peaks, the new random forest model can better identify menstrual blood. In summary, the multiplex amplification system for tissue identification of five types of forensic body fluids based on DNA methylation is potential for good forensic application.

The likelihood ratio paradigm of facial similarity score is the theory and method for interpreting the evidential significance of score finding from facial comparison. Facial similarity score likelihood ratio is the latest method of Bayesian likelihood ratio paradigm for forensic science. The likelihood ratio (LR) of facial similarity score is the ratio of the occurring probability of the facial score finding quantitatively assigned based on the probability distribution data of facial scores, under a pair of conflicting propositions that usually represent the claims of the prosecution and defense parties. The propositions typically deal with the question of whether a facial image with unknown identity collected at a crime scene comes from a suspect with known identity. The face score LR expresses the relative support direction and strength of the face score finding for the propositions of the prosecution and defense parties, providing quantitative evidence value for decision-makers to determine the disputed fact of the face source. The decision maker determines the fact of the facial source proposition based on the facial score LR opinion, or the posterior probability of the facial source proposition derived from the LR and the prior odds through Bayesian law, combined with other evidence, to exclude reasonable doubt. The likelihood ratio paradigm of facial similarity score is completely different from the traditional paradigm we are accustomed to in terms of scientific logic, opinion formation, expression, understanding, and reasoning applications. It also differs from the widely used LR paradigm of DNA feature findings, which poses new requirements and challenges for forensic examiner and decision-makers.

Detecting abnormal behavior is crucial for maintaining public security, especially in densely populated critical areas. Traditional target detection algorithms often struggle to deliver satisfactory results under these conditions due to challenges like dense target distribution, significant scale variation, and complex backgrounds. YOLOv8 is one of the better perforing detection models effect among the object detection models. This study introduces a novel approach to improve detection accuracy by integrating advanced mechanisms into the YOLOv8 backbone network. Firstly, the coordinate attention (CA) mechanism is incorporated into the C2f module of the backbone network. This enhances the network’s focus on targets amidst complex backgrounds by emphasizing relevant features and suppressing noise. Secondly, the swin transformer model is integrated into the YOLOv8 backbone. The swin transformer facilitates greater information interaction across the feature map, effectively utilizing the background information and improving object detection accuracy under complex scenarios. The datasets used in the experiments are described, the evaluation indexes of P, R, AP and mAP are listed, and ablation experiments and comparative experiments are carried out. Experiments demonstrate the feasibility and effectiveness of these improvements. The enhanced network is compared with several mainstream networks, showing a significant improvement in average accuracy, reaching 95.1%. Compared to the basic network YOLOv8, the average precision has been improved by 2.4%, which proves the effectiveness of this method. In summary, the innovative integration of the CA mechanism and Swin Transformer model into the YOLOv8 backbone network addresses key challenges in detecting abnormal behavior in densely populated and complex environments. These enhancements lead to improved detection accuracy, making it a promising approach for public security applications.

Currently, capillary electrophoresis-based Y-STR genotyping kits can detect up to 40 Y-STR loci simultaneously. However, these kits only report length-based genotypes and are unable to provide STR sequence information. The STRSeqTyperY68 kit, designed for forensic male pedigree differentiation using next-generation sequencing technology, excels at genotyping 67 Y-STR loci plus a sex-determinant locus in a single-tube reaction on the MiSeq FGx sequencing platform. It simultaneously provides both length and sequence polymorphism genetic information, simplifies testing procedures, enhances efficiency, and facilitates precise differentiation of male family lineages. The ITO method is commonly used to calculate the kinship index of two individuals’ biological relationship based on Mendel’s law of genetic segregation. Additionally, it evaluates consanguineous relationships within five degrees of kinship between two individuals. The combination of next-generation sequencing technology and the ITO method can effectively narrow down the range of potential families. This paper documents a rape and murder case that remained unsolved for eight years. By cooperatively utilizing the STRSeqTyperY68 kit and the ITO method, the potential connection between crime scene evidence and reference samples was evaluated. Gradually, the investigative leads were narrowed down, leading to the resolution of the case.

In this study, we report a three-step mutation at D8S1132 locus between an alleged father (AF) and child in a paternity case. Three autosomal STR multiplex amplification kits were used for capillary electrophoresis detection. At the D8S1132 locus, the genotypes were: alleged father 17/23, parent mother 22, and child 20/22, which does not comply with Mendelian inheritance laws. Paternity was confirmed by calculating the cumulative paternity index and a three-step mutation was identified at the D8S1132 locus. Next-generation sequencing was further used to validate the experimental results and explore the source of the mutation. The results indicated that the allele 20 of the child was derived from the allele 23 of the alleged father. In addition, next-generation sequencing platform that simultaneously detected different genetic markers such as STR, SNP, and mitochondria DNA increased the cumulative parental index, which further confirmed the paternal relationship. Therefore, this case suggests that in paternity testing, when multiple mutations occur at a certain STR locus, capillary electrophoresis and next-generation sequencing can be combined for cross-validation to improve the credibility of the identification results.

Within the realm of law enforcement, the utilization of WeChat data has emerged as an indispensable investigation tool, extensively employed in crime investigations and clues tracking. This paper focuses on the information shared by WeChat users in their Moments, with particular attention to interactions between friends. A method for extracting and analyzing clues based on the WeChat Moments relationship network is proposed. Firstly, social connections between users and friends are extracted by analyzing interactions such as likes and comments in WeChat Moments. The WeChat Moments relationship network is then constructed using force-directed graph techniques, providing a visual representation of the relationships between users and their friends. Subsequently, in-depth analysis is conducted through graph clustering and centrality analysis methods. By identifying closely connected individuals, potential associated groups and social circles are revealed, offering key leads for subsequent investigative work. Lastly, focusing on these closely connected individuals, a thorough analysis of their chat records is performed using word cloud technology and the TextRank algorithm. By mining keywords and topics, a more comprehensive understanding of communication content is obtained, aiding in the accurate assessment of the activities and intentions of individuals involved in the case. Through application and validation in real cases, this method demonstrates the ability to rapidly construct the WeChat Moments relationship network, identify closely connected individuals, and perform targeted analysis of their chat records. The results of the experiments show significant achievements in improving the efficiency, accuracy, and depth of lead acquisition, providing robust support for law enforcement investigations. The proposed method, based on the WeChat Moments relationship network, offers new perspectives and technological means for law enforcement investigations. Future work may involve further optimizing algorithms and enhancing the capability to handle large-scale data to adapt to the complex and dynamic nature of criminal environments, thereby providing more effective support for investigative efforts.

Fingerprint, one of the most reliable and valuable evidence in the crime scenes, has long been recognized as a powerful tool for personal identification and worldwide law-enforcing departments to fight against relevant crimes. For many years, in practice, fingerprint analysis has been developed based on the latent fingerprint visualization, primarily. However, most of these fingerprints in crime scenes have been ambiguous, deformed or fragmentary, which contributed to the difficulty in fingerprint analysis. So, recently, some researchers have gradually paid increasing attention on the fingerprint age for fingerprint analysis, though their researches were almost taken placed in the lab. To improve the accuracy and reliability of fingerprint age analysis, and effectively promoting the application of fingerprints age in practice, in this article, according to these researchers’ reports, the morphological characteristics of fingerprints related to the fingerprint age, including two-dimensional (2D) morphological characteristics (e.g. ridge widths and color contrast between ridges and furrows) and three-dimensional (3D) morphological characteristics (e.g. ridge heights), were reviewed, respectively. Furthermore, fingerprint residues which are transferred onto the object surfaces when fingertips touch object surfaces, reveal a wealthy of information, especially fingerprint age. Therefore, the changes of fingerprint residues involving electrical effect, the optical characteristics, as well as the change of compositions such as squareness, wax esters and fatty acids, were also comprehensively summarized in this review. Particularly, the future research directions and prospects were discussed about the methods and the reagents of latent fingerprint development, the equipment and the technologies of fingerprint detection, the composition and the degradation rates of fingerprint residues, and the influencing factors model of age determination of fingerprint.

As one of the most commonly used reagents for amino acid detection, ninhydrin has a wide range of applications in forensic science and is a classic and effective method for displaying old fingerprints on permeable objects. However, the traditional ninhydrin display method still needs further improvement in the display effect of latent fingerprints on objects with complex background colors. Based on a review of relevant research results at home and abroad, a brief review was conducted on the composition of fingerprint substances, the mechanism of ninhydrin in fingerprint development, and the improvement of traditional ninhydrin development methods. A detailed review was also provided on the methods for enhancing the development effect since ninhydrin was applied in the field of fingerprint development. The innovation of the traditional ninhydrin solution method for enhancing visualization mainly manifests in three aspects: 1) optimization of ninhydrin solution method reagent formula, such as screening of the best solvent, exploring the optimal concentration, and discussing the influence of pH value on visualization effect; 2) The innovation of ninhydrin display methods, such as solid medium method, ninhydrin vacuum fumigation method, spray display method, etc., mainly solves the problems of carbonization interference and background ink interference in thermosensitive paper; 3) The fingerprint enhancement treatment using ninhydrin, mainly includes metal salt enhancement, rare earth-Ruhmann’s Purple violet coordination compound enhancement, and trypsin enhancement. The metal salt enhancement method and the rare earth-Ruhmann’s Purple coordination compound enhancement method have great research potential in the future development trend of latent fingerprints.

From Sanger sequencing to high-throughput sequencing, the rapid development of sequencing technology has been providing better technical support for combating crime through forensic DNA analysis. In recent years, the third-generation sequencing technology, mainly based on nanopore sequencing technology, has been widely applied in life science research, in vitro diagnosis, public health, food safety and other fields. Nanopore sequencing technology with super-long reading and real-time sequencing has great potential in the field of forensic genetics. Many authorities and experts have already realized the great potential of nanopore sequencing applications for forensic purposes, although its application in forensic science is still in its infancy. There is little relevant research literature in the field of forensic science, and we should research and explore it further. In this article, the authors attempt to describe the basic principle and characteristics of nanopore sequencing technology, and share the updates of nanopore sequencing-based STR typing, MH typing, mtDNA, DNA methylation and RNA sequencing during the past several years. Meanwhile, non-human genetic material can provide medical examiners with special evidence and clues. The past decade has witnessed the enormous potential of nanopore sequencing technology in non-human forensic genetics. Especially in the areas of microorganisms, plant, and animal forensics, the application of nanopore sequencing to species identification can exert a huge implication, and provide the vital evidence and clues for the public security. In addition, nanopore sequencing has been used to detect viruses at the scene. In the field of forensic genetics, the nanopore sequencing with portability and real-time sequencing makes it most likely to sequence directly of biological samples at the crime scene. This development of the nanopore sequencing has opened up new possibilities by bringing “the laboratory into the field”. This draw the incomparable attraction to the practical application in public security. Moreover, several problems with nanopore sequencing in forensic genetics are discussed, including complex data analysis, high error rate, high sample quality requirements, and analytical methods, and there is a certain distance from the daily application of forensic genetics, which need in-depth research. Finally, we hope that this review can provide a reference for related research and applications, opening up ideas for relevant personnel.

The subjective likelihood ratio paradigm of pattern features is the theory and method for the interpretation of evidence significance of feature findings of pattern evidence. Subjective likelihood ratio (LR) of pattern features is the probability ratio of occurrence of pattern features assigned under the two opposing propositions representing the prosecution and defense respectively, based on expert knowledge or the combination of expert knowledge and data. The proposition hierarchy for evaluating the subjective LR of pattern features includes source level and active level. Subjective LR of pattern features expresses the relative support direction and intensity of the pattern features findings for the propositions of the prosecution and defense, providing qualitative evidence value for decision makers to determine disputed factual propositions. Decision makers will determine the propositions facts based on subjective LR opinions of pattern features, or based on the posterior probabilities of the propositions derived from LR through Bayes’ theorem, combined with other evidence in the case, in the way to exclude reasonable doubts. The subjective LR paradigm of pattern features differs significantly from the traditional paradigm we are accustomed to in terms of scientific logic and the formation, expression, understanding, and reasoning application of opinions, and it also differs from the objective LR paradigm of DNA feature. This presents new requirements and challenges for forensic examiner and decision-makers.

This paper explores the progress of artificial intelligence technology in the identification and reconstruction of crime scene elements. With the development of information technology, there are challenges faced by crime scene element identification and reconstruction. The paper discusses the application benefits of artificial intelligence, the relevant applications of artificial intelligence in forensic examination, and outlines the key steps of artificial intelligence in crime scene element identification and reconstruction, to explore the possibility of applying this method to crime scene element identification and reconstruction. Finally, the paper looks forward to the future development of artificial intelligence in forensic examination and suggests that it may play an important role in improving the intelligence level of crime scene examination and increasing the efficiency of case investigation. It is hoped that relevant research will provide a solution for the technical transformation of crime scene examiners and lay a foundation for the intelligent and digital development of forensic technology.

With the intensive integration of deep learning and computer vision, a series of advanced technologies such as facial recognition, image (video) generation, and image classification, have made rapid progress. However, deep learning models are considered “black box models” due to their difficulty in explaining internal processes and predicting results, which poses a serious challenge to the interpretability of image evidence in the field of forensic science. Based on this, this review outlines an overview of interpretability issues based on deep learning. Emphasis was placed on the theoretical and methodological research on the interpretability of facial features based on deep learning both domestically and internationally, such as saliency maps method, perturbation-based method, and score/statistics-based method. Their applications in facial recognition and other related fields, especially in the field of forensic science portraits, were summarized. This review proposes the problems of facial feature interpretability methods based on deep learning models, and looks forward to the future development direction of facial feature interpretability based on deep learning.

With the swift progress of artificial intelligence (AI), the field of forensic DNA examination is witnessing a technological transformation. AI has been integrated into multiple facets of forensic DNA analysis, encompassing intelligent DNA expert systems, AI-assisted optimization of examination procedures, innovative AI-assisted DNA statistics and analysis, rapid electrophoresis data analysis powered by AI, complex mixture sample analysis, and big data inference models. These advancements have significantly enhanced the precision and efficiency of forensic DNA testing. However, the integration of AI has also introduced challenges such as data privacy, model interpretability, algorithmic bias, and legal regulation. Addressing these issues necessitates close collaboration among forensic DNA experts, bioinformatics specialists, and AI professionals. Additionally, it requires the establishment of appropriate legal and regulatory frameworks to ensure that AI applications adhere to ethical standards and effectively support judicial fairness. This article provides an in-depth examination of the application of AI in forensic DNA analysis and the challenges it presents. It analyzes specific case studies to illustrate how AI contributes to the automation and intelligence of forensic DNA analysis, while also highlighting potential risks and challenges. The paper aims to offer guidance and references for the application of AI in the forensic DNA field.