The main characteristic of forensic science is that, with the goal of continuously improving the efficiency and accuracy of case handling, it is transformed and re-innovated through the introduction of theories, technologies and useful practices from various fields. Along with expanding, the scope and depth of forensic science research, more scientific and accurate methods are provided for the admissibility of evidence and the confirmation of facts in cases, as well as building a more systematic and complete system of knowledge and technology. In order to standardize the evaluation and management of technological innovation in this field, improve the efficiency of innovation and the stability and reliability of technology, and promote technological exchanges and cooperation as well as the optimal allocation of resources, it is attempted to introduce the evaluation of technological maturity in this field and to explore the applicable determination standards. The systematic advancement of related research is inferred by analyzing the three continents and their mixed communities, and the guiding role of technological maturity in the development of criminal technological innovation is analyzed by example.

Electronic data forensics plays a crucial role in modern criminal investigations. However, with the continuous enhancement of smartphone security mechanisms, user privacy protection features－such as privacy spaces and system clones－have posed significant challenges to forensic work, particularly when privacy passwords are unavailable. To address this challenge, this study combines the UFED tool with manual analysis methods to conduct a comprehensive investigation into the data storage mechanisms of privacy spaces and system clones in four smartphone brands: OPPO, Redmi, Vivo, and Honor. The experimental results demonstrate that data in the privacy space and system clone of some devices are stored in an unencrypted format within the file system. When the file systems were analyzed using three domestic forensic tools, data from the privacy space and system clone of some devices were not successfully parsed, revealing the data protection mechanisms in smartphones from different brands, as well as the challenges faced by domestic forensic tools. This study provides new insights into improving domestic smartphone forensic methods and offers new approaches to address the current challenges in privacy protection data extraction, with significant practical application value.

To enhance the self-learning ability of models in acoustic scene classification tasks and reduce the cost of sample annotation, an improved semi-supervised model, ASC-FixMatch, is proposed in this study. The ASC-FixMatch model is capable of fully utilizing the limited labeled data and the abundant unlabeled data. Building upon the fundamental architecture of convolutional recurrent neural networks, the model has refined the consistency regularization strategy of FixMatch. It compels the learning of invariant features that remain stable after severe transformations by expanding the data perturbation space at the deep feature level, thereby enhancing the model's generalization ability to new data. During experiments, three different weighting strategies for the loss function of unlabeled data were adopted. The performance of the ASC-FixMatch model was compared with other classical semi-supervised models. The results indicated that, on the TAU Urban Acoustic Scenes 2019 development data set, the ASC-FixMatch model with the best loss weighting strategy achieves an accuracy improvement of 16.70%, 4.71%, and 6.65% compared to semi-supervised models based on pseudo-labeling, mean-teacher methods, and interpolation consistency training algorithms, respectively. The proposed model demonstrates a certain superiority in performance in the task of semi-supervised acoustic scene classification.

This study investigates a novel method for developing latent fingerprints by combining an ultrasonic atomization device with an aggregation-induced emission (AIE) material, TPA-1OH. Experiments were conducted to determine the operational parameters of the ultrasonic atomization device, optimize the formulation ratio of the developer TPA-1OH, and evaluate its potential impact on subsequent DNA analysis. The results demonstrate the feasibility of the proposed method for latent fingerprint development using the ultrasonic atomization device integrated with TPA-1OH. Following treatment with TPA-1OH, latent fingerprints exhibited significantly enhanced visualization under a 445 nm laser with a yellow filter, outperforming untreated controls in both clarity and contrast. Additionally, no significant DNA degradation was detected in the developer-treated specimens, indicating compatibility with downstream forensic DNA workflows.

The research progress of artificial intelligence (AI) in forensic pathology was systematically reviewed, which focuses on the key technical challenges frequently encountered in forensic practice, including histopathological diagnosis, cause of death analysis, postmortem interval estimation, injury mechanism analysis, and bloodstain pattern analysis. By sorting out the intersections of forensic pathology examination and AI, this study aims to promote the application of relevant research findings in forensic practice and empowers the formation of new-quality combat effectiveness in public security, as well as the improvement of the intelligence level of forensic work.

This study applies deep learning technology to the CT image reading and diagnosis of nasal area fractures in forensic clinical identification, with the aim of improving the accuracy of fracture localization and differentiation between fresh and old fractures, reducing the rates of missed diagnosis and misdiagnosis, and achieving intelligent recognition of nasal fractures. A total of 600 cases of confirmed fresh fractures, old fractures, and no fractures in the nasal area were collected. 9 294 frames of transverse and coronal CT images were captured and manually annotated for the overall bone structure and fracture site of the nasal area. A Faster R-CNN neural network, VGG16 backbone feature extractor, and CBAM module were used to establish an intelligent system model. The CT transverse images of the nasal area as a whole, the CT transverse images of the fracture site, and the CT coronal images of the fracture site were trained and tested separately. The results showed that the average recognition accuracy of the model in all three groups exceeded 90%, achieving accurate localization of fracture sites. This study confirms that the deep learning-based intelligent identification system model for nasal fractures can effectively assist forensic appraisers in CT image diagnosis of nasal fractures. It contributes to reduce the missed diagnosis and misdiagnosis of such injuries, and further improve the accuracy of forensic clinical practice.

With the rapid development of telecommunications technology, new forms of crimes such as illegal cyberattacks and telecom fraud keep emerging, which greatly raises the difficulty of investigations. The importance of cross-regional and cross-departmental collaborative case-solving has become increasingly prominent. To address the limitations of existing collaborative platforms in electronic evidence examination, including difficulties in data processing, restricted collaborative functionality, and insufficient automated analysis capabilities, this study innovatively designs and develops a new collaborative platform for case-related evidence materials. The platform employs a multi-language hybrid architecture integrating core modules such as electronic data transmission, BCP packet parsing, data cleaning, and batch extraction. Key innovations include modular design, one-click deployment in standalone environments, keyword extraction algorithms, and word cloud visualization. Application results verified that the platform significantly enhances case investigation efficiency and data analysis capabilities, providing real-time, mobile, and convenient case evidence disposal.

Forensic science serves as a crucial guarantee for safeguarding judicial justice and enhancing national crime governance capacity. The localization and quality development of its equipment directly affect the safety and efficiency of law enforcement operations in China. This paper analyzes practical equipment requirements within three key forensic domains: crime scene investigation and evidence collection, laboratory analysis and identification, and digital forensics and emerging crime governance. Currently, portable and intelligent equipment for crime scene investigation has advanced, supported by China's light industry foundation; laboratory analysis equipment is gradually closing the gap with international advanced levels; and digital forensics equipment for emerging crimes has rapidly developed driven by AI and internet technologies. However, challenges remain, including reliance on imported core technologies and an incomplete standardization system. Taking DNA equipment as an example, this paper proposes a high-quality development path: beginning with domestic reagents tailored to the genetic traits of the Chinese population, followed by progressive achievements in large-scale sequencer assembly, core component development, and independent analysis software creation. Simultaneously, a standardization system covering reference materials and methodologies should be established. Looking ahead, China's forensic equipment development should adhere to the principles of self-reliance, standardization, and practical applicability. Through deeper technology integration and AI-driven solutions, China can strengthen its advantages, enhance equipment localization, and address deficiencies. Collaborative efforts among police security institutions, enterprises, universities, and research institutions will promote the systematic adoption of domestic equipment. Furthermore, the Belt and Road Initiative can facilitate the export of equipment and spread of standards, ultimately enabling China to transition from following to leading in forensic science, thereby providing core technological support for judicial justice and modern crime governance.

This paper mainly introduces the research progress of deep learning technology in fingerprint information since 2018, including fingerprint image processing, fingerprint recognition, fake fingerprint detection technology, fingerprint dataset generation, and multimodal biometric recognition involving fingerprints. Fingerprint image processing includes fingerprint image segmentation, fingerprint image enhancement, and fingerprint image correction. Fingerprint recognition includes deep learning network-based fingerprint recognition, contactless fingerprint recognition, and fingerprint recognition based on three-level features of fingerprints. With the continuous deepening of research on deep learning technology, the study of fingerprint recognition algorithms using deep learning technology on large-scale fingerprint datasets will still be a research direction in the future. Moreover, using deep learning techniques to detect and identify fake fingerprints will be another direction for future research. The dataset of fake fingerprints needs to be dynamically updated and expanded in order to improve the ability of deep learning networks to detect and recognize fake fingerprints. In addition, one of the future research directions is how fingerprints, as an important biometric feature of the human body, can be integrated into multimodal biometric recognition based on large language models to achieve better identity recognition results.

Sudden cardiac death cases are common in forensic pathology practice. The diagnosis of death caused by early ischemic heart disease is difficult due to the lack of specific pathologic changes. Postmortem biochemical examination can provide objective evidence for the postmortem diagnosis of cause of death. It has been proved that postmortem biochemical analysis of markers of sudden cardiac death, such as the classical markers NT-proBNP, CK-MB, cTnT, cTnI, as well as the newer markers sLOX-1, H-FABP, and sST2 reported in recent studies, have auxiliary diagnostic significance and practical application potential in forensic identification of ischemic heart disease. Based on forensic medicine and clinical research, this paper summarizes the research reports on both classic and new markers, and discusses the establishment of a multi-marker diagnostic system to facilitate its application in forensic pathological practice.

Detection of Bitcoin illegal transactions is a significant challenge in blockchain technology, particularly when faced with complex transaction patterns and issues of class imbalance. This paper proposes a feature-enhanced graph neural network approach aimed at improving the accuracy of Bitcoin illegal transaction detection. First, the BERT model is employed to enhance transaction features, leveraging its powerful contextual modeling capabilities to extract more expressive features. Second, an innovative model architecture is designed, combining LSTM with a dual-channel ONGNNConv, where the latter passes its output to an attention mechanism for weighted aggregation, thereby enabling more effective capture of latent patterns in the transaction network. Finally, to address the class imbalance problem, a weighted binary cross-entropy loss is incorporated into the loss function to enhance the detection of illegal transactions. Experimental results demonstrate that the proposed method outperforms existing baseline models across multiple evaluation metrics, validating its effectiveness and robustness in Bitcoin illegal transaction detection.

This study investigates the detection capability of whole genome sequencing (WGS) technology for varying DNA input levels, aiming to establish a novel technical approach for SNP-based genealogical inference in forensic biological samples. On the SalusPro sequencing platform (China), WGS with a depth of 25× were performed on samples containing 0.5, 0.2, and 0.05 ng of DNA respectively. From these data, 645 199 autosomal SNP loci (Wegene GSA chip) were extracted and subjected to quality control. Genetic relationships were predicted by calculating the total length of identity by descent (IBD) fragments between individuals using IBD algorithms. The results demonstrated that when DNA input was ≥0.2 ng, the locus detection rate and genotype concordance rate exceeded 95% and 99%, respectively. The IBD fragment lengths showed no significant difference compared to the standard samples (P>0.05), with an average confidence interval accuracy of 91.40% for 1st- to 7th-degree relationships. In contrast, at 0.05 ng DNA input, the locus detection rate and genotype concordance rate dropped below 80% and 97%, respectively. The IBD fragment lengths and confidence interval accuracy were significantly lower than those of the standard samples (P=0.004), rendering this input level unsuitable for reliable genealogical inference.

Bayes’ theorem is regarded as the most basic theory in much of the literature that discusses forensic science comparison methods. This paper analyzes the problems faced by using this theorem in forensic science, especially the significance of different representations in the theorem and the logical reasoning problem, and argues that Bayes’ theorem cannot provide meaningful support for the conclusion of feature comparison, only as a calculation tool. If the forensic science comparison method is based on Bayes’ theorem, the theoretical basis is not solid. At a time when forensic science methods are facing change, it is even more necessary to get to the bottom of the problem in theory.

Over the last few decades, many new discoveries at the neurobiological, cellular, and molecular levels have helped researchers to further understand chronobiology. The issue of time inference in forensic medicine is always an important problem that needs to be solved, as it is closely related to the investigation of criminal cases. Current research indicates that it is possible to infer the time of cases within a day based on the rhythmic expression of biomarkers. Therefore, utilizing the circadian clocks could improve the accuracy of estimating the postmortem interval, wound age, and the deposition time of body fluid stains within a day. In this paper, the mechanisms of circadian clocks and their application in forensic medicine were reviewed, in order to provide new ideas for the research of time prediction in forensic medicine.

To evaluate the application potential of the one-step mRNA-PCR fluorescence multiplex amplification detection technology for identifying body fluid stains in case samples, a total of 89 samples suspected to be blood, saliva, semen, and vaginal secretions were collected. These samples were analyzed collaboratively with two forensic DNA laboratories lacking prior RNA operation experience. The laboratories performed total RNA extraction, one-step RT-PCR amplification, and capillary electrophoresis detection using different experimental instruments. The results demonstrated that three housekeeping genes (GAPDH, ACTB, and PRL19) were successfully detected in all 89 case samples. Additionally, specific markers for each body fluid were identified: HBB and HBA for peripheral blood; MMP7 and MMP10 for menstrual blood; STATH and HTN3 for saliva; PRM2 and SEMG1 for semen; and CYP2B7P1 and HBD1 for vaginal fluid. Notably, each participating DNA laboratory was able to independently complete the detection of RNA markers in the case samples, and parallel testing of the same samples across different laboratories yielded consistent results. This study provides robust evidence supporting the application of the one-step mRNA-PCR fluorescence multiplex amplification detection system for the source identification of body fluid stains in forensic case samples.

A total of 328 soil samples were collected from Guangling County and classified into habitat-based categories, including vegetable soil, wetland soil, park soil, roadside soil, and hilly soil. The study aimed to investigate the distribution characteristics of bacterial community diversity across different land-use types at the county scale, as well as the geographic traceability of soil of unknown origin. High-throughput sequencing was conducted using the Illumina MiSeq PE250 platform, and the microbiome 16S rRNA gene amplicon sequencing data were analyzed using the Qiime 2 pipeline. Additionally, physicochemical indicators of 14 soil samples were determined. The soil bacterial community structure profiling demonstrated that, the relative abundance of dominant bacterial groups varied across different habitats. Principal coordinate analysis (PCoA) results indicated that intra-group differences among soil sample sites within the same subgroups were small, while inter-group differences among soil type sample sites from different subgroups were significant. Under this categorization, pH, TN, WC, IC, TP, Al, Mn, and Pb were identified as important environmental factors driving changes in soil microbial community composition in the region. Finally, a random forest model was established, and its parameters were optimized. Using the collected samples as a training set, the model achieved the highest prediction accuracy when utilizing soil microbial data, with an accuracy of 83.58%.

Etomidate is a non-barbiturate intravenous anesthetic. After intravenous administration, it binds to plasma albumin and distributes into brain and heart tissues rapidly. Etomidate has the characteristics of rapid onset, short duration, and stable circulation. It is mainly used in clinical anesthesia induction or short-term surgical anesthesia. Due to its anesthetic properties, abusers make it into smoke powder, smoke oil, etc., and add them to e-cigarettes, which have widespread hazards and high concealment. Long-term use of etomidate will cause damage to the endocrine system, liver and nervous system. The toxicity to nerve cells is particularly prominent. In severe cases, apnea or even death may occur. At present, the abuse of etomidate has become a global issue, and a certain abuse trend also exists in China. Because etomidate is easy to obtain, teenagers account for the vast majority of users, which seriously endangers their physical and mental health. On September 6, 2023, the State Drug Administration, the Ministry of Public Security and the National Health Commission jointly issued the Announcement on the Adjustment of the List of Narcotic Drugs and Psychotropic Substances, which included etomidate (except for etomidate-containing drug preparations approved for marketing in China) on the list of category II psychotropic drugs, but the phenomenon of abuse is still frequent. At present, the research on etomidate mainly focuses on clinical medication, adverse reactions and detection methods. However, the research on toxicokinetics, toxicological mechanism and abuse or addiction mechanism is still insufficient. In this paper, the pharmacokinetics, anesthesia mechanism, toxicity, and abuse status of etomidate were retrospectively analyzed, and the detection methods such as gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography-mass spectrometry (HPLC-MS) and quantitative nuclear magnetic resonance spectroscopy (NMR) were summarized, in order to provide reference for the in-depth study and forensic identification of etomidate.

In forensic pathology, Sudan III staining is used to confirm fat embolism, and its quantitative grading is of significant importance in determining the cause of death. However, manual grading based on microscopic observation is highly dependent on personal experience. To objectively quantify the degree of fat embolism, we explored a method for automatic segmentation of fat droplets in Whole Slide Images (WSI) of lung tissue stained with Sudan III. Although the colors of the Sudan III stained sections are simply consisted of transparent tissue and scarlet fat droplets, issues such as residual dye, uneven staining of the fat droplets, irregular shapes, and significant size differences can lead to missegmentation and insufficient segmentation accuracy. To address this, we propose a contrastive language-image pre-training (CLIP) model framework combined with prompt learning for fat droplet segmentation: first, feature maps output by the CLIP image encoder are fused through skip connections, guiding the model to accurately segment fat droplets using CLIP’s prior knowledge via text prompts; then, a dice loss function is used to alleviate the imbalance between the foreground and background of the image; finally, validation is performed on the slice dataset and compared with U-Net, FCN8s, and Unet++ models. The results indicate the method proposed in this article is superior to others in segmenting fat droplets on stained slice images. Moreover, the proposed cross-modal prompt learning can be integrated into other large segmentation models to perform specific target segmentation tasks.

This study aims to explore the use of spectrophotometry and hyperspectral imaging technology combined with machine learning to effectively distinguish the types of stamp pad ink. Hyperspectral data and chromaticity values of 27 stamp pad ink samples from different brands and models were collected. Principal component analysis (PCA) was applied to the average chromaticity data for dimensionality reduction, and K-Means cluster analysis was used to successfully classify the ink samples into four categories. Subsequently, four classification models, namely LightGBM (light gradient boosting machine), XGBoost (extreme gradient boosting), SVM (support vector machine) and KNN (K-nearest neighbor) were used. The test set and training set were determined at a ratio of 1:4, and the samples of each category in the results of cluster analysis were identified one by one. The results showed that the SVM model, LightGBM model, and XGBoost model performed well. Specifically, SVM achieved 100% accuracy for sample classification in categories I, II, and III, and 98.3% for sample IV. This study provides a new method for quickly and accurately identifying the types of stamp pad ink.

The improvement of public security technology holds significant practical importance for combating illegal crimes and maintaining social stability. The progress of public security technology is inextricably linked to the support of scientific research projects. Based on the literature data from China National Knowledge Infrastructure (CNKI), this article employs quantitative analysis tools, such as knowledge graphs, to conduct a data-driven analysis of scientific and technological literature published between 2018 and 2023, which was funded by basic research business expense projects. The research primarily encompasses trend analysis of publication quantities, the distribution patterns of research topics, disciplines, journals, authors, and institutions. Three major aspects how basic research business expenses support the development of public security science and technology have been summarized: providing continuous and systematic support for basic research, effectively enhancing cooperation and exchange among institutions, and fostering interdisciplinary integration and innovative collaborations. Furthermore, the management mode of basic research business expenses is explored from five perspectives: adhering to demand orientation, enhancing management proficiency, prioritizing young talent, innovating organizational models, and incubating high-level projects.

With the strict regulation of etomidate, homologues of etomidate have emerged. Among them, the more common substances are metomidate, isopropoxate and propoxate. Etomidate and its homologues are very similar in structure, which requires extreme caution in analysis and detection. In this article, a qualitative and quantitative determination method for metomidate, isopropoxate, and propoxate in electronic cigarette oil, hair and urine was developed. Samples were extracted by organic solvent, gas chromatography-mass spectrometry (GC-MS) was used to verify the qualitative results of metomidate, isopropoxate and propoxate in e-liquid. Gas chromatography (GC) was used to verify the quantitative results of e-liquid. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to verify the qualitative and quantitative results of hair and urine. The results showed that the limit of detection(LOD) by GC-MS for metomidate, isopropoxate and propoxate in e-liquid was 10 μg/mL; the limit of quantification(LOQ) by GC was 20 μg/mL; and the linearity ranged 20 to 100 μg/mL (R²>0.998 8). For the 3 substances in hair, LOD was 0.005 ng/mg, LOQ was 0.05 ng/mg, and the linearity was 0.05 to 5 ng/mg (R²>0.994 5). In urine, LOD was 1 ng/mL, LOQ was 5 ng/mL, and the linearity was 5 to 100 ng/mL (R² >0.999 0). The intra-day and inter-day precision RSD and accuracy were both less than 15%; the recovery rate ranged from 94.7% to 114.3%. The method is accurate, reliable and applicable in the analysis of practical samples.

In motor vehicle-related cases, criminals often modify the VIN (Vehicle Identification Number) of the vehicle frame to make the involved vehicles regain legal status. However, currently, there is a lack of technical means that can quickly and accurately identify whether the VIN code area has been tampered. This paper studies the use of ultrasonic methods to detect VIN code tampering. By employing appropriate probes and coupling agents, combined with wavelet transform noise reduction technology, reliable determination thresholds were explored and a non-destructive testing method was proposed. This method can quickly and accurately detect whether the VIN area of the vehicle frame has been tampered with by “replacement” through hidden welds, and can also precisely identify the size and location of weld defects. This portable non-destructive testing technology provides strong support for quickly investigating suspected stolen vehicles and effectively improves the efficiency of combating criminals.

Facing the digitization and intellectualization of crime trends and the transformation of social governance models, forensic science is also in urgent need for transformation and upgrading. Introducing the trinity concept of investigation, prevention, and service into forensic science will facilitate its shift from “criminal investigation” to “social governance.” While strengthening investigative support for combating crime, this concept will enhance the capacity for crime prevention and public service, thereby contributing to the modernization of the national governance system and governance capabilities. This paper introduces the origin and theoretical foundation of the trinity concept of “investigation, prevention, and service”, explores its rationale in forensic science work, examines its practical applications in guiding forensic practices, and proposes actionable pathways for forensic science under this tripartite framework.

To address the over-reliance on qualitative expert judgment in identifying evidence from electrical fire, this study proposes a deep learning-based classification model for categorizing copper conductor melt marks. A total of 793 metallographic images of copper conductor melt marks collected from actual fire scenes were analyzed. Based on the specific conditions of the fire scenes and the structural characteristics of the melt marks, the images were classified by experts into four categories: fire melt marks, electric heat melt marks, primary short circuit melt marks, and secondary short circuit melt marks. Five convolutional neural network algorithms—VGG16, Inception v3, Xception, ResNet50, and EfficientNetV2S—along with two ensemble learning methods, soft voting and hard voting, were employed to train the classification model. Model performance was evaluated using five distinct metrics. The soft voting ensemble learning model, which integrates VGG16, ResNet50, and Xception, achieved the highest classification accuracy of 80.5%, outperforming the best individual model (71.1%). This study demonstrates the feasibility of applying deep learning to the intelligent classification of metallographic images of copper conductor melt marks. The proposed method provides probabilistic identification conclusions, which reduce dependence on expert judgment and promote the quantitative advancement of electrical fire evidence analysis.

Gamma-hydroxybutyrate (GHB) is a psychotropic drug listed in the United Nations 1971 Convention on Psychotropic Substances and China’s Regulations on the Administration of Narcotic Drugs and Psychotropic Substances. GHB has a strong inhibitory effect on the central nervous system, which can cause temporary memory loss, nausea, vomiting, hypersexuality, hallucination and even death. It is a more common chemical synthetic drug in entertainment venues. In recent years, GHB and its precursor γ-butyrolactone (GBL) are often used by criminals, leading to intentional injury, traffic accidents, rape and other malignant cases, causing a series of serious public health problems and social security problems. GHB is a normal endogenous substance in human body, which can be increased by exogenous intake. This means that timely sampling is needed to ensure the accuracy of the test results. If the concentration of the biological material cannot be detected in time, it cannot be proved that the victim or suspect had exogenous GHB ingestion. At the same time, the metabolism of GHB in human body is fast, the metabolites are complex, and the test window is short, which brings challenges to the police inspection and identification. In the face of the social reality that the cases of “anesthesia rape” and “anesthesia robbery” involving GHB continue to occur frequently, it is particularly important to study the testing and identification technology of GHB. This paper introduces the abuse and control of GHB at home and abroad, points out the difficulties in the detection and identification of GHB, and then summarizes the research progress in the detection and identification technology of GHB in blood, saliva, hair and urine. The endogenous levels of GHB in the biological specimens above and the critical concentrations of GHB after exogenous intake are enumberated, which can help forensic experts to exclude false positive results and assist in determining GHB abuse, in order to provide reference for the anti-drug work of public security in China.

In recent years, with the rising prevalence of telecommunication network fraud cases, the law enforcement agencies have significantly increased their efforts to combat the development and utilization of fraudulent technologies. Consequently, criminal groups have heightened their counter-surveillance capabilities. To evade law enforcement, many criminal groups chose to set up their own instant messaging (IM) services on servers and encrypt the transmitted data to prevent authorities from obtaining incriminating evidence, such as registration information and chat logs, through the third-party companies. This paper introduces a case study involving the decryption of chat data encrypted with the Blowfish+CBC mode algorithm on a MongoDB database, aiming to provide a reference for the investigation of similar telecommunication network fraud cases.

In recent years, the rapid development of artificial intelligence (AI) has brought unprecedented opportunities and challenges to scientific and technological research in various fields. The cross-integration of forensic toxicology and AI has increasingly become a research hotspot of forensic science, providing new ideas and methods to solve the problems of traditional forensic toxicology. In this paper, relevant researches in recent years have been reviewed, focusing on the research progress of AI in toxicological research such as on-site investigation of poisoning, toxicant screening and qualitative and quantitative detection, toxicity prediction, toxicokinetics, toxicants interaction, as well as the identification and characterization of involved personnel in cases, and the challenges faced by AI technology in the field of forensic toxicology have also been analyzed, in order to provide reference for AI technology to better serve the research and application of forensic toxicology.

Before the widespread use of digital photography technology, photographic negatives captured on photosensitive film served as crucial evidence in solving old and unresolved cases, providing important information for the successful resolution of cases. However, early black-and-white evidence negatives, taken during case proceedings, were prone to severe mold damage due to factors such as storage conditions, leading to blurred prints and significantly impeding the extraction of forensic details. This paper introduces a restoration method for severely mold-damaged negatives, which involves using a high-boiling-point liquid material with diisooctyl phenyl phosphite (DPOP) as its main component. Applying this material to the surface of the moldy negatives fills in the roughness caused by mold and eliminates the diffuse reflection of visible light caused by mold spots, effectively restoring the image quality on the damaged negatives. Meanwhile, the method is reversible. Once a clear and complete image is obtained, the applied restoration material can be removed, allowing the negative to revert to its original state and ensuring the integrity of the evidence. Employing this method, we successfully recovered the image on an evidence negative from a serious criminal case, fully reproducing the forensic information recorded on the film over 30 years ago and establishing a solid foundation for the case’s successful resolution.

Rapid DNA analysis technology represents an innovative approach by integrating multiple stages of the DNA analysis process, including DNA extraction, multiplex PCR amplification, and capillary electrophoresis. This analysis technology enables the automatic execution of all these processes, eliminating the need for manual intervention. Commercially available rapid DNA testing systems can simulate the laboratory environment through the internal environment offered by automated equipment. This advancement liberates the entire testing procedure from the stringent environmental conditions typically required in a DNA level three laboratory. In this article, the performance of the domestic fast integrated forensic DNA analyzer Quick TargSeq™ was verified under extreme environments. The tested instrument was loaded onto an SUV for both on-vehicle and on-the-move testing. The tests ranged from an altitude of 500 m on the plain to a peak altitude of 3800 m, which included 4 park-and-test points (with 1 reference point on the plain) and 5 on-the-move test trips. Throughout the test, there was a mix of rainy and snowy weather, with environmental temperatures dropping as low as -1 ̊C. A total of 36 results were collected, which included 2 from reference points at plain, 4 from on-the-move trip at plain, 18 from park-and-test points at highland and 12 from on-the-move trip at highland. Taking all the collected data into calculation, the loci detection rate of the test is 99.21% and the loci accuracy is 100%. The test results indicate that the Quick TargSeq™ DNA system is highly environmental adaptable and capable of operating effectively in both highland and on-vehicle settings.

Exosomes, which are extracellular vesicles with a double-layer membrane, play significant roles in biological processes. They act as tiny carriers, capable of transporting a wide range of genetic materials and metabolites, including DNA, RNA, proteins, lipids, etc. Hypoxia, a state of low oxygen availability, is a powerful environmental factor that triggers significant changes in exosomes. Their composition, secretion process and overall functionality will all be drastically affected under hypoxic conditions. In the realm of forensic pathology, the precise identification of death from mechanical asphyxia (DMA) has always been a major challenge. The corpses of individuals who have died from mechanical asphyxia often lack distinct and characteristic signs that can directly point to this particular cause of death. This absence of specific indicators makes it extremely difficult for forensic pathologists to achieve precise diagnosis, which brings trouble to the criminal investigation and court trial of DMA cases. In this paper, we conducted a comprehensive review of the hypoxia-induced changes of exosomes and discussed the benefits, limitations, and feasibility of application of these changes to precise identification of DMA. Through this in-depth discussion, we hope to offer new perspectives and directions for relevant studies, thereby contributing to the advance of forensic pathology.

Industrial explosives are usually strictly controlled, with many sales links and high prices, which bring great profits to the homemade explosives. In recent years, cases of illegal manufacturing, trading, transportation and storage of ammonium nitrate explosives have frequently occurred. The raw materials for homemade ammonium nitrate explosives are widely available and easy to obtain. These result in the complex compositions and formulations. Accurate qualitative and quantitative characterization of its composition is of great significance for comparison, tracing and case investigation. Powder X-ray diffraction (PXRD) has become an efficient method to identify the component. However, it is still in the qualitative stage, i.e. matching of the phase and lack of quantitative method to illustrate the content of each substance in mixtures. In this paper, a quantitative method based on PXRD is described, including pre-processing method, sample preparation, instrument parameter settings and full spectrum fitting method. Firstly, the crystalline phase and amorphous phase components contained in the sample are accurately judged. Subsequently, Rietveld refinement method is used in which multi-dimensional factors such as structural parameters, peak parameters, instrument parameters are all included. The differences between the measured pattern and the calculated pattern is the target function and the optimization algorithm is used to obtain model parameter. After refinement fitting, the best structural parameters and other parameters including content of each component are all identified. The method can realize the quantitative characterization of powder, granular, or block solids. It can provide an important basis for the comparison and tracing of such evidences, so as to further correlate the cases.

At present, the research on gasoline poisoning mostly focused on gasoline identification and detection. However, the autopsy and internal distribution were less studied. The purpose of this article is to provide reference data and pathological information for the determination of fatal poisoning caused by gasoline. Toxicological examination was performed on a deceased case of inhaling gasoline. The automated thermal desorber-gas chromatography mass spectrometry was used for qualitative and quantitative analysis of gasoline in the in vivo samples. The detection technology, the qualitative and quantitative evaluation methods of gasoline, the symptoms and mechanisms of gasoline poisoning and the internal distribution of fatal cases were summarized. The toxicology analysis results showed that, gasoline components were detected in the oral and nasal contents, lung, stomach contents, esophageal contents, pleural effusion and heart blood of the deceased. The concentrations in the esophageal contents and pleural effusion were 23.89 μg/mL and 26.7 μg/mL, respectively. Except for the oral and nasal contents and heart blood, the gasoline components in the in vivo samples showed a pattern of more light components and less heavy components, which was slightly different from control gasoline. As a low-toxicity narcotic poison, the mechanism of gasoline poisoning is mainly related to its physical properties such as high fat-solubility and high volatility. The symptoms of gasoline poisoning are non-specific. However, gasoline poisoning is not difficult to determine basing on occupational background investigation and odor identification of clothing and environment. According to the experience, the degree of poisoning is closely related to intake, so accurate quantification and the lethal dose of gasoline are key factors. Therefore, gasoline detection of the in vivo samples and literature review contributed to accumulate forensic toxicological data, and also provided reference for forensic toxicologists to deal with similar cases.

Determining the sequence of intersecting lines between laser printing and stamped impression is one of the key contents of questioned document examination. Fluorescence microscope is considered to be one of the most effective ways to examine the sequence of intersecting seal and toner lines of questioned documents. However, due to various factors, determining the sequence of crossing lines has always been a challenge to forensic document examiners. Considering the influence of types of factors such as toner morphology, stamped impression fluorescence, and paper, a systematic analysis of laser printing handwriting and print timing issues was conducted using the ZMSX-05 vermilion ink timing instrument to excite fluorescence. The fluorescence phenomenon on the surface of toner was observed under two different timing conditions (“printing before stamping” and “stamping before printing”). Experiments have shown that the morphology of ink powder plays an important role in determining the sequence of intersecting lines. When the toner powder is compact and piled up on the surface of the paper and the seal ink has strong fluorescence, the sequence of the intersecting lines can be determined quickly and accurately; however, when the toner powder is non-compact and penetrates into the paper fibers, the seal ink has weak fluorescence, making it relatively difficult to determine the sequence of the intersecting lines. However, by comparing the transmitted light and fluorescence test images, it is possible to accurately determine the sequence between laser printing and stamp impression under most conditions, especially for compact and non-compact toner examination, for which 100% accuracy rate and 90% detection rate were achieved in the blind test, but some examiners made errors in determining the sequence under the interference of paper fibres, which indicates that the conclusions drawn by examiners with different professional abilities are different. This also reminds us that in actual identification, examiners should understand the principle of the fluorescence method, distinguish the effect of each element on the sequence of intersecting lines, and scientifically apply examination methods in order to make correct identification opinions. In addition, time should be another important element in determining the sequence of intersecting lines, but the time interval between laser printing and stamping, as well as the changes that occur over time after the formation of the two time sequences, require long-term detection. For example, how the fluorescence of the printed text on the surface of the toner powder changes over time, etc. This should be an important line of research for the future, so that experimental data can provide reference and clarification for document examiners.

Routine external examination of dead body can provide information to estimate the postmortem interval (PMI) roughly by observing postmortem phenomena in situ such as corneal opacity. With the development of computer vision, these traditional direct observation methods have developed with more objective, accurate, standardized, and convenient approaches. By reviewing the relevant literature published in recent years, this article summarized the application and improvement of PMI estimation based on corneal images, from aspects of observing or detecting indicators, equipment, evolving data processing methods, models, etc., in order to show application and research progress of computer vision technology applied in the field of forensic medicine. It introduced the main animal model research and human cadaver research, focused on several parts such as research materials, methods and results, and provided some comparative analysis. Then it extracted out several key points including the influence of eyelid opening or closing, the effect of light source, the interference of diseases and injuries, the selection of regions of interest in images, and the understanding of data processing methods, etc. Lastly, several advice were put forward to make a better understanding of the future development trend which may offer reference and ideas to colleagues.

Gun-related cases pose extreme societal harm and urgently demand efficient and precise detection methods. This study aims to integrate the reflectance transformation imaging (RTI) technique with deep learning to apply it to the field of gun and bullet recognition. In the experiment, a total of 1 500 samples of fired cartridge cases were selected from five QSZ92 9 mm pistols. Detailed images of the markings on the base of the cartridge cases were captured using the DTV3.1 intelligent imaging system to obtain their normal maps. Thereafter, the pre-trained ResNet-50 network extracted features from the normal maps and underwent classification training. The model’s performance was evaluated by outputting AUC values, accuracy on the test set, and a confusion matrix. The experimental results reveal a total AUC value of 0.98 across the five guns, with gun No. 2 achieving the highest accuracy of 97.66% and gun No. 1 the lowest at 93.75%. This study demonstrates that the automatic recognition method of cartridge case marks based on RTI technology and deep learning yields significant results, offering valuable reference for the identification of other traces in the field of trace inspection.

With the improvement of living standards and an increasing awareness of personal health, the demand for health foods has surged. Consequently, the market has expanded continuously, presenting numerous regulatory challenges for the relevant authorities. The illegal addition of drugs in health foods poses a significant threat to consumer health and impedes the sustainable development of the health foods industry. Therefore, it is imperative to establish comprehensive methods for detecting these unlawful substances in health foods and to develop robust regulatory systems. Among these, health foods that claim to relieve physical fatigue are particularly vulnerable to such illegal adulterations, which primarily fall into two categories. The first is often marketed with alleged aphrodisiac or sexual enhancement benefits, while the second claims to improve cognitive function. Many unscrupulous vendors illegally add phosphodiesterase 5 inhibitors (PDE5i) or synthetic nootropics to enhance these claimed benefits. This paper provides a summary of the types, hazards, and prevalence of illicit drug additives in health foods designed for aphrodisiac or cognitive improvement, and gives an overview of the latest advancements in detection methodologies, including high-performance liquid chromatography (HPLC), liquid chromatography-mass spectrometry (LC-MS), spectroscopy, immunoassays, and electrochemical analysis. The primary methods for detecting illegal drug additives in aphrodisiac health foods include HPLC, LC-MS, spectroscopy, mass spectrometry (MS), immunoassay, and electrochemical analysis. For brain-tonifying health foods, the main detection methods are HPLC and LC-MS. Finally, this article explores the future of detecting illicit drug additives in health foods from the perspectives of standardizing preprocessing methods, expanding the application of portable instruments, employing various techniques for non-targeted screening, and utilizing artificial intelligence and machine learning technologies. The intent of this paper is to offer valuable insights and reference for law enforcement and regulatory agencies in the pursuit of monitoring and ensuring the safety of these health foods.

The anonymity and decentralization of Bitcoin make it a significant medium for illicit transactions, posing challenges for traditional detection methods in handling complex transaction network structures. This study proposes a graph neural network model based on a pre-trained Conditional Variational Autoencoder (CVAE) to enhance the efficiency and accuracy of Bitcoin illicit transaction detection. The model generates K−1 feature vectors through the CVAE, which have the same number as the input features, and then combines these generated K−1 feature vectors with the original feature vector to ultimately form K feature vectors. Each feature vector undergoes multi-channel aggregation and max pooling, resulting in multiple feature vectors. These vectors are subsequently processed through linear layers and layer normalization, followed by another round of max pooling to obtain a global feature vector. Finally, the feature vectors are further processed through graph convolutional layers and linear layers to generate the final classification result. The model integrates input feature vectors at the output layer through a skip mechanism. Experimental results demonstrate that this model performs excellently in Bitcoin illicit transaction detection, significantly improving detection accuracy and robustness.

In recent years, Android system applications (hereinafter referred to as ‘APPs’) have become one of the primary ‘tools’ used by criminals for fraud. Criminals develop fraudulent apps and distribute their installation packages, known as Android application packages or APK files, to victims. After downloading and installing these apps, victims are deceived through their interactions within the apps. Therefore, the functional analysis of apps on Android devices has become a crucial source of for analyzing the processes of fraudulent activities and identifying the perpetrators of such crimes. With the development of protective technologies in recent years, an increasing number of fraudulent application files now employ various protective measures to prevent virtual machine executing and packet capturing, making dynamic analysis of these APPs increasingly difficult. This paper introduces common anti-packet capture techniques, including APK environment detection, packet capture detection, and certificate verification detection, and starts with reverse code analysis of APKs, dynamic packet capture analysis, and the underlying system code of Android, which explores the feasibility of bypassing dynamic detection and anti-packet capture mechanisms. The study of these methods for evidence collection provides valuable insights for the analysis of various types of fraudulent and malicious APPs.

This study established an analytical method suitable for the rapid on-site detection of trace methamphetamine (MET) in sewage by integrating magnetic solid phase extraction (MSPE) with immunochromatographic technology. Using magnetic nanospheres conjugated with monoclonal antibodies against methamphetamine (MET) as an immunomagnetic probe, solid-phase extraction was employed to enrich and extract methamphetamine drugs in sewage. Subsequently, the extracted methamphetamine was then quantified using colloidal gold immunochromatographic test strips. The quantitative analysis of methamphetamine was carried out by optimizing various parameters such as the amount of labeled antibody, the pH of desorption solution, the desorption time, the water sample volume, and the water sample pH. The experimental results showed that the regression equation of the standard curve of the prepared test strip was 0.999 6, and the detection limit was 0.13 ng/mL.The combination of immunomagnetic probes and colloidal gold immunochromatography enabled the detection of trace methamphetamine drugs in sewage samples within 30 minutes. The method utilized a sewage sample volume of 50 mL, achieving a concentration factor of 50 times, with a quantitative detection limit of 8 ng/L.This combined method is simple to operate, time-efficient, and has low dependence on specialized equipment. Additionally, it does not require the use of organic solvents. It is suitable for the rapid on-site detection of trace amounts of methamphetamine drugs in sewage. Additionally, it can serve as an auxiliary tool for laboratory detection of sewage samples and be applied to the monitoring of urban sewage toxicity, thus playing an important role in relevant fields.

With the full advancement of the low-altitude economic strategy, low-altitude flight safety has increasingly become a top priority for national stability and security. Drone crimes are rapidly evolving from potential risks to real threats, with both the number of cases and the level of harm rising exponentially. In response to the critical challenges of drone governance, such as “difficulties with low-altitude surveillance, target identification, and timely disposal”, this article proposes to build a four-dimensional linkage technical investigation and prevention pathway of “global intelligent sensing network - three-dimensional forensic examination system - precise source tracing technology - coordinated rapid disposal”. This system, for the first time, proposes the focus of investigations on drone-related incidents, provides technical support for the high-quality development of drones, and provides systematic solutions to global non-traditional security governance.