Mycotoxins are secondary metabolites produced by phytopathogenic fungi under specific conditions. They are widely distributed in crops, and are chemically stable and highly toxic. In criminal cases, the detection of mycotoxins in biological samples can provide more accurate forensic evidence; therefore, the detection of mycotoxins in biological samples has gained great attention. In this study, common mycotoxins and their metabolites in biological samples are introduced, and pretreatment methods, such as liquid-liquid extraction, solid-phase extraction, and protein precipitation, are summarized. Detection methods, such as immunoassay, fluorescence assay, gas chromatography-mass spectrometry, and liquid chromatography-mass spectrometry, were reviewed, and the future of mycotoxins and metabolites detection in biological samples were prospected with the aim of providing information on the detection of mycotoxins in the field of forensic science.

To study the qualitative analysis of unknown quinazolinone in suspected tablets without reference substance, the new compound discovered from a dubious package called “Huyouyou” seized by the police was tested with methaqualone standard materials and etaqualone standard materials by gas chromatography-mass spectrometry (GC-MS). The retention time of unknown compound is closed to methaqualone and etaqualone, and ion fragment m/z 235 existed both in spectrogram of methaqualone and the unknown compound. There were not any satisfying matching search results in NIST20s and new psychoactive substance (NPS) spectral library Cayman Spectral Library, and the unknown compound was suspected to be a new quinazolinone. Ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) was further used to analyze the structures of methaqualone standard materials, etaqualone standard materials and the suspected new quinazolinone. The first-grade fragment and second-grade fragment information of methaqualone standard materials and etaqualone standard materials were consistent to potentially lytic pathway. Possible molecular formula of the suspected new quinazolinone by software Formula Predictor as C₁₆H₁₄N₂O₂. According to the second-grade fragment information, the suspected new quinazolinone had a quinazolinone nucleus structure and an attached phenyl ring, but the molecular weight was 2 daltones larger than the etaqualone, suggesting a methyl moiety is replaced by oxygen. Chemical structure of the suspected new quinazolinone was predicted as 2-methyl-3-(2-methoxyphenyl)- 4(3H)-quinazolinone. Nuclear magnetic resonance (NMR) was finally used to estimate the suspected molecular formula 2-methyl-3-(2-methoxyphenyl)-4(3H)-quinazolinone.¹H-NMR and ¹³C-NMR data supported the location of the oxygen, the locations of the carbons and hydrogens were also estimated simultaneously. The chemical structure of the suspected new quinazolinone was finally verified by NMR, and the new quinazolinone was suggested to be named as methoxyqualone.

In video investigation work, high-quality surveillance video portraits can provide a lot of effective information about criminal suspects, help investigators determine the direction of investigation, narrow the scope of investigation, and lock the criminal suspect. However, it is often encountered that the quality of video face images are too low to be recognized and facial image depth restoration methods such as GFP are only suitable for the restoration of a single facial image. To this end, this paper proposes a video facial image restoration technology based on the GFP, which extends the GFP method to the application of video investigation, facilitates the timely locking of criminal suspects in the video investigation work, and improves the efficiency of solving cases. Firstly, pre-processing operations such as video framing, facial image crop alignment, and oblique perspective correction are performed on the surveillance video. Then the GFP method is used to restore the depth of the pre-processed facial image. Finally, the restored face images through the reverse processing such as facial image registration, image restoration and video synthesis to generate high-quality facial image video. This paper verifies the effectiveness of portrait restoration technology based on GFP through simulation experiment, practical application and comparison experiment. Among them, simulation experiment and practical application were carried out on a large number of pre-processed real degraded face images and simulated degraded face images. The results show that the GFP is superior to other facial image depth restoration methods in terms of subjective visual effects and objective quantitative index such as FID, PSFR, SSIM, and NIQE. And by using YTF facial image video data set for comparison experiment, the experimental results show that the method of adding pre-processing and reverse processing is superior to the traditional GFP method. The video portrait restoration technology based on GFP with pre-processing and inverse processing proposed in this paper has better restoration effect for low-quality video portraits.

In the process of cracking down on new types of network-related crimes, the collection of data from suspects’ mobile phones is a key part in obtaining evidence, while in actual cases, the WeChat records in the mobile phone, which have vital clues and evidence, are usually deleted. How to quickly and efficiently restore the deleted WeChat records in the suspects’ mobile phones is an urgent problem to be solved in the investigation of related cases. This paper used the latest version of the four most commonly used forensics systems to collect WeChat data twice from more than thirty models of mainstream Huawei and Apple cell phones. The data was collected directly without any operation on the mobile phone, and then the data was collected again after the old version of the operating system was upgraded. In this way, we could compare and analyze the effect of different forensic systems to restore the deleted chat records of Huawei and Apple mobile phones. This paper can provide a reference for the selection of forensic tools in the investigation of related cases.

With the breakthrough of deep-learning technology in the field of artificial intelligence, deepfake portrait videos appear more and more frequently, such as facial tampering, pornographic video face swapping, changing politicians’ faces and making false statements, etc. This kind of deepfakes may pose a threat to societies; therefore, distinguishing deepfake videos from genuine ones has become an urgen issue. Lots of deepfake detection methods are carried out by constructing many data sets with different compression factors. At present, the deepfake detection technology based on deep-learning algorithm is popular, which requires lots of significant time consumption and massive computing power for training classification model. At the same time, the attributes of black box and unexplainability of deep learning networks also plague the researchers in forensic science. In order to solve the problem of authenticity forensics of the deepfake portrait videos, this paper takes portrait videos encoded by H.264/AVC as the research object, and proposes a method based on inter-frame quantization parameter intensity value to detect deepfake portrait videos and real portrait videos. The selection of inter-frame quantization parameter intensity value and the determination of the inter-frame quantization parameter intensity by binary Logistic regression equation are expounded in detail. The experimental results show favorable accuracy and robustness for the deepfake portrait videos synthesized by DeepFaceLab platform. The paper proposed an interpretable detection method for deepfake portrait videos, which is conducive to determine the direction of investigation and confirm the criminal facts. But there are some limitations. Firstly, the experimental samples are not rich enough. Secondly, the introduced method is greatly affected by video compression, which caused limited application scenarios. Thirdly, the analysis efficiency needs to be optimized.

For the analysis of the suspected drugs seized in a local case, and for the identification and characterization of the compound, this paper identified and characterized the target compound based on several techniques including gas chromatography-mass spectrometry (GC-MS), liquid chromatography-high resolution time-of-flight mass spectrometry (LC-QTOF), Fourier transform infrared spectroscopy (FT-IR), hydrogen nuclear magnetic resonance spectroscopy (¹H NMR), carbon nuclear magnetic resonance spectroscopy (¹³C NMR) and X-ray diffraction (XRD) of single crystal. The fragment information and neutral loss mass spectrum of ketamine and F-ketamine were determined and compared with the target compound. Based on the characterization results and neutral loss mass spectrum, the compound possesses bromine stable isotopes and a similar structure with ketamine and F-ketamine, which has been identified as Br-ketamine. Ketamine, one of the most widely abused drugs, and its derivative F-ketamine (2-FDCK) have been both forbidden by the Chinese government. To avoid the legal sanction, drug criminals usually modify the chemical structures of old drugs. It can be expected that Br-ketamine has some risks of drug abuse. However, there was no relevant research on characterization, psychoactivity, addiction and physiological toxicity. Therefore, this work provides identification and characterization of Br-ketamine, a new phencyclidine derivative. On one hand, this work provides significant references for the identification of Br-ketamine and further research. On another hand, the proposed identification method using neutral loss mass spectra can also be applied to other unknown compounds.

With the widespread application of printing equipment, such as office equipment, and the development of printing technology, the conventional inspection methods, which take substantive paper documents as the analysis object and take morphological characteristics and chemical properties as the analysis points, have been unable to meet all the needs of printing document identification. At present, the method of transforming printed documents into digital images by using image capture devices such as flat-panel scanners and digital cameras has become a hot spot in the field of document identification. In many cases, files are either actively or passively converted into images for storage, and the domestic inspection standards for paper carriers cannot be applied to the inspection of such files based on digital images. The ISO/IEC 24790 standard comprehensively covers the quality inspection methods of images printed by office imaging systems, which may enrich the inspection methods of digital printed documents in China and promote the construction of digital inspection standards. This paper interprets the international standard ISO/IEC 24790, identifies the reference modes for domestic standards, and elaborates the applicability of the evaluation methods for large area graphic image quality attributes, character and line image quality attributes introduced in international standards. However, the road of quantification is still in the early stage for China. At this stage, it is necessary to consider the correlation between attributes and document inspection and the measurement accuracy of the instrument when introducing the physical indicators in the standard into document identification. Undeniably, the introduction of the international standard ISO/IEC 24790 and the establishment of a comprehensive evaluation method that reflects the relationship between the perceptual characteristics of human visual system and the physical properties of images have provided new impetus for quantitative construction.

Some special components in blended gasoline may create difficulties for the identification of accelerants in arson cases. On the other hand, the special components and abnormal proportions could be used as classification basis. Research on the difference between blended gasolines and commercially available gasolines is helpful for the identification of accelerant, and can also provide reference for the investigation of gasoline sources in fire-related cases. In order to accurately identify gasoline components and distinguish blended gasoline from different sources, some typical blended samples were tested by gas chromatography mass spectrometry (GC-MS), and the chromatogram characteristics such as TIC, aromatics and alkanes were also analyzed. Experimental results show that most blended gasolines in daily cases can be divided into four categories: gasoline with unusual antiknock compound, gasoline with unusual isoparaffin, gasoline with unusual normal paraffin and gasoline with unusual multicomponent. The first two categories do not interfere with the identification of gasoline and its residue, while gasoline with unusual normal paraffin may cause misjudgment, and the one with unusual multicomponent should not be easily classified as gasoline. The results of this study have certain guiding significance for the examination and identification of blended gasoline and its residue, and have practical application value for the comparative analysis and tracing the source of gasolines.

To investigate the effects of ketamine abuse on cognitive function in patients with long-term dependency, PubMed, Cochrane Library, Web of Science English database, CNKI, and Wanfang Data Platform Chinese databases were searched online until January 2022. Two researchers independently extracted literature information and then used the Stata software (version 14.0) to calculate the standardized mean difference (SMD), 95% confidence interval (95%CI), and heterogeneity after data extraction. Eight studies met the inclusion criteria, with 285 patients in the observation group and 345 in the healthy control group. The results of the Meta-analysis showed that, compared with the healthy controls, ketamine abuse significantly impaired the cognitive processing speed (SMD=－1.16, 95%CI: －1.48 to －0.83, z=7.05, P<0.001), verbal memory (SMD=－0.72, 95%CI: －1.22 to －0.22, z=－2.803, P=0.005), visual memory (SMD=－0.70, 95%CI: －1.18 to －0.23, z=－2.890, P=0.004) in ketamine-dependent individuals. There were no significant differences in the working memory (SMD=－0.05, 95%CI:－0.66 to 0.55, z=0.18, P=0.859) and executive ability (SMD=0.19, 95%CI: －0.27 to 0.66, z=0.81, P=0.418) (P>0.05). Long-term ketamine abuse can significantly impair the cognitive processing speed, verbal memory, and visual memory of dependent patients. However, no significant effect was observed on the working memory and executive ability.

The occurrence of famous fingerprint identification errors, such as the McKie case in the UK and the Madrid train bombing case in Spain, has triggered an increase in the scientific requirements for fingerprint evidence, and the industry is in urgent need of basic theoretical research on close non-matches (CNMs). In addition, with the continuous expansion of the fingerprint database, the fingerprints screened by automatic fingerprint identification system with the highest scores are highly similar to the fingermarks, and the identification risks brought by CNMs are constantly amplified. The problem of CNMs has become one of the important research contents in the field of fingerprint inspection, which is of great significance to the construction of fingerprint identification standards. In this paper, firstly, the research background of CNMs is expounded with the Madrid train bombing case. Secondly, the concept of CNMs and a peer-reviewed standard are introduced. Then, the causes of CNMs (mainly monozygotic twins and automatic fingerprint identification system), the false positive error rate and the causes of identification errors are comprehensively and deeply analyzed and summarized. Finally, combined with the research status, some prospects for its future development trend are put forward, with a view to supplementing the basic theory of fingerprint identification and providing some reference for the quantitative evaluation of fingerprint evidence.

Vertebral fracture evaluation is a common task in forensic practice. Vertebral fracture diagnosis and discrimination based on imaging have been bottleneck problems that have plagued judicial appraisal for a long time. The massive group-petitions-and-stalking lawsuits caused by the large diagnosis deviation, low efficiency, repeat and multiple appraisals have seriously affected social harmony. This study aims to construct an artificial intelligence (AI) method for the rapid diagnosis of human vertebral fracture images to realize the automated assessment of vertebral fractures. A total of 1 151 cases of lumbar vertebral fractures were collected as research samples, divided into a training set of 800 cases, a validation set of 151 cases, and a testing set of 200 cases. Using the training set and validation set samples, the vertebral fracture evaluation model was constructed through six steps, including image preprocessing, spinal positioning model construction, vertebral body positioning and recognition model construction, vertebral body segmentation model construction, fracture diagnosis model construction and model evaluation. We applied the test set to test the model. The results show that the established AI evaluation model achieved an accuracy rate of 76% in identifying vertebral fractures. This model provides technical support for developing automated assessment tool for vertebral fractures.

Based on a large number of forensic and related medical literature and bioinformatics databases, the purpose of this study is to explore the expression differences of non-coding Piwi-interacting RNA (piRNA) molecules in common forensic body fluids, evaluate the body fluid differentiation ability of target molecules by statistical analysis, and establish a suitable model of identification method, so as to provide a method supplement for the identification of common body fluids (spots) in forensic genetics. In this study, we collected 120 human common body fluid samples in forensic genetics (including peripheral blood, menstrual blood, semen, saliva and vaginal secretion), and the expression of target piRNA molecules in five kinds of body fluids was detected by real-time fluorescent quantitative PCR method. To determine the applicable internal reference genes, the stability of the three internal parameters in five kinds of body fluids was evaluated by Genorm, NormFinder, BestKeeper software and ΔCt four methods. The difference of relative expression ΔCq of each marker in different body fluids was analyzed, and the marker molecules which can be used to distinguish body fluids were screened. At the same time, aiming at the difficult problem of distinguishing between saliva and vaginal secretion in forensic application, three piRNA molecules were identified and combined to establish a support vector machine (SVM) classification model to identify saliva and vaginal secretion. In addition, the stability of these small molecules was evaluated by detecting the relative expression of piRNAs in simulated case samples dried at room temperature for 1 month, 6 months, 12 months and ultraviolet irradiation for 12 hours and 48 hours, respectively. Finally, in this study, nine marker molecules were screened for body fluids identification, which were piR-33043, piR-33151, piR-57125, piR-31662, piR-31068, piR-31925, piR-43771, piR-43773, piR-55521, and the piR-33151, piR-31662 and piR-31925 three piRNA molecules combination was established as a SVM classification model to effectively distinguish saliva and vaginal secretion with an accuracy of 100%. These three piRNA molecules could still be detected in samples dried indoors for 12 months or UV irradiation for 48 hours. The results of this study show that there were significant differences in the relative expression of piRNA molecules in different body fluids. PiRNA molecules have the ability to identify common types of body fluids in forensic genetics, and the established classification model can effectively and accurately distinguish saliva and vaginal secretion, which has high application potential.

Based on gas chromatography mass spectrometry and multivariate analysis, non-targeted profiling of methamphetamine in China was established to deduce the synthetic routes. After optimizing analysis condition with gas chromatography mass spectrometry, following appropriate examination of all the peaks, 32 impurities were selected as the specific components in seized methamphetamine samples. Unsupervised (principal component analysis, PCA; hierarchical cluster analysis, HCA) and supervised (orthogonal projections to latent structures-DA, OPLS-DA) techniques were employed for classifying the 190 MA seizures. The results of PCA, HCA and OPLS-DA were in good agreement and showed a good tool for analyzing seizures correlation and synthesis routes. The established non-targeted screening technology has the advantage of simple operation, high reliability, not needing standard materials of impurities and can provide informative support to crack down the methamphetamine-related crime.

There are few morphological methods for fingerprint age analysis, and it is not certain that the mastoid line morphology of fingerprint is correlated with age. Through statistical and correlated analysis of quantifiable feature information, it is found that the number of wrinkles and fine line, the density of mastoid line, the width of mastoid line and small furrow, and other indicators are correlated with age, but the use of these indicators is not enough to analyze the age of the hand imprint remnant. The purpose of this paper is to study the performance characteristics of fingerprints in different age groups, especially the age-related characteristic information that is difficult to be quantified in fingermarks at the present stage, such as the fuzzy phenomenon of fingermarks, the symmetry degree of the edge of friction ridges, and the change rule of sweat holes with age. We explore the specific method of age analysis by fingerprints through observing and summarizing. The fingerprints were observed through the microscope, and the typical examples were visually displayed by fingerprint atlas. Fingerprints are extracted from a large number of samples and represent the characteristics of most samples of the corresponding age. In this paper, the characteristics and performance of different age groups are introduced through text, and the characteristics of fingerprinting performance in different age groups are displayed more intuitively and vividly with the fingerprint atlas. After observing a large number of fingerprint samples in different age groups in the fingerprint archives, the morphological characteristics of fingerprint in different age groups were summarized, and the specific steps and methods of age analysis are preliminarily formed. The specific process of such analysis was showed in combination with practical cases. The map of fingerprint contains the various typical and atypical manifestations of the fingermark samples at the corresponding age. Detailed steps are proposed for a morphological method of age analysis using handprints, which, like a code of practice, can be used by an experienced fingerprint analyst to make a preliminary estimate of the age of the person left behind. This paper provides a direction for researching fingerprint-based age analysis.

Whether the suspicious handwriting is written or not is the key in the handwritirg examination, which directly determines the conclusion. The traditional methods of forging handwriting, inclicding printing, copying and stamping, are easy to identify, because there are no stroke pressure. But the advances in robotic writing technology pose new challenges for forensic document examiners. Robotic writing can form three-dimensional stroke indentation, and it is easy to misjudge the handwriting formed by normal writing in the examination. Some researchers have modified the Cartesian writing robot to make its handwriting more natural, and more similar to the handwriting written by human, which is difficult to distinguish. Forensic document examiners should be alert to this kind of high-level forged handwriting. In this study, by utilizing the optical tomography and depth-of-field technology of AB 8000 handwriting quantitative tester, we measured the strokes of natural handwriting samples and high-level robotic writing samples, and obtained the width and depth of stroke, calculated the ratio i of the horizontal distance between the deepest mark and the edge of stroke and the width of stroke ink. We alsa measured the distribution degree of i value by chart and differential value operation, and analyzed the aggregation and fluctuation of the mark angles between high-level robotic writing samples and human handwriting samples. The results showed that the stroke angle of high-level robotic writing samples has high aggregation and low volatility, while the stroke angle of human writing samples has low aggregation and high volatility. Therefore, it is verified the validity of stroke angle feature in recognizing the robotic writing. It is also observed in the experiment that the stroke angle of long strokes is more stable, and the angle data of turning strokes and crossing strokes will be disturbed, so it is more accurate to choose long strokes for measurement.

The paper aims to study the occurrence of close non-matches (CNMs) in large-scale databases to enhance the risk awareness of fingerprint practitioners when they compare the suspect’s fingerprint against the ones from AFIS. Based on the database of more than 10 million people, the central areas of 40 arches were selected for research. Each fingerprint is marked with 40 minutiae to start sending queries. Reducing the number of feature points, in turn, to explore whether the amount can influence the AFIS occurrence rate of homologous fingerprints and CNMs, and make statistics on the similar areas of fingerprint and patterns of CNMs. Usually, as the number of minutiae increases, the occurrence rate of homologous features increases, and the occurrence rate of CNMs decreases. A total of 454 CNMs were found in 40 arches of volunteers’ fingerprints, one of CNMs was completely consistent with 15 feature paints. Average per query, the occurrence rate of CNMs in the top 100 candidates of each query is about 75.7%, of which the proportions of CNMs of the low, medium, and high levels are 46.5%, 19.7%, and 9.5%, respectively. In addition, CNMs appeared more in the arch pattern center’s 3, 4, and 6 regions, with an occurrence rate of 44.27%, 24.45%, and 18.5%, respectively, and CNMs were more from the triangular region of the dustpan pattern. This study contributes to improving the ability of fingerprint practitioners to identify CNMs, suggesting that fingerprint practitioners should pay more attention to the occurrence of CNMs in the middle-under parts of the central area of the arch.

This study is intended to provide a visual and quantifiable auxiliary technology for analysis of rib fracture injury mode. The rib fracture caused by boxing and chest compression is simulated by the finite element method: (1) The fist model is set to hit the left chest of THUMS4.02 (Total Human Model for Safety 4.02) at the speed of 6m/s. (2) The pressing hammer model is set to presses the precordial region of THUMS4.02 with a vertical downward force of 400 N. Distribution and morphology of rib fractures shown by the simulation results were compared, and the injury mechanism of the two was analyzed from the perspective of biomechanics. The simulation results are compared with those reported in the literature. The simulation results show that: (1) Shape and distribution characteristics of rib fractures: the shape of the broken ends of rib fractures caused by boxing is inward bending, and the fractures are distributed at the direct hitting positions of the ribs. The shape of the broken ends of rib fractures caused by chest compression is outward bending, and the fractures are distributed in the nondirect stress areas of the ribs. (2) Biomechanical response characteristics: when boxing the chest, the strain is concentrated in the position of the boxing ribs. Compressive stress is generated on the outer side of the rib at the striking position, and tensile stress is generated on the inner side. When pressing the chest, in addition to the strain distribution at the pressing position, there is another obvious strain distribution at the front line of the rib axillary on the pressing side of the chest. The lateral surface of the rib at the axillary front line generates tensile stress, and the medial surface causes compressive stress. The literature supports the simulation results, suggesting that the finite element method can be used to realize visual and quantifiable auxiliary analysis and interpretation of the injury mode of rib fracture.

For many speech technologies, it is required to have only one speaker in a complete statement, or it will degrade the performance of the algorithm. Therefore, speaker diarization becomes an important front-end for this system in the presence of multiple speakers. In order to improve the performance of the speaker diarization system, in this study, a d-vector based speaker binarization method is proposed, which is trained by a generalized end-to-end (GE2E) loss function that achieves better performance on speakers verification tasks. Firstly, the GE2E loss function is used to train a deep neural network (DNN) based on long short-term memory (LSTM) to extract the speaker-discriminative embeddings (d-vector), which is a neural network based on audio embeddings. Secondly, reference speech segments of each speaker are extracted from the input audio file and their embeddings are trained separately. While training continuous embeddings of the entire audio file, the cosine similarity of these continuous embeddings to the speakers’ embeddings is computed. Finally, the segments with a cosine similarity score greater than 0.75 are stored in the audio file of the corresponding speaker. This method adopts the principle of first recognition and then segmentation, and experimental results show that the algorithm has excellent performance in the scene where the number of speakers can be predicted and the short speech of single speakers can be easily obtained. Therefore, it can be used in multi-speaker automatic recognition system to automatically segment the target speaker’s voice to improve the performance.

In the investigation of telecom fraud dens, it is a key link to investigate the electronic equipment involved and extract electronic data, and the on-site evidence collection of memory data is an essential part of it. The scene of telecom fraud dens is usually complex and diverse, which commonly does not have the conditions to use large-scale forensics equipment. At the same time, there are numerous types and quantities of equipment involved, and the time for forensics is tight, which brings great technical difficulties and challenges to the scene forensics of memory data. Based on the relevant principles in “General Method for Collecting Electronic Evidence Data on the Spot”, this paper studied the current situation of memory data extraction in on-site evidence collection of telecom fraud dens. Firstly, according to the actual needs of the scene investigation of telecom fraud dens, the memory extraction methods of Windows, MAC and Linux systems in normal state were introduced. Then, for some troublesome problems, from the mechanism of operating system hibernation and unexpected system crash, the influence of hibernation files and dump files on memory extraction was analyzed, and the memory extraction method without knowing the password was proposed by using DMA dynamic memory reading technology. Finally, based on the characteristics of memory extraction in den investigation, a scheme of scene memory forensics was proposed and applied to practical cases, which provided reference for the exploration and evidence collection of such cases.

With the rapid development of science and technology and the Internet, digital images are playing a more indispensable role in our life. Digital images are increasingly inseparable from social media, forensics, journalism, scientific research, medicine, and all sorts of industries. However, due to the high editability of digital image, anyone can modify the content of digital image arbitrarily through the image editing program, which makes the authenticity and objectivity of the image become unknown. Therefore, how to identify the authenticity of the image has become a hot topic among experts and scholars. In the recognition of image authenticity, the tampering form of modifying the image content by copying and pasting the same image is very common in many cases of image tampering. Aiming at such problems, this paper attempts to identify the copy-pasted tampering image of the same image based on LBP algorithm. Firstly, the corner points in the tampered image were identified by Harris corner detection technology, and a rectangular window with corner points as the core was constructed. Then, the rectangular window features were obtained by local binary pattern (LBP) technology, and the feature vectors were determined. Then, the feature vectors were sorted lexicographically. The image tampered area was determined by the similarity of adjacent feature vectors, and the image tampered area was determined by the similarity of adjacent feature vectors. Finally, wrong mismatching points are removed by RANSAC operator. Experimental results demonstrate that the algorithm can effectively check the degree of image tampering, and has high robustness for images tampered with by complex behaviors such as rotation tampering, brightness tampering, fuzzy tampering and noise tampering. Although the copy-paste source of multi-region tampering cannot be determined, the multi-region tampering region can be identified. The method can verify the same image copy-paste tampering image effectively.

The fingerprint analysis of age is one of the important research directions in the field of forensic science, but there are some problems with various existing analysis methods, such as strong subjective experience, low feature utilization, small sample size and poor classification effect. To solve the above problems, an age classification model based on random forest algorithm was proposed. Firstly, the right-hand fingerprint samples of 1 600 males (aged 11 to 76) were collected to quantitatively process the feature data that might change with age. Then, the correlation analysis between each feature’s data and individual age was carried out to screen for features that were significantly correlated with age. Finally, based on random forest, ExtraTrees, CatBoost, KNN and BP neural network algorithms, four and six age classification models were established respectively. The applicability of the random forest algorithm was evaluated by model comparison, and the importance ranking of characteristic variables was output. The experimental results show that the random forest algorithm is the best for classification among the five classification models, which are as follows: in the four-classification model, the classification accuracy of random forest is 0.812, and the Kappa coefficient is 0.738; in the six-classification model, the classification accuracy of random forest is 0.738, and the Kappa coefficient is 0.675. Therefore, the fingerprint age classification model based on the random forest algorithm proposed in this paper can judge the age range of individuals more scientifically and accurately, so as to provide clues and ranges for investigation and technical inspection.

Paralytic shellfish poisoning is globally among the most commonly-seen poisoning incidents resulted from shellfish, posing huge threat to people’s life and health with its widest distribution, greatest harm and highest mortality (its oral-lethal dose less than 1mg for human). Paralytic shellfish poisoning is caused from the highly-toxic paralytic toxins of shellfish. The paralytic shellfish toxins (PSTs) are able to paralyze nerves and muscles and affect both the nerval and cardiovascular systems. PSTs have many kinds and structure-similar derivatives that can transform into one another, hence conducing difficulties to detect them. A review was here carried out into the PSTs about their properties, mechanism of action, toxic symptoms and metabolic pathways, having summarized the relating pretreatment methods of solid-phase extraction, dispersive solid-phase extraction and immunoaffinity chromatography, and successively discussed the applicable detection choices including the biologically-infected mouse approach, immunoassay, liquid-chromatography fluorescent detection and liquid chromatography-mass spectrometry. Therefore, valuable references should be capable of providing with this review for the concerned people to research PSTs, develop and improve relevant detection performance, examine correlative forensic-significant toxins and stipulate involving norms and standards.

Image retrieval algorithms aim at achieving individual re-identification through searching across a gallery of people’s images from different non-overlapping video monitoring cameras. However, night-shot infrared pedestrians’ images captured by surveillance systems have different data distributions from those of conventional colored RGB images, resulting in loss of all the color components which are crucial for pedestrian comparison. The differences between the two kinds of images are regarded as their modality discrepancy that can lead to large intra-class variations and modality gaps across different cameras. Thus, it is necessary and valuable to effectively bridge the modality gaps and alleviate the heterogeneous characteristics between pedestrian images shot nightly in infrared mode and those monitor-kept in the daytime so that the available retrieval algorithms could be improved of their effects under the widely-used visible-light-rendering image database. Accordingly, a dual-path partial-parameter-sharing framework was here designed with combination into a deep convolutional neural network (CNN) to extract the features from two-modality images possessing both the global coarse-granularity and local fine-granularity. The pre-trained ResNet50-based network was used to optimize the model training with the constraints of cross-modality entropy loss, center-based and sample-based triplet loss, eventually having brought forth an optimal model through adjustment of the weighted constraints into functions of the three losses indicated above. Subsequently, the feature measurement was carried out to validate the recognition effect on cross-modality images. The experimental results showed that the extracted multi-granularity features presented good application effect on the tested dataset under single shot mode, having SYSU-MM01 dataset demonstrated of effect verification and visualization that the resulting multi-granularity components did complement each other and hold a competitive testing accuracy compared to other several relevant algorithms. This research can serve video investigation work and help the on-going dynamic and static pedestrian image comparison. The algorithm proposed here would have broad applicable prospects in both the video investigation and future intelligent practice.

Recordings made by mobile phone are a common form of evidence in current litigation, but it is easy to separate recording files from their initial equipment, so the evidence review of originality and integrity of recordings is prone to cause disputes, which often need to be resolved through judicial expertise. However, the current achievements mostly focus on the integrity identification of mobile phone recordings and the equipment appraisal of IOS recording files. For the research on the traceability analysis of Android recording files, there is still a lack of research results. Because of this, we selected several Huawei and Xiaomi smartphones, simulated the typical operation behaviors such as recording, deleting, collecting, and transferring ordinary recordings and call recordings, and proposed a method for recording equipment identification & state analysis based on the operating system files, application logs and specific backup data through experiments. The experimental results show data recording methods and contents differences between Huawei and Xiaomi smartphones. However, under the recording process, the two types of mobile phones will write data records related to the recording files in specific operating system files. Therefore, we can identify the original recording equipment of specific mobile phone recording files and their changes by analysing system files, application logs, and even the particular backup data. This method is effective for recording devices’ identification of ordinary and call recordings made by Huawei and Xiaomi Android smartphones and their state analysis; Furthermore, it can also be used for equipment identification & state analysis for Harmony OS recordings and other multimedia files in Android smartphones. Of course, since many mobile phone manufacturers have made secondary development of the Android operating system, we must continue research equipment identification & state analysis of recordings made by other Android smartphone brands.

Glass is frequently encountered trace evidence in forensic investigations. Currently, the examination reports of glass can only present the physicochemical data or give comparison results qualitatively, lacking a quantitative evaluation index to draw conclusions. The likelihood ratio (LR) approach has been widely used in forensics to provide a measure of evidence value to evaluate the contribution of evidential physicochemical data all around the world. But in China, the quantitative evidence evaluation method has not yet been built yet for glass evidence, which poses judges and prosecutors difficulties to use glass evidence accurately in the trial. In order to provide a quantitative interpretation and evaluation of the evidential value of glass, an LR model was tried to be built and its performance was evaluated. A kernel density estimation using a Gaussian kernel was carried out to estimate the probability density function for 750 refractive index data of 150 different glass samples. An LR model was built based on the probability density function. Moreover, the histogram of log LR and false inclusion and false exclusion rates were used to evaluate the LR model using LR=1 as the threshold. The result showed the LR values of within-source pairwise comparisons were in the range of 6.58 to 204 500, and the between-source range is 0 to 0.68. Evaluation of the results revealed low misleading rates. Only one pair within-source comparison was found with LR<1 (expected to be>1). The false exclusion misleading rate was 0.67 %. There were 173 pairs of between-source comparisons among pairs were found with LR>1 (expected to be<1), leading to 1.55% false inclusion misleading rate. The likelihood ratio model built based on Gaussian kernel density estimation presents a satisfactory result for glass sample comparisons based on glass refractive index data, providing a practicable quantitative evaluation method for comparing glass samples.

To comprehensively understand the current situation and problems of the development of trace evidence specialty in criminal technology identification organizations in China, and put forward countermeasures and suggestions, a questionnaire was designed to investigate the construction of trace evidence laboratories in China. 307 provincial, municipal, district and county-level criminal technical identification organizations that carried out physical and chemical tests in 31 provinces, autonomous regions, and municipalities directly under the Central Government (excluding Hong Kong, Macao, and Taiwan) were investigated with the Questionnaire Star. The results of the questionnaire were analyzed with statistical methods. The 307 criminal technology identification organizations involved in the investigation included 31 provincial, 261 municipal, and 15 district/county-level, including 121 that passed the national laboratory accreditation (CNAS) and 186 that hadn’t passed the CNAS accreditation; 304 have passed the qualification certification, and 3 haven’t passed the qualification certification. All the criminal technical institutes involved in the investigation have set up physical and chemical testing laboratories, and the vast majority (301, accounting for 98.0%) have not set up a special trace evidence department. 224 organizations carried out the examination of trace evidence, accounting for 73.2%. There are 1 463 technicians in 307 laboratories, with an average of 4.77 in each laboratory. The professional and technical personnel are mainly chemical and related majors, accounting for 65.0%. GC-MS and GC are the most commonly used physical and chemical analysis instruments, with 409 and 3 457 sets respectively. The common analysis equipment for trace evidence specialty, such as scanning electron micro-scope/energy dispersive spectrometer (SEM-EDS, 63 sets) and infrared spectrometer (IR, 117 sets), account for a relatively small proportion. Accelerants and fire residues, explosives and post-blast residues, paints, gunshot residues and fibers are the most commonly encountered evidence. The training of professional and technical personnel is the most important factor restricting the development of the trace evidence specialty. Research has found that there are some problems in the development of the trace evidence specialty in China, including the weak awareness of the discovery, collection and application of trace evidence, the relatively backward laboratory equipment, the imperfect technical system of trace evidence examination, and the relatively lagging information system construction. The trace evidence laboratory has entered the stage of comprehensive construction in China. The development of trace evidence specialty in China can be promoted by clarifying the professional boundaries of trace evidence, building regional trace evidence laboratories, improving the technical method system of trace evidence, strengthening technical exchanges, training and promotion, and strengthening the training of trace evidence professionals.

Detecting and identifying the combustion promoter residue in the fire site sample is a difficult task. The fire combustion process may be accompanied by pyrolysis, weathering/evaporation or microbial degradation of the combustion agent, etc., making the collection of residues difficult, and the subsequent identification of the fire combustion agent material evidence also brings interference, so it is very important to determine the nature of the fire and find out the cause of the fire in a timely and accurate manner whether there are diesel and other combustion aids at the fire scene. In this paper, polyethylene (PE) plastic products-plastic wrap and polyethylene raw materials were used as research objects to explore the influence of them on the analysis and identification of diesel fuel, an ignition booster at the scene of arson. Solid phase microextraction-gas chromatography mass spectrometry (SPME-GC-MS) was used to compare and analyze the characteristic spectra of samples and diesel fuel in three different combustion states: bulk body, incomplete combustion, complete combustion to spontaneous combustion extinguishing. Through the comparative analysis of polyethylene raw materials and PE plastic wrap body, it is found that the main components contained in the two components are basically the same, and the individual components contained in PE plastic wrap are not detected in polyethylene raw materials, mainly due to the addition of some additives in the production process of PE plastic wrap. Incomplete combustion of plastic wrap in the three combustion states has the greatest effect on diesel. When m/z=85, 178, 192, 183, the two contain the same components and the distribution law is basically the same, but when the ion m/z=83 is extracted, the sample of incomplete combustion plastic wrap does not contain the same naphthene as diesel. The application of SPME-GC-MS technology can distinguish incomplete combustion plastic wrap from diesel, which is convenient and fast, has high sensitivity and fast analysis speed, and can be used as a preferred method for experimental preliminary screening, which is suitable for the analysis of combustion promoters in the fire field.