An Adaptive Hate Speech Detection Approach Using Neutrosophic Neural Networks for Social Media Forensics

Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due tothe complex nature of language used in such platforms. Currently, several methods exist for classifying hatespeech, but they still suffer from ambiguity when differentiating between hateful and offensive content and theyalso lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm(WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two Multi-Layer Perceptron (MLPs)for neutrosophic sets classification. During the training process of the MLP, the WOA is employed to exploreand determine the optimal set of weights. The PSO algorithm adjusts the weights to optimize the performanceof the MLP as fine-tuning. Additionally, in this approach, two separate MLP models are employed. One MLPis dedicated to predicting degrees of truth membership, while the other MLP focuses on predicting degrees offalse membership. The difference between these memberships quantifies uncertainty, indicating the degree ofindeterminacy in predictions. The experimental results indicate the superior performance of our model comparedto previous work when evaluated on the Davidson dataset.

Evaluation of a Novel 32 X-STR Loci Multiplex System in the Forensic Application

The AGCU X Plus STR system is a newly developed multiplex PCR kit that detects 32 X-chromosomal STR loci simultaneously.These are DXS6807,DXS9895,linkage group 1(DXS10148,DXS10135,DXS8378),DXS9902,DXS6795,DXS6810,DXS10159,DXS10162,DXS10164,DXS7132,linkage group 2(DXS10079,DXS10074,DXS10075),DXS981,DXS6800,DXS6803,DXS6809,DXS6789,DXS7424,DXS101,DXS7133,GATA172D05,GATA165B12,linkage group 3(DXS10103,HPRTB,DXS10101),GATA31E08 and linkage group 4(DXS8377,DXS10134,DXS7423).A major advantage of this kit is that it takes into account linkage between loci,in addition to detecting more X-STR loci.In order to evaluate the forensic application of 32 X-STR fl uorescence amplifi cation system,PCR settings,sensitivity,species specifi city,stability,DNA mixtures,concordance,stutter,sizing precision,and population genetics investigation were evaluated according to the Scientific Working Group on DNA Analysis Methods(SWGDAM)developmental validation guidelines.The study showed that the genotyping results of each locus were signifi cantly accurate when the DNA template was at least 62.5 pg.Complete profi les were obtained for the 1∶1 and 1∶3 combinations.A total of 209 unrelated individuals from Southern Chinese Han community,consisting of 84 females and 125 males,were selected for population studies,and 285 allele profi les were detected from 32 X-STR loci.The polymorphism information content(PIC)ranged from 0.2721 in DXS6800,to 0.9105 in DXS10135,with an average of 0.6798.DXS10135(PIC=0.9105)was the most polymorphic locus,with discrimination power(DP)of 0.9164 and 0.9871 for the male and female.The cumulative PD_(F),PD_(M),MEC_(trio) and MEC_(duo) valu es were all greater than 0.999999999.There were 78 different DXS10103-HPRTB-DXS10101 haplotypes among the 125 males,and the haplotype diversity was 0.9810.There was no signifi cant difference in the cumulative PD_(F),PD_(M),MEC_(trio) and MEC_(duo) values whether considering linkage or not.In summary,the new X-STR multiplex typing system is effective and reliable,which can be useful in human genetic analysis and kinship testing as a potent complement to autosomal STR typing.

Fusion of Type-2 Neutrosophic Similarity Measure in Signatures Verification Systems: A New Forensic Document Analysis Paradigm

Signature verification involves vague situations in which a signature could resemble many reference samples ormight differ because of handwriting variances. By presenting the features and similarity score of signatures from thematching algorithm as fuzzy sets and capturing the degrees of membership, non-membership, and indeterminacy,a neutrosophic engine can significantly contribute to signature verification by addressing the inherent uncertaintiesand ambiguities present in signatures. But type-1 neutrosophic logic gives these membership functions fixed values,which could not adequately capture the various degrees of uncertainty in the characteristics of signatures. Type-1neutrosophic representation is also unable to adjust to various degrees of uncertainty. The proposed work exploresthe type-2 neutrosophic logic to enable additional flexibility and granularity in handling ambiguity, indeterminacy,and uncertainty, hence improving the accuracy of signature verification systems. Because type-2 neutrosophiclogic allows the assessment of many sources of ambiguity and conflicting information, decision-making is moreflexible. These experimental results show the possible benefits of using a type-2 neutrosophic engine for signatureverification by demonstrating its superior handling of uncertainty and variability over type-1, which eventuallyresults in more accurate False Rejection Rate (FRR) and False Acceptance Rate (FAR) verification results. In acomparison analysis using a benchmark dataset of handwritten signatures, the type-2 neutrosophic similaritymeasure yields a better accuracy rate of 98% than the type-1 95%.

Research on Electronic Forensics Based on Blockchain Technology

The development of high technology,for public life to provide a justification at the same time,also encouraged the spirit of cybercrime,to become more and more rampant.In network crime,electronic data is usually used as the main evidence to determine the facts of the crime and plays an important role in the smooth trial of the case.But because electronic data on dependent,concealment,easy destructive strong science and technology,the forensics work is now in trouble.The mature use of blockchain technology can avoid existing problems to a certain extent,which is helpful to the smooth progress of electronic forensics.This paper on electronic evidence how to more effectively,combined with research blockchain technology,improve the efficiency of electronic evidence collection work.

Truth Behind Speech:Forensic Linguistic Analysis on Speech

We are living in a society constructed by many aspects as well as languages.There are many ways to deal with legal cases,language is also an active one among them.As it is proved that resultant of forensic linguistic researches do help around.

中文语境下“Forensic Science”界定与诠释

作为美国司法制度的一项有机组成部分,"Forensic Science"已与其诉讼法律文化紧密融为一体。美国诉讼活动中,双方当事人对于某项专门性问题进行举证时,往往需要运用被称为"Forensic Science"的科学实证活动予以证明。在将"Forensic Science"一词引入国内的同时,必须在中文语境基础上对其进行准确界定与诠释。通过中美诉讼法律文化的比较研究,并对我国现有相关专业术语进行辨析后可以发现,"司法鉴定"一词与"Forensic Science"不仅在内涵、外延上较为吻合,亦符合我国诉讼制度与司法实践的现实要求。同时,采用"司法鉴定"一词,还可以充分反映出"Forensic Science"的本质属性,避免因中文界定的偏差而造成不必要的争议与误解。

Characteristics of Pattern Visual Evoked Potential in Two Eyes with Varying Visual Acuity in One Eye and Forensic Application

In this study, we aimed to study the pattern visual evoked potentials （P-VEPs） in two eyes with varying visual acuity in one eye and to provide an objective estimation of visual acuity by comparing P-VEPs in one and two eyes. Thirty subjects were chosen, who had one eye with an acuity of 5.0, 4.85, 4.6, 4.0, or scieropia and obstructed vision and the other eye with an acuity of 5.0, respectively. P-VEPs were detected under the large grating stimuli at 3x4 spatial frequency, moderate grating stimuli （12× 16 spatial frequency） and small grating stimuli （48×64 spatial frequency）. Under large grating stimuli, there was no significant difference in P100 peak latency between the groups, nor was there a significant difference between the amplitude of two eyes and the amplitude of one normal-vision eye. Under moderate and small grating stimuli, there was a significant difference in P100 peak latency between the group with both eyes having an acuity of 5.0 and the group with visual acuity below 4.0 in one eye. There was a significant difference in P100 amplitude between the group with visual acuity of 5.0 in both eyes and the group with one normal-vision eye. There was no significant difference in the amplitude of two eyes and the amplitude of one normal-vision eye between any other two groups. In forensic identification, characteristics and variability of P-VEPs in one and two eyes can be used to identify malingering or decline in visual acuity.

PHOTOREALISTIC COMPUTER GRAPHICS FORENSICS BASED ON LEADING DIGIT LAW

As the advent and growing popularity of image rendering software,photorealistic computer graphics are becoming more and more perceptually indistinguishable from photographic images.If the faked images are abused,it may lead to potential social,legal or private consequences.To this end,it is very necessary and also challenging to find effective methods to differentiate between them.In this paper,a novel leading digit law,also called Benford's law,based method to identify computer graphics is proposed.More specifically,statistics of the most significant digits are extracted from image's Discrete Cosine Transform(DCT) coefficients and magnitudes of image's gradient,and then the Support Vector Machine(SVM) based classifiers are built.Results of experiments on the image datasets indicate that the proposed method is comparable to prior works.Besides,it possesses low dimensional features and low computational complexity.

Forensic Seismology and Boundary Element Method Application vis-à-vis ROKS Cheonan Underwater Explosion

On March 26,2010 an underwater explosion(UWE)led to the sinking of the ROKS Cheonan.The official Multinational Civilian-Military Joint Investigation Group(MCMJIG)report concluded that the cause of the underwater explosion was a 250 kg net explosive weight(NEW)detonation at a depth of 6 9 m from a DPRK"CHT-02D"torpedo.Kim and Gitterman(2012a)determined the NEW and seismic magnitude as 136 kg at a depth of approximately 8m and 2.04,respectively using basic hydrodynamics based on theoretical and experimental methods as well as spectral analysis and seismic methods.The purpose of this study was to clarify the cause of the UWE via more detailed methods using bubble dynamics and simulation of propellers as well as forensic seismology.Regarding the observed bubble pulse period of 0.990 s,0.976 s and 1.030 s were found in case of a 136NEW at a detonation depth of 8 m using the boundary element method(BEM)and 3D bubble shape simulations derived for a 136kg NEW detonation at a depth of 8 m approximately 5 m portside from the hull centerline.Here we show through analytical equations,models and 3D bubble shape simulations that the most probable cause of this underwater explosion was a 136 kg NEW detonation at a depth of 8m attributable to a ROK littoral"land control"mine(LCM).

Analysis of Turnaround Time during Casefile and Sample Processing in Forensic Science Laboratory

Turnaround time (TAT), is the total time interval from when a request for forensic laboratory analysis is received until when the results are collected by the client. The performance of the forensic science laboratory (FSL) is affected by extended TAT in the case-file and sample processing steps necessitating critical analysis reported in this paper. The total TAT was obtained as the sum of measured time interval for each work station (six of which were studied). Extended TAT leads not only to customer complaints, but also paves way for customers to seek for services from competitors, leading to lost competitive edge for the FSL. This study was conducted to establish the baseline data on TAT (between 2014 and 2015) to enable implementation of corrective actions. Six casefile processing steps were identified for which starting and completion times were recorded in dates, giving TAT values in days. The TAT data for each step was collected as each case file is processed and analyzed separately using statistical analysis while comparing the data for the two years (Y2014 and Y2015) and?among?three forensic science laboratory disciplines (biology/DNA, chemistry and toxicology). The overall turnaround time (TTAT) was?the?highest for forensic biology/DNA compared to forensic toxicology and chemistry. The analysis time (TAT2) was the longest of all six case-file processing steps. Using Pareto analysis, the three major steps necessitating root-cause analysis and intervention to minimize TAT were analysis turnaround time (TAT2), report collection time (TAT6) and report review time (TAT4). It was concluded that the causes for extended TAT are within control by the FSL management, although financial and human resources are required.

Digital Forensics and Cyber Crime Datamining

Digital forensics is the science of identifying, extracting, analyzing and presenting the digital evidence that has been stored in the digital devices. Various digital tools and techniques are being used to achieve this. Our paper explains forensic analysis steps in the storage media, hidden data analysis in the file system, network forensic methods and cyber crime data mining. This paper proposes a new tool which is the combination of digital forensic investigation and crime data mining. The proposed system is designed for finding motive, pattern of cyber attacks and counts of attacks types happened during a period. Hence the proposed tool enables the system administrators to minimize the system vulnerability.

A Survey of Blind Forensics Techniques for JPEG Image Tampering

Blind forensics of JPEG image tampering as a kind of digital image blind forensics technology is gradually becoming a new research hotspot in the field of image security. Firstly, the main achievements of domestic and foreign scholars in the blind forensic technology of JPEG image tampering were briefly described. Then, according to the different methods of tampering and detection, the current detection was divided into two types: double JPEG compression detection and block effect inconsistency detection. This paper summarized the existing methods of JPEG image blind forensics detection, and analyzed the two methods. Finally, the existing problems and future research trends were analyzed and prospected to provide further theoretical support for the research of JPEG image blind forensics technology.

Cloud Foren:A Novel Framework for Digital Forensics in Cloud Computing

Since its birth in the early 90 's,digital forensics has been mainly focused on collecting and examining digital evidence from computers and networks that are controlled and owned by individuals or organizations.As cloud computing has recently emerged as a dominant platform for running applications and storing data,digital forensics faces well-known challenges in the cloud,such as data inaccessibility,data and service volatility,and law enforcement lacks control over the cloud.To date,very little research has been done to develop efficient theory and practice for digital forensics in the cloud.In this paper,we present a novel framework,Cloud Foren,which systematically addresses the challenges of forensics in cloud computing.Cloud Foren covers the entire process of digital forensics,from the initial point of complaint to the final point where the evidence is confirmed.The key components of Cloud Foren address some challenges,which are unique to the cloud.The proposed forensic process allows cloud forensic examiner,cloud provider,and cloud customer collaborate naturally.We use two case studies to demonstrate the applicability of Cloud Foren.We believe Cloud Foren holds great promise for more precise and automatic digital forensics in a cloud computing environment.

A Novel Forensic Computing Model

According to the requirement of computer forensic and network forensic, a novel forensic computing model is presented, which exploits XML/OEM/RM data model, Data fusion technology, forensic knowledgebase, inference mechanism of expert system and evidence mining engine. This model takes advantage of flexility and openness, so it can be widely used in mining evidence.

Primary Exploration of Reliability Evaluation of Computer Live Forensics Model on Physical Memory Analysis

The integrity and fidelity of digital evidence are very important in live forensics. Previous studies have focused the uncertainty of live forensics based on different memory snapshots. However,this kind of method is not effective in practice. In fact,memory images are usually acquired by using forensics tools instead of using snapshots. Therefore,the integrity and fidelity of live evidence should be evaluated during the acquisition process. In this paper,we study the problem in a novel viewpoint. Firstly,several definitions about memory acquisition measure error are introduced to describe the trusty. Then,we analyze the experimental error and propose some suggestions on how to reduce it. A novel method is also developed to calculate the system error in detail. The results of a case study on Windows 7 and VMware virtual machine show that the experimental error has good accuracy and precision,which demonstrate the efficacy of the proposed reducing methods. The system error is also evaluated,that is,it accounts for the whole error from 30% to 50%.

Low-Cost CSI Using Forensic GPR, 3D Reconstruction, and GIS

At a time in which several law enforcement agencies all around the world are facing severe expenditure reviews, the increasing use of low-cost solutions during crime scene investigations (CSI) can be a sustainable solution without being detracting from a scientific and rigorous forensic approach. Recently, some private companies are trying to satisfy the growing needs of the police to have cheaper and faster techniques to survey crime scenes and to obtain results with scientifically high-level outputs, more or less comparable with similar outputs from more expensive instruments. This paper reported results from a crime scene in which a low-cost GPR instrument, a free 3D photogrammetry software, and an open-source geographical information system (GIS) were deployed. The collection, processing, and analyses of these data will provide a very promising starting point for future low-cost and professional applications.

Optimization of Evidence Analysis Cost Using Arbitrary Re-Sampling Techniques for Sample Influx into Forensic Science Laboratory

This study analyzes the sample influx (samples per case file) into forensic science laboratory (FSL) and the corresponding analysis costs and uses arbitrary re-sampling plans to establish the minimum cost function. The demand for forensic analysis increased for all disciplines, especially biology/DNA between 2014 and 2015. While the average distribution of case files was about 42.5%, 40.6% and 17% for the three disciplines, the distribution of samples was rather different being 12%, 82.5% and 5.5% for samples requiring forensic biology, chemistry and toxicology analysis, respectively. Results show that most of the analysis workload was on forensic chemistry analysis. The cost of analysis for case files and the corresponding sample influx varied in the ratio of 35:6:1 and 28:12:1 for forensic chemistry, biology/DNA and toxicology for year 2014 for 2015, respectively. In the two consecutive years, the cost for forensic chemistry analysis was comparatively very high, necessitating re-sampling. The time series of sample influx in all disciplines are strongly stochastic, with higher magnitude for chemistry, biology/DNA and toxicology, in this order. The PDFs of sample influx data are highly skewed to the right, especially forensic toxicology and biology/DNA with peaks at 1 and 3 samples per case file. The arbitrary re-sampling plans were best suited to forensic chemistry case files (where re-sampling conditions apply). The locus of arbitrary number of samples to take from the submitted forensic samples was used to establish the minimum and scientifically acceptable samples by applying minimization function developed in this paper. The cost minimization function was also developed based on the average cost per sample and choice of re-sampling plans depending on the range of sample influx, from which the savings were determined and maximized. Thus, the study gives a forensic scientist a business model and scientific decision making tool on minimum number of samples to analyze focusing on savings on analysis cost.

Analysis of Stress Backlogs during Case-File Processing in Forensic Science Laboratory

Case-file backlogs were identified as one of the cause factors affecting the competitiveness of the forensic science laboratory (FSL). Backlogs represent case-files?that remain unprocessed or unreported within a selected time interval (year, week or month) which leads to increased customer complaints, rework, cost of analysis, degradation of biological samples, etc. Case-file backlogging was quantified in three consecutive years (2014 to 2016), using the following parameters: case-files received and case-files processed, difference of which gives case-files backlogged. There was a need to define time interval for a case-file to be regarded as backlogged (that is, one week), results of which can translate into backlogged case-files per month or year. A data collection tool was established and used for three work stations (forensic chemistry, biology/DNA and toxicology laboratories). The tool includes starting and ending date for each?time interval, in which the numbers of case-files received and processed were entered followed by computing the backlogs. It was observed that, case-files reported?increased between 2014 and 2016 leading to a decrease in backlogged case-files.?The annual percentage of the case-files backlogged was highest for forensic?toxicology. The highest number of case-files backlogged was observed for forensic?chemistry, followed by forensic biology/DNA. The number of case-files?backlogged per analyst per year was highest in 2014 and dropped continuously?towards 2016, being comparably higher in forensic biology/DNA and chemistry.?Probability density functions (PDFs) and cumulative distribution functions (CDFs)?of backlogs data indicated that a large number of backlogs created in previous?weeks were eliminated. It was concluded that the effect of case-file backlogging on FSL competitiveness can be minimized by continued management effort in backlog elimination.

LC-MS/MS analysis of 2-aminothiazoline-4-carboxylic acid as a forensic biomarker for cyanide poisoning

AIM: To demonstrate the potential of using 2-aminothiazoline-4-carboxylic acid(ATCA) as a novel biomarker/forensic biomarker for cyanide poisoning. METHODS: A sensitive method was developed and employed for the identification and quantification of ATCA in biological samples, where the sample extraction and clean up were achieved by solid phase extraction(SPE). After optimization of SPE procedures, ATCA was analyzed by high performance liquid chromatographytandem mass spectrometry. ATCA levels following the administration of different doses of potassium cyanide(KCN) to mice were measured and compared to endogenous ATCA levels in order to study the significance of using ATCA as a biomarker for cyanide poisoning.RESULTS: A custom made analytical method was established for a new(mice) model when animals were exposed to increasing KCN doses. The application of this method provided important new information on ATCA as a potential cyanide biomarker. ATCA concentration in mice plasma samples were increased from 189 ± 28 ng/mL(n = 3) to 413 ± 66 ng/mL(n = 3) following a 10 mg/kg body weight dose of KCN introduced subcutaneously. The sensitivity of this analytical method proved to be a tool for measuring endogenous level of ATCA in mice organs as follows: 1.2 ± 0.1 μg/g for kidney samples, 1.6 ± 0.1 μg/g for brain samples, 1.8 ± 0.2 μg/g for lung samples, 2.9 ± 0.1 μg/g for heart samples, and 3.6 ± 0.9 μg/g for liver samples. CONCLUSION: This finding suggests that ATCA has the potential to serve as a plasma biomarker / forensic biomarker for cyanide poisoning.

Digital Forensics for Recoloring via Convolutional Neural Network

As a common medium in our daily life,images are important for most people to gather information.There are also people who edit or even tamper images to deliberately deliver false information under different purposes.Thus,in digital forensics,it is necessary to understand the manipulating history of images.That requires to verify all possible manipulations applied to images.Among all the image editing manipulations,recoloring is widely used to adjust or repaint the colors in images.The color information is an important visual information that image can deliver.Thus,it is necessary to guarantee the correctness of color in digital forensics.On the other hand,many image retouching or editing applications or software are equipped with recoloring function.This enables ordinary people without expertise of image processing to apply recoloring for images.Hence,in order to secure the color information of images,in this paper,a recoloring detection method is proposed.The method is based on convolutional neural network which is quite popular in recent years.Unlike the traditional linear classifier,the proposed method can be employed for binary classification as well as multiple labels classification.The classification performance of different structure for the proposed architecture is also investigated in this paper.