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.

CVIP-Net: A Convolutional Neural Network-Based Model for Forensic Radiology Image Classification

Automated and autonomous decisions of image classification systems have essential applicability in this modern age even.Image-based decisions are commonly taken through explicit or auto-feature engineering of images.In forensic radiology,auto decisions based on images significantly affect the automation of various tasks.This study aims to assist forensic radiology in its biological profile estimation when only bones are left.A benchmarked dataset Radiology Society of North America(RSNA)has been used for research and experiments.Additionally,a locally developed dataset has also been used for research and experiments to cross-validate the results.A Convolutional Neural Network(CNN)-based model named computer vision and image processing-net(CVIP-Net)has been proposed to learn and classify image features.Experiments have also been performed on state-of-the-art pertained models,which are alex_net,inceptionv_3,google_net,Residual Network(resnet)_50,and Visual Geometry Group(VGG)-19.Experiments proved that the proposed CNN model is more accurate than other models when panoramic dental x-ray images are used to identify age and gender.The specially designed CNN-based achieved results in terms of standard evaluation measures including accuracy(98.90%),specificity(97.99%),sensitivity(99.34%),and Area under the Curve(AUC)-value(0.99)on the locally developed dataset to detect age.The classification rates of the proposed model for gender estimation were 99.57%,97.67%,98.99%,and 0.98,achieved in terms of accuracy,specificity,sensitivity,and AUC-value,respectively,on the local dataset.The classification rates of the proposed model for age estimation were 96.80%,96.80%,97.03%,and 0.99 achieved in terms of accuracy,specificity,sensitivity,and AUC-value,respectively,on the RSNA dataset.

Forensic Study on Objective Evaluation of Visual Acuity of Ametropia with the Event-related Potential P3

Objective In this study,we aimed to assess the characteristics of the P3 component from an event-related potential(ERP)that was induced by visual acuity(VA)processing.Furthermore,we sought to provide electrophysiological evidence for the objective evaluation of VA.Methods We recruited 32 participants with myopia-related ametropia.They reported no other ocular diseases and had an uncorrected VA of 4.0 in both eyes.We used the block letter“E”at different visual angles and orientations as the graphic stimuli.The oddball paradigm,consisting of 4 modules,was used for ERP analysis.The standard stimuli of each module were identical,with a visual angle of 1°15′.The visual angles of the target stimuli were 1°15′,55′,24′,and 15′.The VA test was performed on each eye separately for all participants,and all characteristics of the P3 component were analyzed.Results There was no significant difference in the P3 peak letencies between the target stimulation angle 1°15′group and the 55′group,or between the target stimulation angle 24′group and the 15′group.There was a significant difference in the P3 peak letencies between the target stimulation angle 1°15′group and the 24′group as well as the 15′group.There was a significant difference in the P3 peak letencies between the target stimulation angle 55′group and the 24′group as well as the 15′group.No significant differences were observed in the P3 amplitude between modules.Conclusion In the oddball paradigm,P3 elicitation indicated a cognitive response to the target stimuli.These data showed that the characteristics of P3 can be used as an objective evaluation of VA.

Comparison Study and Forensic Analysis between Experiment and Coupled Dynamics Simulation for Submerged Floating Tunnel Segment with Free Ends under Wave Excitations

This paper presents dynamic-behavior comparisons and related forensic analyses of a submerged floating tunnel(SFT)between numerical simulation and physical experiment under regular and irregular waves.The experiments are conducted in the 3Dwave tank with 1:33.3 scale,and the corresponding coupled time-domain simulation tool is devised for comparison.The entire SFT systemconsists of a long concrete tunnel and 12 tubular aluminummooring lines.Two numerical simulation models,the Cummins equation with 3D potential theory including second-order wave-body interaction effects and the much simpler Morison-equation-based formula with the lumped-massbased line model,are designed and compared.Forensic analyses for mooring-line adjustments in the simulation are carried out in view of the best representation of the physical system.After that,the measured pre-tension distribution and systemstiffness of twelvemooring lines arewell reproduced in the numericalmodel.Subsequently,the dynamic responses and mooring tensions of the SFT are compared under regular and irregular waves.The measured and simulated results coincide reasonably well for both regular-and irregular-wave conditions.

Research on Metaverse Security and Forensics

As a subversive concept,the metaverse has recently attracted widespread attention around the world and has set off a wave of enthusiasm in academic,industrial,and investment circles.However,while the metaverse brings unprecedented opportunities for transformation to human society,it also contains related risks.Metaverse is a digital living space with information infrastructure,interoperability system,content production system,and value settlement system as the underlying structure in which the inner core is to connect real residents through applications and identities.Through social incentives and governance rules,the metaverse reflects the digital migration of human society.This article will conduct an in-depth analysis of the metaverse from the perspective of electronic data forensics.First,from the perspective of Internet development,the background and development process of the metaverse is discussed.By systematically elaborating on the concept and connotation of the metaverse,this paper summarizes the different views of current practitioners,experts,and scholars on the metaverse.Secondly,from the perspective of metaverse security,the social risk and crime risks of the metaverse are discussed.Then the importance of metaverse forensics is raised.Third,from the perspective of blockchain,smart wearable devices,and virtual reality devices,the objects and characteristics of metaverse forensics have been studied in depth.Taking smart wearable devices as an example,this paper gives the relevant experimental process of smart bracelet forensics.Finally,many challenges faced by metaverse forensics are summarized by us which provide readers with some exploratory guidance.

Enhancing CNN for Forensics Age Estimation Using CGAN and Pseudo-Labelling

Age estimation using forensics odontology is an important process in identifying victims in criminal or mass disaster cases.Traditionally,this process is done manually by human expert.However,the speed and accuracy may vary depending on the expertise level of the human expert and other human factors such as level of fatigue and attentiveness.To improve the recognition speed and consistency,researchers have proposed automated age estimation using deep learning techniques such as Convolutional Neural Network(CNN).CNN requires many training images to obtain high percentage of recognition accuracy.Unfortunately,it is very difficult to get large number of samples of dental images for training the CNN due to the need to comply to privacy acts.A promising solution to this problem is a technique called Generative Adversarial Network(GAN).GAN is a technique that can generate synthetic images that has similar statistics as the training set.A variation of GAN called Conditional GAN(CGAN)enables the generation of the synthetic images to be controlled more precisely such that only the specified type of images will be generated.This paper proposes a CGAN for generating new dental images to increase the number of images available for training a CNN model to perform age estimation.We also propose a pseudolabelling technique to label the generated images with proper age and gender.We used the combination of real and generated images to trainDentalAge and Sex Net(DASNET),which is a CNN model for dental age estimation.Based on the experiment conducted,the accuracy,coefficient of determination(R2)and Absolute Error(AE)of DASNET have improved to 87%,0.85 and 1.18 years respectively as opposed to 74%,0.72 and 3.45 years when DASNET is trained using real,but smaller number of images.

Hyper-Tuned Convolutional Neural Networks for Authorship Verification in Digital Forensic Investigations

Authorship verification is a crucial task in digital forensic investigations,where it is often necessary to determine whether a specific individual wrote a particular piece of text.Convolutional Neural Networks(CNNs)have shown promise in solving this problem,but their performance highly depends on the choice of hyperparameters.In this paper,we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification.We conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms:Adaptive Moment Estimation(ADAM),StochasticGradientDescent(SGD),andRoot Mean Squared Propagation(RMSPROP).The model is trained and tested on a dataset of text samples collected from various authors,and the performance is evaluated using accuracy,precision,recall,and F1 score.We compare the performance of the three optimization algorithms and demonstrate the effectiveness of hyperparameter tuning in improving the accuracy of the CNN model.Our results show that the Hyper Tuned CNN model with ADAM Optimizer achieves the highest accuracy of up to 90%.Furthermore,we demonstrate that hyperparameter tuning can help achieve significant performance improvements,even using a relatively simple model architecture like CNNs.Our findings suggest that the choice of the optimization algorithm is a crucial factor in the performance of CNNs for authorship verification and that hyperparameter tuning can be an effective way to optimize this choice.Overall,this paper demonstrates the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification in digital forensic investigations.Our findings have important implications for developing accurate and reliable authorship verification systems,which are crucial for various applications in digital forensics,such as identifying the author of anonymous threatening messages or detecting cases of plagiarism.

Computer Forensics Framework for Efficient and Lawful Privacy-Preserved Investigation

Privacy preservation(PP)in Digital forensics(DF)is a conflicted and non-trivial issue.Existing solutions use the searchable encryption concept and,as a result,are not efficient and support only a keyword search.Moreover,the collected forensic data cannot be analyzed using existing well-known digital tools.This research paper first investigates the lawful requirements for PP in DF based on the organization for economic co-operation and development OECB)privacy guidelines.To have an efficient investigation process and meet the increased volume of data,the presented framework is designed based on the selective imaging concept and advanced encryption standard(AES).The proposed framework has two main modules,namely Selective Imaging Module(SIM)and Selective Analysis Module(SAM).The SIM and SAM modules are implemented based on advanced forensic format 4(AFF4)and SleuthKit open source forensics frameworks,respectively,and,accordingly,the proposed framework is evaluated in a forensically sound manner.The evaluation result is compared with other relevant works and,as a result,the proposed solution provides a privacy-preserving,efficient forensic imaging and analysis process while having also sufficient methods.Moreover,the AFF4 forensic image,produced by the SIM module,can be analyzed not only by SAM,but also by other well-known analysis tools available on the market.

Instagram Mobile Application Digital Forensics

In this research,we developed a plugin for our automated digital forensics framework to extract and preserve the evidence from the Android and the IOS-based mobile phone application,Instagram.This plugin extracts personal details from Instagram users,e.g.,name,user name,mobile number,ID,direct text or audio,video,and picture messages exchanged between different Instagram users.While developing the plugin,we identified resources available in both Android and IOS-based devices holding key forensics artifacts.We highlighted the poor privacy scheme employed by Instagram.This work,has shown how the sensitive data posted in the Instagram mobile application can easily be reconstructed,and how the traces,as well as the URL links of visual messages,can be used to access the privacy of any Instagram user without any critical credential verification.We also employed the anti-forensics method on the Instagram Android’s application and were able to restore the application from the altered or corrupted database file,which any criminal mind can use to set up or trap someone else.The outcome of this research is a plugin for our digital forensics ready framework software which could be used by law enforcement and regulatory agencies to reconstruct the digital evidence available in the Instagram mobile application directories on both Android and IOS-based mobile phones.

Internet of Things for Digital Forensics Application in Saudi Arabia

Despite the extensive empirical literature relating to the Internet of Things (IoT), surprisingly few attempts have sought to establish the ways in which digital forensics can be applied to undertake detailed examinations regarding IoT frameworks. The existing digital forensic applications have effectively held back efforts to align the IoT with digital forensic strategies. This is because the forensic applications are ill-suited to the highly complex IoT frameworks and would, therefore, struggle to amass, analyze and test the necessary evidence that would be required by a court. As such, there is a need to develop a suitable forensic framework to facilitate forensic investigations in IoT settings. Nor has considerable progress been made in terms of collecting and saving network and server logs from IoT settings to enable examinations. Consequently, this study sets out to develop and test the FB system which is a lightweight forensic framework capable of improving the scope of investigations in IoT environments. The FB system can organize the management of various IoT devices found in a smart apartment, all of which is controlled by the owner’s smart watch. This will help to perform useful functions, automate the decision-making process, and ensure that the system remains secure. A Java app is utilized to simulate the FB system, learning the user’s requirements and security expectations when installed and employing the MySQL server as a means of logging the communications of the various IoT devices.

Forensics: Collection of Sound Digital Evidence

This summary paper will discuss the concept of forensic evidence and evidence collection methods. Emphasis will be placed on the techniques used to collect forensically sound digital evidence for the purpose of introduction to digital forensics. This discussion will thereafter result in identifying and categorizing the different types of digital forensics evidence and a clear procedure for how to collect forensically sound digital evidence. This paper will further discuss the creation of awareness and promote the idea that competent practice of computer forensics collection is important for admissibility in court.

The Quality in Psychiatric Care-Forensic In-Patient Staff (QPC-FIPS) instrument: Psychometric properties and staff views of the quality of forensic psychiatric services in Sweden

The aim of the present study was to evaluate the psychometric properties and dimensionality of the instrument Quality in Psychiatric Care-Forensic In-Patient Staff (QPC-FIPS) and to describe the perceived quality of psychiatric care among forensic inpatient service staff. A sample of 348 forensic inpatient staff from 18 forensic wards in Sweden participated in the study. A confirmatory factor analysis revealed a seven-factor structure with item loadings > 0.50 on expected factors, indicating adequate psychometric properties. The staff’s ratings of quality of care were high, 94% being positive. The highest ratings were found for the secluded-environment dimension and the lowest for the secure-environment dimension. Several factors influenced the ratings of quality of care, for instance, staff’s time to perform their duties and staff’s age. It is concluded that the QPC-FIPS can give valuable information about staff’s perceptions of the quality of care provided at inpatient forensic psychiatric care services, which can be used to identify areas for quality improvement. Use of the QPC-FIPS is an easy and inexpensive way to evaluate quality in forensic inpatient care, preferably in conjunction with the QPC-FIP instrument developed for forensic inpatients and covering the same items and dimensions.

Forensic Readiness: Emerging Discipline for Creating Reliable and Secure Digital Evidence

Traditional approaches to digital forensics reconstruct events within digital systems that often are not built for the creation of evidence; however,there is an emerging discipline of forensic readiness that examines what it takes to build systems and devices that produce digital data records for which admissibility is a requirement. This paper reviews the motivation behind research in this area,a generic technical solution that uses hardware-based security to bind digital records to a particular state of a device and proposed applications of this solution in concrete,practical scenarios. Research history in this area,the notion of secure digital evidence and a technical solution are discussed. A solution to creating hardware-based security in devices producing digital evidence was proposed in 2012. Additionally,this paper revises the proposal and discusses three distinct scenarios where forensic readiness of devices and secure digital evidence are relevant. It shows,how the different requirements of the three scenarios can be realized using a hardware-based solution. The scenarios are:lawful interception of voice communication,automotive black box,precise farming. These three scenarios come from very distinctive application domains. Nevertheless,they share a common set of security requirements for processes to be documented and data records to be stored.

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.

CNN-Based Forensic Method on Contrast Enhancement with JPEG Post-Processing

As one of the most popular digital image manipulations,contrast enhancement(CE)is frequently applied to improve the visual quality of the forged images and conceal traces of forgery,therefore it can provide evidence of tampering when verifying the authenticity of digital images.Contrast enhancement forensics techniques have always drawn significant attention for image forensics community,although most approaches have obtained effective detection results,existing CE forensic methods exhibit poor performance when detecting enhanced images stored in the JPEG format.The detection of forgery on contrast adjustments in the presence of JPEG post processing is still a challenging task.In this paper,we propose a new CE forensic method based on convolutional neural network(CNN),which is robust to JPEG compression.The proposed network relies on a Xception-based CNN with two preprocessing strategies.Firstly,unlike the conventional CNNs which accepts the original image as its input,we feed the CNN with the gray-level co-occurrence matrix(GLCM)of image which contains CE fingerprints,then the constrained convolutional layer is used to extract high-frequency details in GLCMs under JPEG compression,finally the output of the constrained convolutional layer becomes the input of Xception to extract multiple features for further classification.Experimental results show that the proposed detector achieves the best performance for CE forensics under JPEG post-processing compared with the existing methods.

A Survey of Anti-forensic for Face Image Forgery

Deep learning related technologies,especially generative adversarial network,are widely used in the fields of face image tampering and forgery.Forensics researchers have proposed a variety of passive forensic and related anti-forensic methods for image tampering and forgery,especially face images,but there is still a lack of overview of anti-forensic methods at this stage.Therefore,this paper will systematically discuss the anti-forensic methods for face image tampering and forgery.Firstly,this paper expounds the relevant background,including the relevant tampering and forgery methods and forensic schemes of face images.The former mainly includes four aspects:conventional processing,fake face generation,face editing and face swapping;The latter is mainly the relevant forensic means based on spatial domain and frequency domain using deep learning technology.Then,this paper divides the existing anti-forensic works into three categories according to their method characteristics,namely hiding operation traces,forgery reconstruction and adversarial attack.Finally,this paper summarizes the limitations and prospects of the existing anti-forensic technologies.

Hair Analysis of Mammals of Brazilian Wildlife for Forensic Purposes

Wildlife trafficking is classified as the fourth largest illegal commerce in the world. Taxonomic identification of wildlife is an ordinary process for forensics experts. The aim of this study was to analyze animal’s hair from Brazilian’s wildlife through microscopic and compare morphology of bristle among species analyzed. Hair samples of nine species were analyzed. Glass slides were analyzed through optical microscopy and following measurements were obtained: total length, medulla diameter, overall diameter and overall ratio diameter of the medulla’s diameter. The images obtained at identification of animals through the morphology of hair and the statistics analysis corroborates in favor for the validation of the technique.

Factor Structure and Psychometric Properties of the Danish Adaptation of the Instrument Quality in Psychiatric Care-Forensic In-Patient Staff (QPC-FIPS)

There is a need for an internationally standardized and psychometrically tested instrument to measure the perceptions of staff members on the quality of forensic inpatient care provided. The aim of the present study was to adapt the Swedish instrument Quality of Psychiatric Care-Forensic In-Patient Staff (QPC-FIPS) to the Danish context and to evaluate its psychometric properties and factor structure in this context. All permanently employed staff members at all 27 forensic inpatient wards in Denmark were invited to answer the Danish version of the QPC-FIPS. In total, 641 staff members participated, resulting in a response rate of 80%. The Danish version of the QPC-FIPS showed adequate psychometric properties and excellent goodness of fit of the hypothesised factor structure. Hence, the Danish QPC-FIPS is an excellent instrument for evaluating quality of forensic inpatient care both in clinical practice and in cross-cultural research. The members of staff generally reported that the care provided to patients was of high quality. The quality of the forensic-specific dimension was rated the highest, followed by the support, secluded environment, encounter, discharge and participation. The quality of the secure environment dimension was perceived to be the worst. The QPC-FIPS includes important aspects of staff members’ assessments of quality of care and offers a simple and inexpensive way to evaluate psychiatric forensic inpatient care. The QPC-FIPS can be used together with the Quality of Psychiatric Care-Forensic In-Patient (QPC-FIP) instrument, which covers the same items and dimensions as the QPC-FIPS, to identify patients’ and staff members’ views on quality of care and to improve the quality of forensic psychiatric care and benchmarking.

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.

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.