Digital Text Document Watermarking Based Tampering Attack Detection via Internet

Owing to the rapid increase in the interchange of text information through internet networks,the reliability and security of digital content are becoming a major research problem.Tampering detection,Content authentication,and integrity verification of digital content interchanged through the Internet were utilized to solve a major concern in information and communication technologies.The authors’difficulties were tampering detection,authentication,and integrity verification of the digital contents.This study develops an Automated Data Mining based Digital Text Document Watermarking for Tampering Attack Detection(ADMDTW-TAD)via the Internet.The DM concept is exploited in the presented ADMDTW-TAD technique to identify the document’s appropriate characteristics to embed larger watermark information.The presented secure watermarking scheme intends to transmit digital text documents over the Internet securely.Once the watermark is embedded with no damage to the original document,it is then shared with the destination.The watermark extraction process is performed to get the original document securely.The experimental validation of the ADMDTW-TAD technique is carried out under varying levels of attack volumes,and the outcomes were inspected in terms of different measures.The simulation values indicated that the ADMDTW-TAD technique improved performance over other models.

Cyber Security within Smart Cities:A Comprehensive Study and a Novel Intrusion Detection-Based Approach

The expansion of smart cities,facilitated by digital communications,has resulted in an enhancement of the quality of life and satisfaction among residents.The Internet of Things(IoT)continually generates vast amounts of data,which is subsequently analyzed to offer services to residents.The growth and development of IoT have given rise to a new paradigm.A smart city possesses the ability to consistently monitor and utilize the physical environment,providing intelligent services such as energy,transportation,healthcare,and entertainment for both residents and visitors.Research on the security and privacy of smart cities is increasingly prevalent.These studies highlight the cybersecurity risks and the challenges faced by smart city infrastructure in handling and managing personal data.To effectively uphold individuals’security and privacy,developers of smart cities must earn the trust of the public.In this article,we delve into the realms of privacy and security within smart city applications.Our comprehensive study commences by introducing architecture and various applications tailored to smart cities.Then,concerns surrounding security and privacy within these applications are thoroughly explored subsequently.Following that,we delve into several research endeavors dedicated to addressing security and privacy issues within smart city applications.Finally,we emphasize our methodology and present a case study illustrating privacy and security in smart city contexts.Our proposal consists of defining an Artificial Intelligence(AI)based framework that allows:Thoroughly documenting penetration attempts and cyberattacks;promptly detecting any deviations from security standards;monitoring malicious behaviors and accurately tracing their sources;and establishing strong controls to effectively repel and prevent such threats.Experimental results using the Edge-IIoTset(Edge Industrial Internet of Things Security Evaluation Test)dataset demonstrated good accuracy.They were compared to related state-of-theart works,which highlight the relevance of our proposal.

AI-Driven Prioritization and Filtering of Windows Artifacts for Enhanced Digital Forensics

Digital forensics aims to uncover evidence of cybercrimes within compromised systems.These cybercrimes are often perpetrated through the deployment of malware,which inevitably leaves discernible traces within the compromised systems.Forensic analysts are tasked with extracting and subsequently analyzing data,termed as artifacts,from these systems to gather evidence.Therefore,forensic analysts must sift through extensive datasets to isolate pertinent evidence.However,manually identifying suspicious traces among numerous artifacts is time-consuming and labor-intensive.Previous studies addressed such inefficiencies by integrating artificial intelligence(AI)technologies into digital forensics.Despite the efforts in previous studies,artifacts were analyzed without considering the nature of the data within them and failed to prove their efficiency through specific evaluations.In this study,we propose a system to prioritize suspicious artifacts from compromised systems infected with malware to facilitate efficient digital forensics.Our system introduces a double-checking method that recognizes the nature of data within target artifacts and employs algorithms ideal for anomaly detection.The key ideas of this method are:(1)prioritize suspicious artifacts and filter remaining artifacts using autoencoder and(2)further prioritize suspicious artifacts and filter remaining artifacts using logarithmic entropy.Our evaluation demonstrates that our system can identify malicious artifacts with high accuracy and that its double-checking method is more efficient than alternative approaches.Our system can significantly reduce the time required for forensic analysis and serve as a reference for future studies.

Utility of Droplet Digital PCR Assay for Quantitative Detection of Norovirus in Shellfish, from Production to Consumption in Guangxi, China

Objective Shellfish are recognized as important vehicles of norovirus-associated gastroenteritis. The present study aimed to monitor norovirus contamination in oysters along the farm-to-fork continuum in Guangxi, a major oyster production area in Southwestern China. Methods Oyster samples were collected monthly from farms, markets, and restaurants, from January to December 2016. Norovirus was detected and quantified by one-step reverse transcription-droplet digital polymerase chain reaction（RT-ddPCR）. Results A total of 480 oyster samples were collected and tested for norovirus genogroups I and II. Norovirus was detected in 20.7% of samples, with genogroup II predominating. No significant difference was observed in norovirus prevalence among different sampling sites. The norovirus levels varied widely, with a geometric mean of 19,300 copies/g in digestive glands. Both norovirus prevalence and viral loads showed obvious seasonality, with a strong winter bias. Conclusion This study provides a systematic analysis of norovirus contamination ‘from the farm to the fork＇ in Guangxi. RT-ddPCR can be a useful tool for detection and quantification of low amounts of norovirus in the presence of inhibitors found particularly in foodstuffs. This approach will contribute to the development of strategies for controlling and reducing the risk of human illness resulting from shellfish consumption.

Digital coherent detection research on Brillouin optical time domain reflectometry with simplex pulse codes

The digital coherent detection technique has been investigated without any frequency-scanning device in the Brillouin optical time domain reflectometry （BOTDR）, where the simplex pulse codes are applied in the sensing system. The time domain signal of every code sequence is collected by the data acquisition card （DAQ）. A shift-averaging technique is applied in the frequency domain for the reason that the local oscillator （LO） in the coherent detection is fix-frequency deviated from the primary source. With the 31-bit simplex code, the signal-to-noise ratio （SNR） has 3.5-dB enhancement with the same single pulse traces, accordant with the theoretical analysis. The frequency fluctuation for simplex codes is 14.01 MHz less than that for a single pulse as to 4-m spatial resolution. The results are believed to be beneficial for the BOTDR performance improvement.

Application of droplet digital PCR in detection of seed-transmitted pathogen Acidovorax citrulli

Bacterial fruit blotch caused by Acidovorax citrulli is a serious threat to cucurbit industry worldwide.The pathogen is seedtransmitted,so seed detection to prevent distribution of contaminated seed is crucial in disease management.In this study,we adapted a quantitative real-time PCR(qPCR)assay to droplet digital PCR(ddPCR)format for A.citrulli detection by optimizing reaction conditions.The performance of ddPCR in detecting A.citrulli pure culture,DNA,infested watermelon/melon seed and commercial seed samples were compared with multiplex PCR,qPCR,and dilution plating method.The lowest concentrations detected(LCD)by ddPCR reached up to 2 fg DNA,and 102 CFU mL–1 bacterial cells,which were ten times more sensitive than those of the qPCR.When testing artificially infested watermelon and melon seed,0.1%infestation level was detectable using ddPCR and dilution plating method.The 26 positive samples were identified in 201 commercial seed samples through ddPCR,which was the highest positive number among all the methods.High detection sensitivity achieved by ddPCR demonstrated a promising technique for improving seed-transmitted pathogen detection threshold in the future.

Digital Signal Processing Based Real Time Vehicular Detection System

Traffic monitoring is of major importance for enforcing traffic management policies.To accomplish this task,the detection of vehicle can be achieved by exploiting image analysis techniques.In this paper,a solution is presented to obtain various traffic parameters through vehicular video detection system(VVDS).VVDS exploits the algorithm based on virtual loops to detect moving vehicle in real time.This algorithm uses the background differencing method,and vehicles can be detected through luminance difference of pixels between background image and current image.Furthermore a novel technology named as spatio-temporal image sequences analysis is applied to background differencing to improve detection accuracy.Then a hardware implementation of a digital signal processing (DSP) based board is described in detail and the board can simultaneously process four-channel video from different cameras. The benefit of usage of DSP is that images of a roadway can be processed at frame rate due to DSP′s high performance.In the end,VVDS is tested on real-world scenes and experiment results show that the system is both fast and robust to the surveillance of transportation.

Using UAVs for detection of trees from digital surface models

A difficult problem in forestry is tree inventory.In this study, a GoProHero attached to a small unmanned aerial vehicle was used to capture images of a small area covered by pinus pinea trees. Then, a digital surface model was generated with image matching. The elevation model representing the terrain surface, a ‘digital terrain model’,was extracted from the digital surface model using morphological filtering. Individual trees were extracted by analyzing elevation flow on the digital elevation model because the elevation reached the highest value on the tree peaks compared to the neighborhood elevation pixels. The quality of the results was assessed by comparison with reference data for correctness of the estimated number of trees. The tree heights were calculated and evaluated with ground truth dataset. The results showed 80% correctness and 90% completeness.

A Novel Method for Detection and Removal of Line Scratch in High Resolution Digital Film

With increasing need of high quality movie, more and more standard resolution films are upconverted to the high-resolution films. After this operation, the defects exist in the old movie are more obvious because they are enlarged in size, therefore, an efficient artifacts detection method with more precise result and lower computational complexity is in need. This paper provided a line scratch mathematical model, which derives from the Kokaram model and Bruni model, and then gave a detection method to meet the requirements of the high-resolution video application.

Digital Cross-Correlation Detection of Multi-Laser Beams Measuring System for Wind Field Detection

A cross-correlation detection method to process backscatter signals of multi-laser beams measuring （MLBM） is presented, which can be firstly filtered by the digital filter composed of average median filter and finite impulse response （FIR） digital filter. The processing of backscatter signals using single-pulse and three-pulse cross-correlation detection methods is depicted in detail. From calculation results, the multi-pulse cross-correlation detection could effectively improve signal-to-noise ratio （SNR）. Finally, both wind velocity and direction are determined by the peak-delay method based on the correlation function which shows high measuring precision and high SNR of the MLBM system with the assistance of the digital cross- correlation detection.

Use of the Wavelet Transform for Digital Terrain Model Edge Detection (Special Issue—Wavelet Analysis)

The purpose of this work is to analyze the feasibility of using the wavelet transform in the edge detection of digital terrain models (DTM) obtained by Laser Scanner. The Haar wavelet transform and the edge detection method called Wavelet Transform Modulus Maxima (WTMM), both implemented in Matlab language, were used. In order to validate and verify the efficiency of WTMM, the edge detection of the same DTM was performed by the Roberts, Sobel-Feldman and Canny methods, chosen due to the wide use in the scientific community in the area of Image Processing and Remote Sensing. The comparison of the results showed superior performance of WTMM in terms of processing time.

Sequential Detection of Digital Watermarking

The paper analyzed a new watermarking detection paradigm including double detection thresholds based on sequential hypothesis testing. A joint design of watermarking encoding and detection was proposed. The paradigm had good immunity to noisy signal attacks and high detection probability. Many experiments proved that the above algorithm can detect watermarks about 66% faster than popular detectors, which could have significant impact on many applications such as video watermarking detection and watermark-searching in a large database of digital contents.

Research on Influence of Electromagnetic Interference on X-Ray Digital Detection

X-ray digital imaging technology has found wide application owing to its advantages of real-time, visualization and rapid imaging. In substations the substantial electromagnetic interference has some influence on the live detection by the X-ray digital imaging technology, hindering the promotion of the technology in the detection of electric equipment. Based on a large number of field tests, the author carded out a series of researches on electromagnetic interference protection measures, image de-noising, and image enhancement algorithms.

Dual Attribute Adversarial Camouflage toward camouflaged object detection

The object detectors can precisely detect the camouflaged object beyond human perception.The investigations reveal that the CNNs-based(Convolution Neural Networks)detectors are vulnerable to adversarial attacks.Some works can fool detectors by crafting the adversarial camouflage attached to the object,leading to wrong prediction.It is hard for military operations to utilize the existing adversarial camouflage due to its conspicuous appearance.Motivated by this,this paper proposes the Dual Attribute Adversarial Camouflage(DAAC)for evading the detection by both detectors and humans.Generating DAAC includes two steps:(1)Extracting features from a specific type of scene to generate individual soldier digital camouflage;(2)Attaching the adversarial patch with scene features constraint to the individual soldier digital camouflage to generate the adversarial attribute of DAAC.The visual effects of the individual soldier digital camouflage and the adversarial patch will be improved after integrating with the scene features.Experiment results show that objects camouflaged by DAAC are well integrated with background and achieve visual concealment while remaining effective in fooling object detectors,thus evading the detections by both detectors and humans in the digital domain.This work can serve as the reference for crafting the adversarial camouflage in the physical world.

A Review of Machine Learning Techniques in Cyberbullying Detection

Automatic identification of cyberbullying is a problem that is gaining traction,especially in the Machine Learning areas.Not only is it complicated,but it has also become a pressing necessity,considering how social media has become an integral part of adolescents’lives and how serious the impacts of cyberbullying and online harassment can be,particularly among teenagers.This paper contains a systematic literature review of modern strategies,machine learning methods,and technical means for detecting cyberbullying and the aggressive command of an individual in the information space of the Internet.We undertake an in-depth review of 13 papers from four scientific databases.The article provides an overview of scientific literature to analyze the problem of cyberbullying detection from the point of view of machine learning and natural language processing.In this review,we consider a cyberbullying detection framework on social media platforms,which includes data collection,data processing,feature selection,feature extraction,and the application ofmachine learning to classify whether texts contain cyberbullying or not.This article seeks to guide future research on this topic toward a more consistent perspective with the phenomenon’s description and depiction,allowing future solutions to be more practical and effective.

Using Digital Twin to Diagnose Faults in Braiding Machinery Based on IoT

The digital twin(DT)includes real-time data analytics based on the actual product or manufacturing processing parameters.Data from digital twins can predict asset maintenance requirements ahead of time.This saves money by decreasing operating expenses and asset downtime,which improves company efficiency.In this paper,a digital twin in braiding machinery based on IoT(DTBM-IoT)used to diagnose faults.When an imbalance fault occurs,the system gathers experimental data.After that,the information is sent into a digital win model of the rotor system to see whether it can quantify and locate imbalance for defect detection.It is possible to anticipate asset maintenance requirements with DT technology by IoT(Internet of Things)sensors,XR(X-Ray)capabilities,and AI-powered analytics.A DT model’s appropriate design and flexibility remain difficult because of the nonlinear dynamics and unpre-dictability inherent in the degrading process of equipment.The results indicate that the DT in braiding machinery developed allows for precise diagnostic and dynamic deterioration analysis.At least there is 37%growth in efficiency over conventional approaches.

A Thorough Investigation on Image Forgery Detection

Image forging is the alteration of a digital image to conceal some of the necessary or helpful information.It cannot be easy to distinguish themodified region fromthe original image in somecircumstances.The demand for authenticity and the integrity of the image drive the detection of a fabricated image.There have been cases of ownership infringements or fraudulent actions by counterfeiting multimedia files,including re-sampling or copy-moving.This work presents a high-level view of the forensics of digital images and their possible detection approaches.This work presents a thorough analysis of digital image forgery detection techniques with their steps and effectiveness.These methods have identified forgery and its type and compared it with state of the art.This work will help us to find the best forgery detection technique based on the different environments.It also shows the current issues in other methods,which can help researchers find future scope for further research in this field.

Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security

Recent security applications in mobile technologies and computer sys-tems use face recognition for high-end security.Despite numerous security tech-niques,face recognition is considered a high-security control.Developers fuse and carry out face identification as an access authority into these applications.Still,face identification authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized user.In the existing spoofing detection algorithm,there was some loss in the recreation of images.This research proposes an unobtrusive technique to detect face spoofing attacks that apply a single frame of the sequenced set of frames to overcome the above-said problems.This research offers a novel Edge-Net autoencoder to select convoluted and dominant features of the input diffused structure.First,this pro-posed method is tested with the Cross-ethnicity Face Anti-spoofing(CASIA),Fetal alcohol spectrum disorders(FASD)dataset.This database has three models of attacks:distorted photographs in printed form,photographs with removed eyes portion,and video attacks.The images are taken with three different quality cameras:low,average,and high-quality real and spoofed images.An extensive experimental study was performed with CASIA-FASD,3 Diagnostic Machine Aid-Digital(DMAD)dataset that proved higher results when compared to existing algorithms.

Detecting circulating tumor material and digital pathology imaging during pancreatic cancer progression

Pancreatic cancer(PC) is a leading cause of cancerrelated death worldwide. Clinical symptoms typically present late when treatment options are limited and survival expectancy is very short. Metastatic mutations are heterogeneous and can accumulate up to twenty years before PC diagnosis. Given such genetic diversity, detecting and managing the complex states of disease progression may be limited to imaging modalities and markers present in circulation. Recent developments in digital pathology imaging show potential for early PC detection, making a differential diagnosis, and predicting treatment sensitivity leading to long-term survival in advanced stage patients. Despite large research efforts, the only serum marker currently approved for clinical use is CA 19-9. Utility of CA 19-9 has been shown to improve when it is used in combination with PC-specific markers. Efforts are being made to develop early-screening assays that can detect tumor-derived material, present in circulation, before metastasis takes a significant course. Detection of markers that identify circulating tumor cells and tumor-derived extracellular vesicles(EVs) in biofluid samples offers a promising non-invasive method for this purpose. Circulating tumor cells exhibit varying expression of epithelial and mesenchymal markers depending on the state of tumor differentiation. This offers a possibility for monitoring disease progression using minimally invasive procedures. EVs also offer the benefit of detecting molecular cargo of tumor origin and add the potential to detect circulating vesicle markers from tumors that lack invasive properties. This review integrates recent genetic insights of PC progression with developments in digitalpathology and early detection of tumor-derived circulating material.

Network Intrusion Detection in Internet of Blended Environment Using Ensemble of Heterogeneous Autoencoders(E-HAE)

Contemporary attackers,mainly motivated by financial gain,consistently devise sophisticated penetration techniques to access important information or data.The growing use of Internet of Things(IoT)technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation,as it facilitates multiple new attack vectors to emerge effortlessly.As such,existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems.To address this problem,we designed a blended threat detection approach,considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence.We collectively refer to the convergence of different technology sectors as the internet of blended environment.The proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational autoencoder.An extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02%detection accuracy.Furthermore,performance of the proposed approach was compared with various single model(autoencoder)-based network intrusion detection approaches:autoencoder,variational autoencoder,convolutional variational autoencoder,and long short-term memory variational autoencoder.The proposed model outperformed all compared models,demonstrating F1-score improvements of 4.99%,2.25%,1.92%,and 3.69%,respectively.