Reviewerly/exHarmony huggingface dataset:
Optimized steganography techniques based on PVDS and genetic algorithm

Due to the rapid development of confidential digital communication over international networks, security is the most significant factor. Steganography is the science of concealed communication and is used to conceal the presence of secret information among the various methods of hiding data in communications. Steganography techniques are usually implemented either in spatial or frequency domains. Some spatial domain methods fundamentally utilize the absolute difference value between neighbor pixels to encode the secret message, categorized as Pixel-Value Differencing Steganography methods (PVDS). In PVDS, the hiding capacity of the adjacent pixels depends on their difference value. Consequently, the secret data will be embedded with a high visual quality of the stego-image. Indicator-based PVDS (IPVDS) technique is derived from the PVDS method. It depends on an Indicator pixel (IP) to extract the secret data correctly. The Genetic Algorithm (GA) approach to optimization depends on the concept of survival of the fittest. This paper introduces two proposals, GA-based steganography schemes (GA-IPVD) and (GA-IPVDM). These new techniques encoded secret data using the IPVDS method. Before embedding data in the cover image, the order of the pixels corresponding to the secret data is modified and rearranged. GA controls the operation of rearranging and modifying the order of pixels and all parameters. The current paper addresses steganography as an optimization problem and seeks to determine the optimal order of the pixels that enhance the matching between the cover image and stego-image to reduce distortion despite high embedded capacity. In the first proposed technique GA-IPVD, GA concentrates on the imperceptibility of embedded data and maximizes the quality of the stego-image according to the given secret data. In contrast, the GA maximizes the capacity of embedded data while maintaining an acceptable amount of stego-image quality in the second proposed technique GA-IPVDM. The proposed systems have embedding key that increase the security of proposed schemes. The experimental results demonstrate that the proposed methods can embed a large quantity of secret data while maintaining an acceptable visual quality of the stego-images. The proposed technique has also demonstrated excellent resistance to a variety of stego-attacks, including the pixel difference histogram (PDH) as well as regular and singular (RS) analyses. Additionally, the out-of-boundary pixel issue (BI), which endures in the majority of modern data hiding algorithms, has been successfully resolved.
Multi-branch stacking remote sensing image target detection based on YOLOv5

Optical remote sensing is crucial in land management, maritime safety, and rescue operations. Currently, high resolution target detection faces the problems including feature loss, false detection, and limited network robustness. To tackle the aforementioned issues, this study introduces a novel MBS-NET model, which is built upon the YOLOv5 structure. The proposed model presents a multi-branch stacking module structure to precisely capture deep target feature information. By introducing a dual-channel attention mechanism module (EGCA) at the model's neck, the proposed method ensures discriminative feature acquisition in crowded objects and vast spatial scenes. Prior to network training, introduce an improved data augmentation strategy to accommodate the multi-scale and directional variations in remote sensing objectives for model detection. Experimental results on the large-scale public DIOR dataset demonstrate that the MBS-NET model introduced in this paper displays exceptional performance and remarkable interpretability in remote sensing scenarios at a large scale. MBS-NET model outperforms YOLOv5 and YOLOv7 models by increasing the accuracy by 5% and 2% respectively. In addition, the recall rate and F1 Score index of MBS-NET model is superior to those of other methods, resulting in significant improvement of detection accuracy and robustness in large scenes.
A Digital Forensics Model for the Examination of QR Code and Android App to Investigate Aadhaar Card Identity Fraud

AbstractThere are various new-age crime rackets reported with the help of digital technology and such cases are routinely investigated by the police and digital forensics analysts. In the course of investigation of different rackets such as Aadhaar card, ATM card, PAN card, ration card, voter identity card, passport, fake Indian currency note, various electronic gadgets such as mobile phones, laptops, desktops, biometric scanners, GPS devices, SIM cards, portable skimmers and hi-tech printers are usually taken from the fraudsters for digital forensics analysis. In one case, some Aadhaar cards, mobile phones, laptops, SIM cards and memory card were seized from some suspected airline passengers by the law enforcement agency. All the key probe elements were received for digital forensics examination. In this paper, we propose a digital forensics model for decoding a distorted QR code and specialized Android app used in the creation of fake Aadhaar cards. This model comprised a new algorithm to decode QR codes and recovery of background data including resources associated with the QR code. The proposed model was tested with a QR code retrieved from a suspect’s laptop and Aadhaar card document. The Android app used for preparation of fake Aadhaar card was analyzed in our model and also checked in a mobile phone to verify data generated through the app. The result of the analysis detects fake Aadhaar cards. Further, examination of mobile phones and laptops revealed various technical data, supporting link evidence for preparing disputed fake Aadhaar cards for ID proof.Keywords: Aadhaar cardAndroid appdecodedigital forensicsID proofQR code AcknowledgmentThe authors are thankful to Mr. Oscar Ochoi, MSAB and Mr. Ajay Sariyal, MSAB for providing technical support to analyze the data for successful investigation of the case.Disclosure statementNo potential conflict of interest was reported by the authors.
Real-time Tracking of Auditing Process Progress with a Customizable Application for Cybersecurity Standards Compliance: A Case Study on ISO 27001 and TISAX

Certifications such as ISO 27001 and TISAX are crucial for organizations seeking to bolster cybersecurity and protect against evolving cyber threats. However, the process of obtaining these certifications involves navigating a rigorous auditing process fraught with challenges. This research paper addresses the difficulties faced by auditors in adapting to constant updates in regulatory requirements, relying on static tools during the gap analysis stage, and managing effective communication and collaboration with clients. Additionally, challenges in real-time tracking of client progress, document management, and generating on-demand reports further complicate the auditing process.To overcome these obstacles, this study introduces AUTOSYS as a proposed solution, detailing the technologies employed, the audit process, the web application architecture, and the implemented security measures. AUTOSYS is a highly customizable web application, for real-time audit monitoring. By leveraging real-time notifications and enabling continuous tracking of audited company activities, including tasks, incidents, and risks. The application incorporates a personalized checklist that adapts to norm requirement updates, thereby facilitating enhanced evaluation of norm compliance and maturity. Furthermore, the proposed solution streamlines audits by effectively managing shared checklists, ensuring efficient document management, and upholding privacy controls.
Ensuring Authenticity and Sustainability in Perfume Production: A Blockchain Solution for the Fragrance Supply Chain

The perfume industry’s complex and multi-faceted supply chain is challenged by issues of transparency, accountability, and efficiency. To address these issues, this paper proposes a ScentTrack blockchain-based solution for the perfume manufactory, that provides benefits such as improved product quality, supply chain efficiency, and increased trust among supply chain partners. The system was created in collaboration with a perfume manufacturer based in Saudi Arabia, and it incorporates multiple chaincodes distributed across multiple channels. These chaincodes serve to monitor and track the various stages of the perfume supply chain. The proposed solution leverages the automation capabilities of blockchain to enhance trust and accountability among participants. To facilitate this, each participant will have access to their own application portal, enabling them to manage and monitor their respective supply chain. Moreover, the system includes extensive event notifications that keep all stakeholders informed about the multiple steps within each stage of the supply chain they are interested in.Based on the findings, the study suggests that a blockchain system for perfume manufacturing supply chains could transform the industry by establishing a more efficient, secure, and transparent network, which lowerultimately lowering costs while improving quality. Additionally, the proposed solution could be adapted for other supply chains, although further research is required to assess its applicability in different industries. The implementation of this blockchain-based solution in the perfume industry could offer a practical demonstration of how blockchain technology can address the challenges of supply chain management.
Analysis of Deep Learning Models to Detect Breast Cancer from Histopathology Images

Breast cancer is a disease where breast cells grow out of control and leads to cancer. Various methodologies have been developed to identify breast cancer. In this paper, we have developed an approach to classify breast cancer from histopathology images. The approach makes use of deep learning based architectures by setting same parameters for all while training and testing on them. Thereafter, all the architectures are compared to see which one is most suited for the classification of breast cancer. Previous works on AlexNet, VGG, ResNet have already been published, and here we have tried to see the performance of those models which have less number of trainable parameters, namely DenseNet121, DenseNet169, DenseNet201, EfficientNetB0, EfficientNetB5, EfficientNetV2B0 and EfficientNetV2S. Here, all the experiments are conducted on BreakHis histopathology dataset by utilizing all the images of resolutions 40X, 100X, 200X and 400X of benign and malignant cancer.
Combining Deep CNNs and Superpixels for Polyp Segmentation in Colonoscopy Images

Polyps can be missed as a result of fatigue or inattention during prolonged or consecutive colonoscopies, with an estimated average rate of 16.8-28%. Consequently, patients with undetected polyps are less likely to survive late-stage colorectal cancer than those without polyps. The detection of polyps in colonoscopy images has been automated using a variety of approaches in order to address this issue. As a result, polyps will be prevented from being overlooked by humans, and the time and expense associated with screening numerous colonoscopies or wireless capsule endoscopies will be reduced. Our primary goal is to develop a system that precisely defines the location of polyps on an image, therefore increasing overall detection accuracy and enabling timely identification of colorectal cancer. In this work, we aim to combine UNet architecture with the pretrained model Inception-Resnet-V2 in the Encoder, and then a phase of segmentation Refinement based superpixels with Simple Linear Iterative Clustering (SLIC) for superpixels generation. The proposed methods’ average Dice on CVC-Clinic612 dataset is 0.8903, with MIoU and MPA of 0.8085 and 0.9796 respectively, and the average Dice on Kvasir-SEG dataset is 0.8758 with MIoU and MPA of 0.7936 and 0.9567 respectively. Comparing the results to state-of-the-art methods, the results show high performance.
Attention to COVID-19: Abstractive Summarization of COVID-19 Research with State-of-the-Art Transformers

The COVID-19 pandemic has led to an over-whelming volume of scientific publications as researchers strive to address the crisis. To alleviate information overload, the COVID-19 Open Research Dataset (CORD-19) was released to help in analyzing large amounts of data and facilitate faster response. Most existing tools based on CORD-19 use extractive summarizers, which suffer from poor coherence and readability. Thus more abstractive summarizers for COVID-19 are needed. Specifically, using state-of-the-art (SOTA) transformers has shown to be successful in summarizing biomedical datasets like arXiv and PubMed. In this study, we finetune two checkpoints of SOTA transformer PEGASUS-X on the CORD-19 dataset: PEGASUS-X <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BASE</inf> -CORD19 and PEGASUS-X <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BASE</inf> -arXiv-CORD19. Our results highlight the importance of finetuning summarizers on domain-specific datasets in the abstractive summarization of COVID-19 research: checkpoints finetuned on CORD-19 out-perform other existing checkpoints and transformers finetuned on more general research datasets (e.g., arXiv and PubMed). After stopword removal in evaluation, we observe that PEGASUS-X <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BASE</inf> -arXiv-CORD19 surpasses PEGASUS-X <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BASE</inf> -CORD19 by a small margin. Our checkpoints still fall behind earlier sequence-to-sequence models; however, this limitation may be due to our constrained GPU resources. Future works, with access to more resources, can further improve our checkpoints for COVID-19 research summarization.
Implementation of Convolutional Neural Network of Non-Biodegradable Garbage Classifier and Segregator Based on VGG16 Architecture

Problems posed by solid waste management have been raising alarming threats today. The increasing number of garbage clogging the drainage systems and the limited space for waste disposal are some of the vivid indications of the waste crisis. One of the solutions for this problem is an intelligent system for classification and segregation of non-biodegradable wastes is implemented with the aid of Convolutional Neural Networks. The system is trained with an initial dataset coming from the images of the waste categories such as plastic bottles, plastic wrappers, plastic cups, and metal canneries. Through VGG16 deep learning architecture, the system can identify the garbage input and classify them accurately. The significance of this study is regarding automation of the garbage segregation process in building an image classifier model that can be deployed into the Materials Recovery Facilities. This is where they sort and market recyclable wastes for the user-end manufacturers. In this manner, it lessens the production of synthetic materials and magnifies the recycling process. The results are shown through graphical representations of the total accuracy of the system against the images subjected in the testing. Although indirect, this research serves as a solution to present the capability of CNN to solve real-world situations.
A Foundation Model Approach to Detect Machine Generated Text

Large Language Models with autoregression generative capabilities like ChatGPT have garnered lots of attention from its launch. However, the cyber security community is also wary of the threats that it poses with cybercriminal and cyber security threat related activities. It could generate highly deceptive phishing and social engineering attacks that could evade human detection and render existing phishing or social engineering detection tools useless. Inspired by the approach used to develop Foundation Model that resulted with amazing capabilities from the contemporary model constructs like ChatGPT, our research endeavour demonstrates a model construct developed using Foundation model approach could yield potential as defensive tool to detect GPT generated text. Preliminary evaluation results show promising results.
An efficient hybrid approach for vehicle detection and tracking

Real-time vehicle detection and tracking is a major research problem in traffic surveillance systems as it aims to improve road safety, optimize traffic flow, and increase transportation efficiency. Traffic surveillance systems typically collect and analyze live traffic footages from multiple cameras, to provide valuable and rich information compared to other sensor-based approaches. However, real-time analysis of traffic footage can be challenging due to the complexity of detecting and tracking objects in the fields. In this paper, we propose an efficient and robust framework for vehicle detection and tracking that is suitable for low-resource and edge devices without compromising precision and accuracy. Our approach combines the Background Subtraction method (BS), an efficient moving object detector, with a deep CNN-based detector in a hybrid detection approach.
Basketball Free Throw Analysis using Object Detection Techniques

The free throw in basketball is one of the important skills for a player. The accuracy of success of the throw affects the final score in a game, that’s why players make more effort to have a high accuracy in scoring free throw.The studies proved that as a human being, every player has his own way to throw the ball. The player position, the ball shot angle and velocity, are the factors that change from a player to another, and therefore affect the success of the throw even for the same player.The aim of this work is to propose a method to automatically measure and correct the extracted parameters of a basketball free throw shooting. The approach aims to create an automated system to correct the position of basketball throws and track the movement of the ball.
Using Deep Generative Models for Glossy Appearance Synthesis and Exploration

Generating images with realistic material appearance using a physically-based renderer demands significant time and human labor.The images are used in psychophysical experiments to study human perception of material appearance attributes, such as glossiness.Recently, deep learning-based image synthesis models have emerged as a promising approach for generating realistic images with less human supervision.Deep Generative Models are deep learning-based models that learn to generate unique and novel images based on a given training data distribution.Using them for image synthesis is fast and manually less tiresome.An additional benefit these Deep Generative Models offer is latent space encodings that may help to better understand the feature space of gloss and its perception.In this study, we propose to explore the possibility of using Deep Generative Models for realistic image synthesis, focusing on gloss appearance and evaluating the efficiency of such gloss generation process using psychophysical experiments.Additionally, we build tools to extract the latent space of generative models to use them as a feature space representation of gloss appearance and perception.Finally, we analyse the trends and patterns in the learnt feature space to aid gloss appearance modelling.
VStab-QuAD: A New Video-Stabilization Quality Assessment Database

We introduce VStab-QuAD, a novel Video-Stabilization Quality Assessment Database, consisting of 320 stabilized video sequences obtained from 64 unstable video sequences with different contents (crowd, parallax, running, zooming, etc) using five different strong performing video-stabilization methods. The stabilized videos that are considered contain residual instability-induced image distortions and additional distortions introduced as a side effect of the digital stabilization algorithms applied. In addition, this study presents a comprehensive objective analysis of the video sequences and their validity for video-stabilization quality assessment. Subjective experiments were performed in a controlled environment using the single stimulus continuous quality evaluation (SSCQE) protocol, and their results are included in the database. Therefore, our proposed VStab-QuAD contains (i) the 64 original videos with in-capture distortions due to initial video instability, (ii) the 320 stabilized videos produced with the five different algorithms, and (iii) associated perceptual quality scores.This paper hence contributes VStab-QuAD as a comprehensive public benchmark database designed to facilitate the development of powerful video-stabilization methods and quality assessment metrics in real-life video acquisition scenarios.
Profiling Language Sign Using Deep Learning

Large segments of society place a high value on sign language as a distinct communication language. In computer vision, gesture detection is still a developing phenomenon and a hot topic. Aside from its many applications in immersive game technology and human-computer interaction, gesture recognition has many applications in deaf-mute sign language interpretation. In order to profile language signs, we created a model of image processing that recognizes Arabic hand sign language based on artificial neural networks, starting with the data collection process and continuing with data profiling, then using the deep learning model to train and identify gestures, all the way to the presentation of the results. We offer a method to help with the conversion of sign language into voice and text-based formats. The outcomes we have achieved are crucial, important and significant for our next achievements.
Vision-Based Gesture Recognition for Mouse Control

Vision-based hand gesture recognition (VGR) systems must provide the following functionalities or criteria to control a computer mouse: (i) hand tracking ability, (ii) continuous static and dynamic hand gesture recognition, and (iii) efficient resource management. Our motivation stems from the fact that only a few research so far has accommodated all these three criteria. In this paper, we developed a VGR system that accommodates these three criteria. We propose an algorithm that simultaneously detects and classifies hand gestures using RGB images and hand skeletons. To evaluate our work, we used the IPN dataset which consists of hand gestures that are suitable for mouse control. Compared to previous methods on the IPN dataset, our resulting VGR system achieves better performance in both isolated and continuous hand gesture recognition (HGR). For continuous HGR, we achieved 61.30% Levenshtein accuracy.
Enhancement of a User Authentication Scheme for Big Data Collection in IoT-Based Intelligent Transportation System

In recent years, research on the Internet of Things (IoT) has seen widespread growth, with a particular focus on mobile networks, especially intelligent transportation systems (ITS), due to their potential application in smart cities. However, communication within ITS suffers from numerous issues, leading to multiple attacks. Therefore, ensuring the secure transmission of messages in ITS poses a significant challenge. Recently, Srinivas et al. have proposed “a three-factor authentication scheme for Big Data collection in IoT-based ITS, called UAP-BCIoT”. However, the proposed scheme has some shortcomings in terms of authentication. In this paper, we show how this flaw affects the users’ access to the data provided by the IoT devices, which are deployed on vehicles. In addition, we propose enhancements to overcome the scheme’s authentication issue. Furthermore, a comparative analysis reveals that the enhanced UAP-BCIoT achieves better efficiency, security, and performance compared to UAP-BCIoT and other schemes.