Authentication Verification Research Articles

Rapid in situ identification of honey authenticity based on RP-Nano-ESI-MS using online desalting

High-content sugar in honey frequently results in severe matrix effects and requires complex pretreatment prior to analysis, posing significant challenges for the rapid analysis of honey. In this study, the reversal polarity nano-electrospray ionization mass spectrometry (RP-Nano-ESI-MS) analysis was developed for the direct evaluation of honey samples. The results indicated that RP-Nano-ESI-MS significantly mitigated the matrix effects induced by high-content sugar through the implementation of online desalting. Furthermore, RP-Nano-ESI-MS has been proven capable of not only differentiating acacia honey adulterated with 10% rape honey, but also effectively distinguishing six types of honey and exhibiting remarkable proficiency in detecting honey adulteration and botanical traceability. Additionally, RP-Nano-ESI-MS exhibited strong quantitative abilities, effectively characterizing variations in amino acid composition among six types of honey with high stability and reproducibility. Our studies underscore the significant potential of RP-Nano-ESI-MS for its rapid in situ analysis of sugar-rich foods like honey, especially in their authenticity verification.

Image authentication with exclusive-OR operated optical vortices

Optical vortices carrying orbital angular momentum have drawn much attention because they provide high-dimensional encoding. Employing an array of optical vortices, we demonstrate an authentication verification system. For security authentication, an exclusive-OR logic operation has been implemented employing a light beam consisting of an array of vortices. A liquid crystal spatial light modulator has been used to generate orthogonal states of optical vortices. The proposed technique can provide a secure method of authentication with straightforward implementation. We have presented simulation and experimental results to verify the proposed scheme.

Video Detection Method Based on Temporal and Spatial Foundations for Accurate Verification of Authenticity

With the rapid development of deepfake technology, it is finding applications in virtual movie production and entertainment. However, its potential for malicious use, such as generating false information, fake news, or synthetic pornography, poses significant threats to national and social security. Various research disciplines are actively engaged in developing deepfake video detection technologies to mitigate the risks associated with malicious deepfake content. Therefore, the importance of deepfake video detection technology cannot be overemphasized. This study addresses the challenge posed by images in nonexistent datasets by analyzing deepfake video detection methods. Using temporal and spatial detection techniques and employing 68 facial landmarks for alignment and feature extraction, this research integrates the attention-guided data augmentation (AGDA) strategy to enhance generalization capabilities. The detection performance is evaluated on four datasets: UADFV, FaceForensics++, Celeb-DF, and DFDC, with superior results compared to alternative approaches. To evaluate the study’s ability to accurately discriminate authenticity, detection experiments are conducted on both genuine and deepfake videos synthesized using the DeepFaceLab and FakeApp frameworks. The experimental results show better performance in detecting deepfake videos than other methods compared.

Olfactory EEG induced by odor: Used for food identification and pleasure analysis

As the need for food authenticity verification increases, sensory evaluation of food odors has become widely recognized. This study presents a theory based on electroencephalography (EEG) to create an Olfactory Perception Dimensional Space (EEG-OPDS), using feature engineering and ensemble learning to establish material and emotional spaces based on odor perception and pleasure. The study examines the intrinsic connection between these two spaces and explores the mechanisms of integration and differentiation in constructing the OPDS. This method effectively visualizes various types of food odors while identifying their perceptual intensity and pleasantness. The average classification accuracy for odor recognition in an eight-category experiment is 96.1%. Conversely, the average classification accuracy for sensory pleasantness recognition in a two-category experiment is 98.8%. The theoretical approach proposed in this study, based on olfactory EEG signals to construct an OPDS, captures the subtle perceptual differences and individualized pleasantness responses to food odors.

Intelligent Video Surveillance System for Bank

With the increasing sophistication of financial crimes and the growing demand for secure banking services, the implementation of advanced security measures has become imperative for banks worldwide. Facial recognition technology emerges as a promising solution to enhance security and streamline authorization verification processes. This paper explores the application of facial recognition technology in banks to determine the authorization status of individuals accessing accounts or conducting transactions. The primary objective of this research is to investigate the efficacy of facial recognition systems in accurately identifying and verifying the identity of individuals in banking environments. By leveraging biometric data, such as facial features, these systems aim to authenticate users with a high level of accuracy and reliability. Moreover, the integration of facial recognition technology enables banks to combat various forms of fraud, including identity theft, account takeover, and unauthorized access. This study will examine the technological aspects of facial recognition systems, including their underlying algorithms, data processing techniques, and integration capabilities with existing banking infrastructure. Additionally, it will analyze the security implications and privacy concerns associated with the deployment of facial recognition technology in banking operations. Furthermore, the research will explore the practical implementation of facial recognition systems in real-world banking scenarios, evaluating their effectiveness in enhancing security, reducing fraud, and improving customer experience. It will assess the potential challenges and limitations faced during deployment, such as system accuracy, scalability, and regulatory compliance

Validation of Raman spectroscopic models to verify the origin of Australian beef grown under different production systems

Verification of beef production systems and authentication of origin is becoming increasingly important as consumers base purchase decisions on a greater number of perceived values including the healthiness and environmental impact of products. Previously Raman spectroscopy has been explored as a tool to classify carcases from grass and grain fed cattle. Thus, the aim of the current study was to validate Partial Least Squares Discriminant Analysis (PLS-DA) models created using independent samples from carcases sampled from northern and southern Australian production systems in 2019, 2020 and 2021. Validation of the robustness of discrimination models was undertaken using spectral measures of fat from 585 carcases which were measured in 2022 using a Raman handheld device with a sample excised for fatty acid analysis. PLS-DA models were constructed and then employed to classify samples as either grass or grain fed in a two-class model. Overall, predictions were high with accuracies of up to 95.7% however, variation in the predictive ability was noted with models created for southern cattle yielding an accuracy of 73.2%. While some variation in fatty acids and therefore models can be attributed to differences in genetics, management and diet, the impact of duration of feeding is currently unknown and thus further work is warranted.

Video Forgery Detection with Deep Learning Using RESNET and CNN Algorithm

Video forgery is continuously increasing in the digital world due to breaches of information security, consequently establishing a scenario for image and video content monitoring for forgery identification. The spread of fake videos raises security risks and anarchy in society. The reason for video forgery is to the augmentation in the malware, which has facilitated user (anyone) to upload, download, or share objects online comprising audio, images, or video. With the development of technology and ease of creation of fake content, the manipulation of media is carried out on a large scale in recent times. Video forgery detection has applications in media science, forensic analysis, digital investigations, and authenticity verification of a video. The purpose of video forensic technology is to extract features to distinguish fake content frames from original videos.

The influence of religiosity on food choice among British Muslims: A qualitative study.

Background: Religiosity is known to have a socio-cultural influence on food choice. However, to date, research exploring the influence of Islam on food selection has almost exclusively focused on fasting during Ramadan and has not explored the influences of Islam on everyday food choices among Muslim people. Aim: This qualitative study explored the influence of Islamic religiosity on everyday food choices among Muslim people. Methods: Thirty-two adult participants residing in the United Kingdom (n = 16 faith leaders and n = 16 lay Muslim people) were recruited from three Sunni mosques, and data was collected using semi-structured interviews. The data was analysed using reflexive thematic analysis and a constant comparison method was applied to draw out similarities and differences between faith leaders and lay Muslim people. Results: The results revealed that Islamic religiosity had an influence over food choice with two main overarching themes 1) Demonstrating religious obedience through food choices and, 2) Spheres of influence on food choice; and five sub-themes 1a) Trusting in familiar food providers, 1b) Verification of halal authenticity, 1c) Seeking purity within food, 2a) The Prophet Muhammed (Peace Be Upon Him) as a role model for food choice and, 2b) Islamic jurisprudence. Conclusion: These findings provide important insights into the influence of Islam on food choice and could be used support the design of faith-informed dietary interventions among Muslim people. Further research is required to examine the role of faith-informed dietary intervention in the Muslim community.

A hybrid data fusion approach with twin CNN architecture for enhancing image source identification in IoT environment

AbstractWith the proliferation of digital devices in internet of things (IoT) environment featuring advanced visual capabilities, the task of Image Source Identification (ISI) has become increasingly vital for legal purposes, ensuring the verification of image authenticity and integrity, as well as identifying the device responsible for capturing the original scene. Over the past few decades, researchers have employed both traditional and machine‐learning methods to classify image sources. In the current landscape, data‐driven approaches leveraging deep learning models have emerged as powerful tools for achieving higher accuracy and precision in source prediction. The primary focus of this research is to address the complexities arising from diverse image sources and variable quality in IoT‐generated multimedia data. To achieve this, a Hybrid Data Fusion Approach is introduced, leveraging multiple sources of information to bolster the accuracy and robustness of ISI. This fusion methodology integrates diverse data streams from IoT devices, including metadata, sensor information, and contextual data, amalgamating them into a comprehensive data set for analysis. This study introduces an innovative approach to ISI through the implementation of a Twin Convolutional Neural Network Architecture (TCA) aimed at enhancing the efficacy of source classification. In TCA, the first CNN architecture, referred to as DnCNN, is employed to eliminate noise from the original data set, generating 256 × 256 patches for both training and testing. Subsequently, the second CNN architecture is employed to classify images based on features extracted from various convolutional layers using a 3 × 3 filter, thereby enhancing prediction efficiency. The proposed model demonstrates exceptional accuracy in effectively classifying image sources, showcasing its potential as a robust solution in the realm of ISI.

Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks

In the current era of social media, the proliferation of images sourced from unreliable origins underscores the pressing need for robust methods to detect forged content, particularly amidst the rapid evolution of image manipulation technologies. Existing literature delineates two primary approaches to image manipulation detection: active and passive. Active techniques intervene preemptively, embedding structures into images to facilitate subsequent authenticity verification, whereas passive methods analyze image content for traces of manipulation. This study presents a novel solution to image manipulation detection by leveraging a multi-stream neural network architecture. Our approach harnesses three convolutional neural networks (CNNs) operating on distinct data streams extracted from the original image. We have developed a solution based on two passive detection methodologies. The system utilizes two separate streams to extract specific data subsets, while a third stream processes the unaltered image. Each net independently processes its respective data stream, capturing diverse facets of the image. The outputs from these nets are then fused through concatenation to ascertain whether the image has undergone manipulation, yielding a comprehensive detection framework surpassing the efficacy of its constituent methods. Our work introduces a unique dataset derived from the fusion of four publicly available datasets, featuring organically manipulated images that closely resemble real-world scenarios. This dataset offers a more authentic representation than other state-of-the-art methods that use algorithmically generated datasets based on image patches. By encompassing genuine manipulation scenarios, our dataset enhances the model’s ability to generalize across varied manipulation techniques, thereby improving its performance in real-world settings. After training, the merged approach obtained an accuracy of 89.59% in the set of validation images, significantly higher than the model trained with only unaltered images, which obtained 78.64%, and the two other models trained using images with a feature selection method applied to enhance inconsistencies that obtained 68.02% for Error-Level Analysis images and 50.70% for the method using Discrete Wavelet Transform. Moreover, our proposed approach exhibits reduced accuracy variance compared to alternative models, underscoring its stability and robustness across diverse datasets. The approach outlined in this work needs to provide information about the specific location or type of tempering, which limits its practical applications.

Application of Multimedia Watermarking Technology in the Field of Information Security

This paper aims at comprehensively discussing the principle, methods, applications, challenges and future development of multimedia watermarking technology. In order to achieve this goal, this paper first introduces the basic concept and classification of multimedia watermarking technology, and then expounds in detail the core principles of spatial domain, frequency domain and mixed domain watermarking technology and the methods of watermark embedding and extraction. In terms of methods, this paper reveals their technical differences in robustness, invisibility and capacity through in-depth analysis of the characteristics of watermarking technology in different domains. In terms of application, the article shows the extensive application of multimedia watermarking technology in copyright protection, content authentication, covert communication and multimedia data integrity and authenticity verification through specific case analysis, highlighting its important value in the field of information security. Generally speaking, this paper systematically analyzes the principle, method and application of multimedia watermarking technology, which provides an important theoretical basis and practical guidance for further research and development in this field.

Lanthanide-Assisted Nanozyme Performs Optical and Magnetic Resonance Dual-Modality Logical Signal for In Vitro Diagnosis.

The iron nanozyme-based colorimetric method, which is widely applied for biosubstrate detection in in vitro diagnosis (IVD), faces some limitations. The optimal catalytic conditions of iron nanozymes necessitate a strong acidic environment, high temperature, and other restrictive factors; additionally, the colorimetric results are highly influenced by optical interferences. To address these challenges, iron nanozymes doped with various transition elements were efficiently prepared in this study, and notably, the manganese-modified one displayed a high catalytic activity owing to its electron transfer property. Furthermore, the introduction of lanthanide ions into the catalytic reactions, specifically the neodymium ion, significantly boosted the generation efficiency of hydroxyl radicals; importantly, this enhancement extended to a wide range of pH levels and temperatures, amplifying the detection signal. Moreover, the nanozyme's superparamagnetic characteristic was also employed to perform a logical optical and magnetic resonance dual-modality detection for substrates, effectively eliminating background optical interference and ensuring a reliable verification of the signal's authenticity. Based on this magnetic signal, the integration of natural glucose oxidase with the nanozyme resulted in a notable 61.5% increase in detection sensitivity, surpassing the capabilities of the traditional colorimetric approach. Consequently, the incorporation of lanthanide ions into the magnetic nanozyme enables the effective identification of physiological biomarkers through the dual-modality signal. This not only guarantees enhanced sensitivity but also demonstrates significant potential for future applications.

Online Sale of Pirated Books from a Legal and Moral Perspective

The increase in online book sales without permission and copyright violations in Indonesia poses a major threat to written works. This problem requires normative legal and moral studies to find solutions that complement previous research. This article aims to analyze and describe the legal regulations for buying and selling pirated books online and explain that the act of pirating books for sale is a moral crime. Normative legal research was used, which produced prescriptive conclusions. The data in this article consists of the Copyright Law, Information and Electronic Transactions Law, and other technical regulations. These regulations are equipped with various supporting references that discuss copyright issues, moral rights, and integrative legal theory, which describes a comprehensive legal system for responding to regulatory violations. This article reveals that national legal policies are not sufficient to guarantee protection for authors and publishers against book piracy and even make it difficult to report this crime to the police so that the use of the criminal justice system becomes ineffective. Indonesian society's perception of book piracy must be improved from previously being a common practice to a moral crime that is financially detrimental, and to the good name of authors and publishers. This article suggests several changes to the regulatory structure. First, we determine the authenticity verification scheme for books sold in bookstores and on marketplace platforms. Second, eliminating the concept of Klach Delict in criminal provisions so that reporting the crime of book piracy can be done by anyone, especially publishers and the public, who observe copyright.

Event-based data authenticity analytics for IoT and blockchain-enabled ESG disclosure

Environment, social, and governance (ESG) disclosure has raised significant interest in academia and industry for sustainable development and investing. However, the data authenticity of the ESG disclosure is still a serious matter of concern. This study proposes a novel solution to solve the above problem. First, an ESG information disclosure system (IBESG) enabled by IoT and blockchain is originally designed. The IBESG integrates IoT and blockchain technologies to facilitate the collection and transmission of data during ESG disclosure, and to ensures the data authenticity, consistency, and transparency. Second, the authors design a Local and Global Authenticity Verification Flow (LGA) consisting of edge computing and cloud computing to sufficiently verify the authenticity through the data flow. In addition, data authenticity analytics algorithms are developed in this study, which contains event-based spatial–temporal analytics and authenticity index computation. Finally, an experimental simulation is carried out to illustrate the implementation of the IBESG and the performance of the verification solution, and the sensitivity analysis of the above solution is conducted. Moreover, the relevant suggestions on deployment are given according to the findings during the experiment, and future work on improving the algorithm and conducting experiments in field manufacturing factories is illustrated. This study is expected to help academia and industry apply the solution in similar scenarios and inspire new ideas.

A Blockchain-Based Trustworthy Access Control Scheme for Medical Data Sharing

Blockchain is commonly employed in access control to provide safe medical data exchange because of the characteristics of decentralization, nontamperability, and traceability. Patients share personal health data by granting access rights to users or medical institutions. The major purpose of the existing access control techniques is to identify users who are permitted to access medical data. They hardly ever recognize internal assailants from legitimate entities. Medical data will involve multilayer access within the authorized organizations. Considering the cost of permissions management and the problem of insider malicious node attacks, users hope to implement authorization constraints within the authorized institutions. It can prevent their data from being maliciously disclosed by end-users from different authorized healthcare domains. For the purpose to achieve the fine-grained permissions propagation control of medical data in sharing institutions, a trust-based authorization access control mechanism is suggested in this study. Trust thresholds are assigned to different privileges based on their sensitivity and used to generate zero-knowledge proof to be broadcasted among blockchain nodes. This method evaluates the trust of each user through the dynamic trust calculation model. And meanwhile, smart contract is employed to verify whether the user’s trust can activate some permissions and ensure the privacy of the user’s trust in the process of authorization verification. In addition, the authorization transaction between users and institutions is recorded on the blockchain for patient traceability and accountability. The feasibility and effectiveness of the scheme are demonstrated through comprehensive comparisons and extensive experiments.

Nomenclatural standards and genetic certificates for apple-tree cultivars developed at the North Caucasian Federal Scientific Center of Horticulture, Viticulture, wine-making

Background. The development of genetic certificates and nomenclatural standards for newly released cultivars of Malus domestica Borkh. (apple tree) ensures an opportunity to preserve the original and authentic genetic and morphological information on domestic cultivars promising for the south of Russia. The purpose of this study was to develop genetic certificates and nomenclatural standards for 7 apple-tree cultivars developed at the North Caucasian Federal Scientific Center of Horticulture, Viticulture, Wine-making (NCFSCHVW), five of which were released jointly with the All-Russian Research Institute of Fruit Crop Breeding (VNIISPK).Materials and methods. The materials included apple-tree cultivars resistant or immune to scab (Venturia inaequalis (Cooke) G. Winter) from the NCFSCHVW collection stock. Conventional herbarization and DNA fingerprinting methods were used. For cultivar certification, an improved technique of DNA sampling and 12 microsatellite markers were applied.Results. Unique DNA profile certificates and nomenclatural standards were produced for 7 apple-tree cultivars: ‘Vasilisa’, ‘Karmen’, ‘Krasny Yantar’, ‘Soyuz’ and ‘Rassvet’ (with the Rvi6 gene) released jointly by the NCFSCHVW and VNIISPK, plus ‘Zolotoye Letneye’ and ‘Feya’ developed at the NCFSCHVW. The apple-tree herbarium specimens were placed for perpetual storage in the WIR Herbarium of the N.I. Vavilov All-Russian Institute of Plant Genetic Resources (VIR).Conclusion. The results obtained are promising for identification and authenticity verification of new domestic apple-tree cultivars during their reproduction and cultivation.

Supervision and early warning of abnormal data in Internet of Things based on unsupervised attention learning

With the continuous development of artificial intelligence and big data, more and more communication protocols have appeared in the network connection. Because the network protocol content specification should not need to obtain the content of the common protocol by any means, it faces different attacks, which increases the complexity of network security detection to an unlimited extent. The absolute security analysis of the content of the relevant data protocol is not too limited. Based on the content of the unknown binary number protocol in the Internet of Things, this paper does the reverse research. To address the inefficiency, learning is focused on the inverse aspect of the content processing logic of the protocol for unsupervised execution attention. This paper proposes a method based on the name of the Dirichlet process mixture model normal field and the unknown protocol of the Internet of Things. It also establishes the behavioral criteria that process the logical inference least squares method to infer the content of the target protocol. Based on the original external characteristics of abnormal data in the Internet of Things, the neural network model of attention mechanism and the model checking of variational self-encoder are chosen. An innovative multi-dimensional spatial neural network model with a centralized control mechanism is taken to extract the spatial relevant information from the original external features, which are regarded as the intermediate external features. The intermediate extrinsic features are input to a variational self-encoder for computation and reconstruction. The experimental results verify that the time required by the proposed model is 45% less than that of the traditional structure. The flow processing improves by 36.7% compared with the optimization of the parameter control function. The specific method only relies on the original external characteristics of the Internet of Things traffic and can achieve better data anomaly detection and classification under the condition of not significantly improving the detection time. The abnormal data detection scheme of the Internet of Things designed in this paper not only fills the detection gap of the current mainstream Internet of Things system which lacks an autonomous supervision mechanism, but also effectively blocks external network intrusion and further improves the management authority verification system. It plays an indispensable role in the long-term stable operation of the Internet of Things platform.

Assessing the Relative Volatility and Beta Radioactivity of Ethanol as a Preliminary Verification of Varietal Authenticity in Philippine Wines and Liquors with Tourism Implications

Alcoholic beverages in the Philippines show significant variation in ethanol content and feedstock, influencing their relative volatility, α, which serves as a strong indicator of the ease of ethanol separation during distillation. This variability arises from differences in the vapor-liquid pseudo-binary equilibrium of the beverages. By constructing a pseudo-binary ethanol-water plot, the ease of dehydrating ethanol was analyzed from a thermodynamic perspective. Using Dalton’s, Raoult’s, Clapeyron, and Trouton’s laws, the study derived the equation:

Film and Television Website Scores Authenticity Verification Based on the Emotional Analysis

Film and Television Website Scores Authenticity Verification Based on the Emotional Analysis

Web-Biometrics for User Authenticity Verification in Zero Trust Access Control

Web-Biometrics for User Authenticity Verification in Zero Trust Access Control