Authentication Applications Research Articles

Genetic Authentication of the Medicinal Plant Portulaca oleracea Using a Quick, Precise, and Sensitive Isothermal DNA Amplification Assay

Portulaca oleracea (PO) is a commonly known medicinal crop that is an important ingredient for traditional Chinese medicine (TCM) due to its use as a vegetable in the diet. PO has been recorded to be frequently adulterated by other related species in the market of herbal plants, distorting the PO plant identity. Thus, identification of the botanical origin of PO is a crucial step before pharmaceutical or functional food application. In this research, a quick assay named “loop-mediated isothermal amplification (LAMP)” was built for the specific and sensitive authentication of PO DNA. On the basis of the divergences in the internal transcribed spacer 2 (ITS2) sequence between PO and its adulterant species, the LAMP primers were designed and verified their specificity, sensitivity, and application for the PO DNA authentication. The detection limit of the LAMP assay for PO DNA identification specifically was 100 fg under isothermal conditions at 63 °C for 30 min. In addition, different heat-processed PO samples can be applied for use in PO authentication in the LAMP assay. These samples of PO were more susceptible to the effect of steaming in authentication by PCR than boiling and drying treatment. Furthermore, commercial PO samples pursued from herbal markets were used to display their applicability of the developed LAMP analysis for PO postharvest authentication, and the investigation found that approximately 68.4% of PO specimens in the marketplace of herbal remedies were adulterated. In summary, the specific, sensitive, and rapid LAMP assay for PO authentication was first successfully developed herein, and its practical application for the inspection of adulteration in PO samples from the herbal market was shown. This LAMP assay created in this study will be useful to authenticate the botanical origin of PO and its commercial products.

Photoluminescent Cellulose Nanofibers-Reinforced Alginate Hydrogel with Color-Tunable and Self-Healing Properties for Authentication Applications

Photoluminescent Cellulose Nanofibers-Reinforced Alginate Hydrogel with Color-Tunable and Self-Healing Properties for Authentication Applications

Handheld Near-Infrared Spectroscopy: State-of-the-Art Instrumentation and Applications in Material Identification, Food Authentication, and Environmental Investigations

This present review article considers the rapid development of miniaturized handheld near-infrared spectrometers over the last decade and provides an overview of current instrumental developments and exemplary applications in the fields of material and food control as well as environmentally relevant investigations. Care is taken, however, not to fall into the exaggerated and sometimes unrealistic narrative of some direct-to-consumer companies, which has raised unrealistic expectations with full-bodied promises but has harmed the very valuable technology of NIR spectroscopy, rather than promoting its further development. Special attention will also be paid to possible applications that will allow a clientele that is not necessarily scientifically trained to solve quality control and authentication problems with this technology in everyday life.

Implementation and Security Test of Zero-Knowledge Protocols on SSI Blockchain

The problem of digital identity acquires more relevance every day in the eyes of a society that spends more and more time connected to the Internet. It has evolved throughout its history to reach a decentralized model known as Self-Sovereign Identity (SSI), which finds its natural tools in the blockchain technology and Zero-Knowledge Proofs (ZKPs). ZKPs, in this context, allow users to prove that their credentials are legitimate without revealing more information than is strictly necessary, and constitute one of the most promising areas of applied cryptography. In this work, an application is developed for the study of Zero-Knowledge Proof methods and, specifically, in their application for authentication in public-private key encryption systems. It focuses on the study of three ZKP protocols (Feige-Fiat-Shamir, Guillou-Quisquater, and Schnorr, which rely on the problems of large number factorizations and discrete logarithms for security) in the practical use-case where a prover wants to demonstrate knowledge of a private key for a public key without revealing the key itself. The application allows the user to modify the necessary parameters in each method to achieve a better understanding of their role in their safety and efficiency. Several types of attacks are carried out against the above-mentioned protocols to analyze their degree of security and what recommendations can be made to improve it.

Exploring the scores: Procrustes analysis for comprehensive exploration of multivariate data

Exploratory data analysis (EDA) through the projection of multivariate data into spaces of low dimensionality using methods such as principal components analysis (PCA) are at the core of chemometric applications in many fields, including metabolomics, biomarker discovery, food authentication, and many others. In addition to revealing underlying class structures in the data, unsupervised EDA methods have become a de facto method of confirmatory analysis for object classification (e.g., by health status, provenance), especially for small sample sizes, because they are not plagued with problems of overfitting that often accompany supervised methods. However, the characteristics of the scores plots for EDA projection methods are often highly dependent on data analysis options chosen, such as the type of preprocessing used, the projection method employed, the variables selected and (in the case of multiblock data) how the data are combined. The combinations of these parameters can lead to hundreds of different scores plots that need to be manually assessed for results that are interesting to the researcher. The present work is intended to expedite this process through a relational analysis of multiple results using Procrustes analysis to compare projections and applying hierarchical clustering to summarize the results in the form of a dendrogram. The software developed, ScorXplor, allows projections to be quickly assessed for their similarity and quality, with interactive plotting of scores plots for visual evaluation. Moreover, the approach provides a better understanding of the role and relationships among the various analysis options (preprocessing, analysis tools, etc.). The method is demonstrated using multiblock spectral data (UV–visible, near-infrared, mid-infrared) for flavored olive oils from different regions of Italy, implementing different preprocessing and fusion options, and applying PCA and maximum likelihood PCA as projection methods.

All‐Printed Electrochromic Stickers

AbstractDisplays are one of the most mature technologies in the field of printed electronics. Their ability to be manufactured in large quantities and at low cost has led to their recent uptake into the consumer market. Within this article this technology is extended to electrochromic display stickers. This is achieved using a recent reverse display architecture screen printed on textile and paper sticker substrates. The electrochromic stickers are comparable to plastic control substrates and show little performance difference even when adhered to curved surfaces. The electrochromic display technology is extended to sticker labels for authentication applications by patterning either the dielectric or the graphical layer. A proof‐of‐concept prototype emulating a wax seal on an envelope is presented to show that other colors can be implemented in this technology.

An investigation of IRIS Recognition Techniques: A Literature Survey

Iris is a colored muscle located within the eye that helps to regulate the amount of light that reaches the eye. It has some specific textural details that is not easily altered or manipulated, making it a characteristic that is ideally suited to biometric systems. Iris has been considered in the field of biometric applications for authentication purpose. The main purpose of this study is, due to the stability and unique feature available in Iris region. Iris patterns play a major role in many possible identification or authentication applications due to their uniqueness, durability and stability, universality. Iris recognition techniques have evolved tremendously in biometric identification and authentication systems over the past two decades, since their evolution. The technique of Iris recognition is quantifiable, robust, and highly accurate so that it achieves the fundamental tenant of ideal biometric technology. Purpose of this paper is to include a timeline analysis of different iris recognition techniques and the development of a novel algorithm for the iris recognition system. This paper also discusses various methods used in performing the iris recognition measures involved.

Application for Student Document Authentication

AICTE every year go through over 6 lakhs documents of students and verify them and approved all verified documents. AICTE manually finishes all work like verification and approved them one by one. And now a days the task become very critical and lengthy because the number of increasing documents and manual work without using any modern technical method. Schemes, training, VC, fellowships are increasing due to enrolment of many students every year. AICTE using some technology for forwarding verified applications but all work not become modern and many tasks are not possible without technology. Many research are done on the verification technology before but the verification system shows various security problems, result percentage of verification method by analysing all these technical issues and the recent technology the verification system application is studied.

Perancangan UI/UX pada Aplikasi Taspen Otentikasi Berbasis Mobile Dengan Menggunakan Metode Design Thinking

Along with the development of today's technology, the role of information system services becomes very important in overcoming various kinds of community problems effectively and efficiently. Taspen Authentication is an example of an information system service aimed at retired civil servants (PNS). However, there are still problems with the taspen authentication application that make it difficult for some users to run it. The majority of users of this application are the elderly group aged ≥ 60 years and have limited physical function and cognitive function. Based on the results of initial observations and interviews of researchers involving Taspen and several users in the elderly category, it was found that there were problems that caused the lack of efficiency in using the taspen authentication application in terms of UI/UX. The purpose of this research is to design a UI/UX that suits the needs of users, especially the elderly, using the design thinking method. In addition to using the design thinking method, researchers also use User Experience Questionnaire (UEQ) at the testing stage to determine the feasibility of the application prototype that has been designed and get feedback from the target user. The results of the study show that the new interface design provides a very good user experience as evidenced by the results of the 6 UEQ scales which are of excellent value. So the researcher concludes that the UI/UX design on the taspen authentication application that uses the design thinking method is able to provide a description of user needs, especially for the elderly.

Deep Learning and Limited Boltzmann Machines for Speaker Recognition

Speaker recognition has become an essential aspect of modern voice-based systems such as security and authentication applications. In this research, we propose a new method for speaker recognition based on deep learning and limited Boltzmann machines. The method comprises preemphasis and overlapping type framing, endpoint detection, feature extraction, and training of a depth belief network pattern using a limited Boltzmann machine layer. The Softmax graders are added in the top layer of the pattern, and the speaker's phonetic feature is input into the pattern for training. The likelihood probability of other speakers' phonetic features is calculated, and the speaker corresponding to the maximum probability is identified as the recognized result. The results show that the proposed method outperforms other state-of-the-art methods, achieving high accuracy and robustness to noise and signal variations.

Heteroleptic Ir(III)-based near-infrared organic light-emitting diodes with high radiance capacity

Near-infrared organic light-emitting diodes (NIR OLEDs) with heavy metals are regularly reported due to the advantages of their various applications in healthcare services, veil authentication, and night vision displays. For commercial applications, it is necessary to look at radiance capacity (RC) instead of radiance because of power consumption. However, recent papers still reported only simple high radiance performance and do not look at device from the point of view of RC. To overcome this hurdle, we designed Ir(III)-based heteroleptic NIR materials with two types of auxiliary ligand. The proposed emitters achieve a highly oriented horizontal dipole ratio (Ir(mCPDTiq)2tmd, complex 1: 80%, Ir(mCPDTiq)2acac, complex 2: 81%) with a short radiative lifetime (1: 386 ns, 2: 323 ns). The device also shows an extremely low turn-on voltage (Von) of 2.2 V and a high RC of 720 mW/sr/m2/V. The results on the Von and RC of the device is demonstrated an outstanding performance among the Ir(III)-based NIR OLEDs with a similar emission peak.

Laser-machined thin copper films on silicon as physical unclonable functions.

Physical unclonable functions (PUFs) are receiving significant attention with the rise of cryptography and the drive towards creating unique structures for security applications and anti-counterfeiting. Specifically, nanoparticle based PUFs can produce a high degree of randomness through their size, shape, spatial distribution, chemistry, and optical properties, rendering them very difficult to replicate. However, nanoparticle PUFs typically rely on complex preparation procedures involving chemical synthesis in solution, therefore requiring dispersion, and embedding within a host medium for application. We propose laser machining of surfaces as a one-step process for the creation of complex nanoparticle based PUFs by machining 600 nm thick copper films on a silicon substrate to yield a complex spatial and chemical distribution of redeposited copper, silicon, and oxide species. The approaches and material system investigated have potential applications in silicon chip authentication.

Deep learning-based optical authentication using the structural coloration of metals with femtosecond laser-induced periodic surface structures.

Structurally colored materials present potential technological applications including anticounterfeiting tags for authentication due to the ability to controllably manipulate colors through nanostructuring. Yet, no applications of deep learning algorithms, known to discover meaningful structures in data with far-reaching optimization capabilities, to such optical authentication applications involving low-spatial-frequency laser-induced periodic surface structures (LSFLs) have been demonstrated to date. In this work, by fine-tuning one of the lightweight convolutional neural networks, MobileNetV1, we investigate the optical authentication capabilities of the structurally colorized images on metal surfaces fabricated by controlling the orientation of femtosecond LSFLs. We show that the structural color variations due to a broad range of the illumination incident angles combined with both the controlled orientations of LSFLs and differences in features captured in the image make this system suitable for deep learning-based optical authentication.

Comparison of DNA extraction methods on CITES-listed timber species and application in species authentication of commercial products using DNA barcoding

Quality and quantity of DNA extracted from wood is important for molecular identification of wood species, which can serve for conservation of wood species and law enforcement to combat illegal wood trading. Rosewood (Dalbergia and Pterocarpus) and agarwood (Aquilaria) are the most commonly found hardwood in timber seizure incidents. To monitor international trade of timber and commercial wood products and to protect these endangered wood species from further population decline, in this study, we have compared three extraction protocols for DNA extraction from 12 samples of rosewood and agarwood timber logs, and later applied the best DNA extraction protocol on 10 commercial wood products claimed to be rosewood and agarwood. We also demonstrated the applicability of DNA mini-barcoding with multi-loci combination with reference library for identifying the species of timber and commercial wood products. We found that a silica column-based method with guanidine thiocyanate-containing binding buffer served the best in DNA extraction from different parts of wood in all three genera with good quality and quantity. Single barcode region ITS2 or multi-loci combinations including ITS2 barcode region generally provide better discriminatory power for species identification for both rosewood and agarwood. All 10 products were identified to species-level using multi-loci combination. In terms of accuracy in labelling, 80% of them were labelled correctly. Our work has shown the feasibility of extracting good quality of DNA from authentic wood samples and processed wood products and identifying them to species level based on DNA barcoding technology.

Two-level QR code scheme based on region matrix image secret sharing algorithm.

Quick response (QR) codes have become increasingly popular as a medium for quickly and easily accessing information through mobile devices. However, the open-source nature of QR code encoding poses a risk of information leakage and potential attacks, especially with the growing use of QR codes in financial services and authentication applications. To mitigate the risk of information leakage due to open-source QR code encoding, this paper proposes a two-level QR code scheme based on a region matrix image secret sharing algorithm. In this scheme, the first-level public information can be directly obtained by scanning with any standard QR code scanner, while the two-level secret information can only be accessed by overlaying the shared images. To enhance the robustness of joint secret information recovery using shared images, this article designs a progressive image secret sharing algorithm based on region matrices. This algorithm meticulously processes high-priority share regions and generates multiple substitute shares. In the event of attacks on key shares, substitute shares can be employed to recover the secret information. For an increased payload capacity of each QR code, an adaptive pixel depth adjustment algorithm is devised. This algorithm ensures that the recovery of two-level secret information maintains high clarity, while not affecting the scanning functionality of each shared QR code. Experimental results validate the feasibility of this scheme, which simplifies the construction matrix, reduces matrix redundancy, and exhibits priority partitioning and higher robustness. Furthermore, QR codes embedding secret shares can safeguard the two-level information, and the recovered images exhibit exceptional clarity.

A review of multi-factor authentication in the Internet of Healthcare Things.

This review paper aims to evaluate existing solutions in healthcare authentication and provides an insight into the technologies incorporated in Internet of Healthcare Things (IoHT) and multi-factor authentication (MFA) applications for next-generation authentication practices. Our review has two objectives: (a) Review MFA based on the challenges, impact and solutions discussed in the literature; and (b) define the security requirements of the IoHT as an approach to adapting MFA solutions in a healthcare context. To review the existing literature, we indexed articles from the IEEE Xplore, ACM Digital Library, ScienceDirect, and SpringerLink databases. The search was refined to combinations of 'authentication', 'multi-factor authentication', 'Internet of Things authentication', and 'medical authentication' to ensure that the retrieved journal articles and conference papers were relevant to healthcare and Internet of Things-oriented authentication research. The concepts of MFA can be applied to healthcare where security can often be overlooked. The security requirements identified result in stronger methodologies of authentication such as hardware solutions in combination with biometric data to enhance MFA approaches. We identify the key vulnerabilities of weaker approaches to security such as password use against various cyber threats. Cyber threats and MFA solutions are categorised in this paper to facilitate readers' understanding of them in healthcare domains. We contribute to an understanding of up-to-date MFA approaches and how they can be improved for use in the IoHT. This is achieved by discussing the challenges, benefits, and limitations of current methodologies and recommendations to improve access to eHealth resources through additional layers of security.

Behavior Privacy Preserving in RF Sensing

Recent years have witnessed the booming development of RF sensing, which supports both identity authentication and behavior recognition by analysing the signal distortion caused by human body. In particular, RF-based identity authentication is more attractive to researchers, because it can capture the unique biological characteristics of users. However, the openness of wireless transmission raises privacy concerns since human behaviors could expose massive private information of users, which impedes the real-world implementation of RF-based user authentication applications. It is difficult to filter out the behavior information from the collected RF signals. In this article, we propose a privacy-preserving deep neural network named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BPCloak</i> to erase the behavior information in RF signals while retaining the ability of user authentication. We conduct extensive experiments over mainstream RF signals collected from three real wireless systems, including the WiFi, radio frequency identification (RFID), and millimeter-wave (mmWave) systems. The experimental results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BPCloak</i> significantly reduces the behavior recognition accuracy, i.e., 85%+, 75%+, and 65%+ reduction for WiFi, RFID, and mmWave systems respectively, merely with a slight penalty of accuracy decrease when using these three systems for user authentication, i.e., 1%-, 3%-, and 5%-, respectively.

Digital transformation for improving sustainable value of products and services from agri-food systems

Value is routinely concentrated at the final links of food production chains as a consequence of market failures or asymmetries that distribute wealth unevenly in agri-food supply systems. Otherwise, for products with a geographic origin, the share can be more equitably distributed by adding environmental, sociocultural or technological values to products associated with sustainable models. Protecting a geographic indication (GI) of value-added products requires complex collective rules of sui generis systems, certified trademarks or business practices, including the approval of protocols or even unfair protection suitlaws. These rules are created on multilevel legislations gathering intrinsic cultural, historical and economic features. As a result, GI schemes are typically costly thus preventing access of general smallholders in product-valued chains. Digital technologies like mobile applications have a promising role in minimizing these limitations along food production chains, from pre-production to production and post-production. The pervasive spread of mobile devices with useful built-in sensors can be therefore a major consequence of the digital transformation in agriculture by means of intuitive applications combined with high-level technologies such as cloud/edge computing and Application Programming Interfaces (APIs). In this article, we present a new perspective on the digital transformation of the agri-food sector that may fasten smallholders' inclusion and access in market ecosystems of value-added products with GI. Such perspective demands the understanding of stakeholders networks for customizing mobile applications for digital authentication of product GI. The approach can foster new compliance schemes as those embedded in Environmental, Social and Governance (ESG) market initiatives.

Imaged Based Authentication System dengan Reduced Difference Expansion

Authentication is an important step in a system. Text-based authentication is an authentication alternative that is currently widely used. However, text-based authentication has the disadvantage of being ineffective when users have multiple accounts. This weakness can be overcome using image-based authentication. The implementation of image-based authentication can use the Reduced Difference Expansion (RDE) steganography method. application of image-based authentication, utilizing images as authentication media. RDE utilizes the reduced pixel difference as a place to insert credential data. Testing the image in this study, the image has an average similarity of 40 dB, indicating that the image that is inserted with credentials and that is not inserted has a high similarity. The similarity increases the security of the image from theft. The average character capacity that can be accommodated is 21,451 characters, a large enough capacity to accommodate credential data. The image is very sensitive to changes, so there is a slight change in the image, causing the image to not be used for authentication

A Semi-Fragile Reversible Watermarking for Authenticating 3D Models in Dual Domains Based on Variable Direction Double Modulation

Aiming at reducing the large distortion of the existing reversible watermarking for three-dimensional(3D) mesh models and meeting the needs of authentication in cloud-based data storage, a semi-fragile reversible watermarking for authenticating 3D models in dual domains is proposed based on variable direction double modulation. A 3D model is first transformed into the spherical coordinate system, and then the direction of quantization modulation is changed under specific circumstances to achieve less distortion. Watermarks are determined by the relative position and vertices number in the one-ring neighbourhood of all vertices, so the watermark generation is independent of the coordinate system and robust to RST(rotation, scaling, and translation). In addition, the watermarks are generated and embedded in both plaintext domain and encrypted domain, and the authentication can be accomplished in both domains. Experimental results and analysis show that the proposed scheme can effectively detect malicious tampering in two domains and reduce the distortion caused by watermark embedding. It has potential application in the content authentication of 3D models in cloud-based data storage.