Authentication Applications Research Articles

Authentication Performance Analysis of Acoustic-Based Ear Biometrics System Using Audible Signals

A study of an acoustic-based ear biometrics system for individual identification using audible signals was undertaken. In this era of too much network connectivity, an increase in cybercrimes has led to the loss of key information or leakage of it. Experts have therefore continued to work hard to protect and secure important data from being stolen, lost, leaked, or tempered with. The use of biometric systems is one of the methods that many have adopted to provide the needed security and protection of data. The authors in this study proposed an acoustics-based ear biometric system for the identification of individuals while taking into consideration the dynamics of real-time data capture and using real persons for user authentication applications. The system has been developed in the MATLAB/SIMULINK language which supports dynamic real-time data capture. The results of simulation experiments showed that with proper experimentation and threshold calibration, it is possible to develop acoustics-based authentication systems that can identify individuals correctly and with 100% recognition accuracy. Depending on the human subjects under study, the threshold cosine similarity setting may vary between 0.2 and 0.4. However, this variation is offset by the enrollment procedure deployed in practice. Furthermore, trend analysis using moving average analysis revealed the possibility that a false acceptance is equally likely even though 100% recognition accuracy was attained.

Physical Unclonable Functions Employing Circularly Polarized Light Emission from Nematic Liquid Crystal Ordering Directed by Helical Nanofilaments.

This study proposes the use of physical unclonable functions employing circularly polarized light emission (CPLE) from nematic liquid crystal (NLC) ordering directed by helical nanofilaments in a mixed system composed of a calamitic NLC mixture and a bent-core molecule. To achieve this, an intrinsically nonemissive NLC is blended with a high concentration of a luminescent rod-like dye, which is miscible up to 10 wt % in the calamitic NLC without a significant decrease in the degree of alignment. The luminescence dissymmetry factor of CPLEs in the mixed system strongly depends on the degree of alignment of the dye-doped NLCs. Furthermore, the mixed system prepared in this study exhibits two randomly generated chiral domains with CPLEs of opposite signs. These chiral domains are characterized not only by their CPLE performances but also by their ability to generate random patterns up to several millimeters, making them promising candidates for high-performance secure authentication applications.

Utilization of Aloe vera-infused Sm3+ doped La2MoO6 nanophosphors: Their role in anti-counterfeiting, white LEDs and transport properties

In the realm of anti-counterfeiting (AC) measures, the focus has increasingly turned towards fluorescent inks, appreciated for their practicality and environmentally friendly nature. However, their susceptibility to wear and vulnerability to moisture damage has hindered their widespread application. Additionally, most available fluorescent inks are single-colored, toxic, and skin-irritating, making them ill-suited for real-world use. In light of these challenges, this article delves into the impact of introducing Sm3+ ions into the La2MoO6 host lattice. The green solution combustion method is employed to synthesis 1–11 mol % Sm3+ doped La2MoO6:Sm3+ nanophosphors (LMO:Sm3+ (1–11 mol %) NPs. The effects of rare earth doping are examined through X-ray diffraction, scanning electron microscopy-energy dispersive spectroscopy, and photoluminescence (PL) analysis. The PL spectra reveal four emission bands, with a prominent 603 nm wavelength band emitting an orange-red hue attributed to the electronic transition of Sm3+ ions. Notably, the impact of Sm3+ ion doping on the phosphor’s luminescence spectra is prominently featured. The optimal concentration is found to be 5 mol % (LMO:5Sm3+), with concentration quenching primarily attributed to exchange-type interactions. The luminescent properties are evidenced by Commission Internationale de l’éclairage (CIE) color coordinates (0.6063, 0.3901), color purity (CP = 99.10 %), and correlated color temperature (CCT = 1383 K). Furthermore, the study delves into optical parameters such as energy band gap and refractive index, revealing values of 2.313 and 2.252 eV, respectively, for the optimal molar concentration. These findings hold promise in the development of AC inks suitable for screen-printing and writing on a variety of surfaces. The resultant patterns exhibit vivid orange-red fluorescence under UV 365 nm irradiation, showcasing potential applications in encryption, security marking, and optical authentication.

Advances in Physalis molecular research: applications in authentication, genetic diversity, phylogenetics, functional genes, and omics.

The plants of the genus Physalis L. have been extensively utilized in traditional and indigenous Chinese medicinal practices for treating a variety of ailments, including dermatitis, malaria, asthma, hepatitis, and liver disorders. The present review aims to achieve a comprehensive and up-to-date investigation of the genus Physalis, a new model crop, to understand plant diversity and fruit development. Several chloroplast DNA-, nuclear ribosomal DNA-, and genomic DNA-based markers, such as psbA-trnH, internal-transcribed spacer (ITS), simple sequence repeat (SSR), random amplified microsatellites (RAMS), sequence-characterized amplified region (SCAR), and single nucleotide polymorphism (SNP), were developed for molecular identification, genetic diversity, and phylogenetic studies of Physalis species. A large number of functional genes involved in inflated calyx syndrome development (AP2-L, MPF2, MPF3, and MAGO), organ growth (AG1, AG2, POS1, and CNR1), and active ingredient metabolism (24ISO, DHCRT, P450-CPL, SR, DUF538, TAS14, and 3β-HSB) were identified contributing to the breeding of novel Physalis varieties. Various omic studies revealed and functionally identified a series of reproductive organ development-related factors, environmental stress-responsive genes, and active component biosynthesis-related enzymes. The chromosome-level genomes of Physalis floridana Rydb., Physalis grisea (Waterf.) M. Martínez, and Physalis pruinosa L. have been recently published providing a valuable resource for genome editing in Physalis crops. Our review summarizes the recent progress in genetic diversity, molecular identification, phylogenetics, functional genes, and the application of omics in the genus Physalis and accelerates efficient utilization of this traditional herb.

Lipidomics in forensic science: a comprehensive review of applications in drugs, alcohol, latent fingermarks, fire debris, and seafood authentication.

Forensic science, an interdisciplinary field encompassing the collection, examination, and presentation of evidence in legal proceedings, has recently embraced lipidomics as a valuable tool. Lipidomics, a subfield of metabolomics, specializes in the analysis of lipid structures and functions, offering insights into biological processes that can aid forensic investigations. While not a substitute for DNA analysis in personal identification, lipidomics complements this technique by focusing on small biological molecules, with distinct sample requirements. This review comprehensively explores the current applications of lipidomics in forensic science. The review commences with an introduction to the concept and historical background of lipidomics, subsequently delving into its utilization in diverse areas such as drug analysis, ethyl alcohol and substitute assessment, latent fingermark detection, fire debris analysis, and seafood authentication. By showcasing the various biological materials and methods employed, this review underscores the potential of lipidomics as a powerful adjunct in forensic investigations.

Laser-Induced Graphene RF Tags for Authentication Applications

Laser-Induced Graphene RF Tags for Authentication Applications

Enhanced Photoluminescence of Curcuma longa Extracts via Chitosan-Mediated Energy Transfer for Textile Authentication Applications.

Dyes for security markings play a pivotal role in safeguarding the integrity of products across various fields, such as textiles, pharmaceuticals, food, and manufacturing among others. However, most commercial dyes used as security markings are costly and may contain toxic and harmful substances that pose a risk to human health. Curcumin, a natural phenolic compound found in turmeric, possesses distinct photoluminescent properties alongside its vibrant yellow color, making it a potential candidate material for authentication applications. This study demonstrates a cost-effective and eco-friendly approach to develop enhanced photoluminescent emissions from curcumin dyes for textile authentication. Curcumin was extracted from C. longa using sonication-assisted-solvent extraction method. The extract was dip-coated and dyed into the textile substrates. Chitosan was introduced as a post-mordanting agent to stabilize the curcumin and as a co-sensitizer. Co-sensitization of curcumin with chitosan triggers energy transfer to enhance its luminescent intensity. The UV-visible absorption peak at 424 nm is associated with the characteristic absorption of curcumin. The photoluminescence measurements showed a broad emission peaking at 545 nm with significant enhancement attributed to the energy transfer induced by chitosan, thus showing great potential as a naturally derived photoluminescent dye for authentication applications.

Rapid identification of the geographical origin of Baimudan tea using a Multi-AdaBoost model integrated with Raman Spectroscopy

The potential of Multi-AdaBoost in spectral analysis is substantial, particularly when combined with weak classifiers and trained to develop into a robust classifier. Given the variable quality of Baimudan tea sourced from diverse regions, the novel application of Raman spectroscopy in conjunction with the Multi-AdaBoost model to analyze the geographic origin of Baimudan tea was introduced. Initially, Raman spectra of Baimudan tea from four distinct origins in Fujian province were gathered, namely Fuan (FA), Fuding (FD), Zhenghe (ZH), and Songxi (SX). Decision Tree (DT) and Support Vector Machine (SVM) models were employed as fitting classifiers to construct the Multi-AdaBoost-DT and Multi-AdaBoost-SVM models. The results demonstrated that the Multi-AdaBoost-DT model exhibited significantly improved recognition rates for FA, FD, ZH, and SX origin compared to the DT model, with the average recognition rate increasing from 86.46% to 91.67%. In contrast, the recognition rates for FA and SX origin in the Multi-AdaBoost-SVM model remained unchanged, attributed to the model having reached a local optimum. The recognition rates of FD origin increased from 91.67% to 95.83%, a significant improvement, while those of ZH origin escalated from 83.33% to 87.50%. The average recognition rate increased from 92.71% to 94.79%. Additionally, Multi-AdaBoost-SVM and Multi-AdaBoost-DT enhanced the sensitivity and specificity of the discrimination outcomes. These results corroborated the effectiveness of the proposed Multi-AdaBoost-SVM model in identifying the geographical origin of Baimudan tea. Moreover, the Multi-AdaBoost model demonstrates potential in elevating the discrimination accuracy of weak classifiers, which bodes well for its application in food authentication and quality control.

An exhaustive measurement of re-sampling detection in lossy compressed images using deep learning approach

The detection of resampling in digital images is critical for image authentication, but performance can be challenging when dealing with lossy compression. This study proposes an efficient feature extraction technique for detecting resampling (i.e., tampering) in post-JPEG compressed images. Our approach combines compression clues with resampling clues and feeds them to various traditional machine learning (ML) algorithms such as logistic regression, K-nearest neighbours (K-NN), support vector machine (SVM), decision tree (DT), and random forest (RF) to detect and classify doctored images in the re-compression scenario. We propose and evaluate feed-forward deep neural networks (DNN) and 1D convolutional neural networks (CNN) based on evaluation parameters such as accuracy, recall, precision, and F1 score, comparing them with the aforementioned traditional ML algorithms. Our results show that the RF and one-dimensional (1D) CNN are the most efficient models for this task. Furthermore, the 1D CNN outperforms the state-of-the-art techniques, particularly in the most challenging case of downscaling in lossy JPEG compressed images. Our proposed method demonstrates promising results for resampling detection in post-JPEG compressed images, which can be helpful in various image authentication applications.

Biometric Authentication Methods on Mobile Platforms

In this work, the authors propose a new biometric authentication system on mobile devices, enhancing security at these terminals and preserving user privacy. The proposed system uses a method of extracting strong features from minutiae with refinement of the method with regard to the further elimination of false minutiae by the calculation of geometric information (orientations and distances between minutiae) to obtain true terminations and stronger bifurcations facilitating the recognition of individuals. A series of tests carried out using a recognition and authentication application allowed us to achieve a false rejection rate of 13.81% and a false acceptance rate of almost zero (0.021%). The authors also propose a security model using hash functions and a random number to make the recognition system revocable, more difficult to compromise and thus reducing the risk of usurpation.

Robust biometric scheme against replay attacks using one-time biometric templates

User authentication is an important issue on the Internet and usually solved through static and often unique passwords. Another method is to use biometrics, but biometric data are sensitive and need to be protected. Protection schemes such as cancelable biometric template generation have appeared, but they are sensitive to replay attacks. In this paper, we propose an original method to generate one-time biometric templates for user authentication applications. This proposed scheme limits replay attacks, consisting of an attacker maliciously retransmitting an intercepted user's identity proof. Our method is generic: any biometric modality could be used, the identity verification is realized by the service/identity provider to be realistic. Biometric features are extracted from captures using deep learning and then protected with biohashing, a cancelable biometric scheme. Finally, a step consisting of cryptographic hashing and symmetric encryption guarantees the generation of a one-time, non-replayable template. We have tested our scheme on two common biometric databases, from faces and fingerprints, and the results confirm its efficiency and robustness to attacks given a rigorous threat model.

Implicit IoT authentication using on-phone ANN models and breathing data

The expansionist nature of the Internet of Things (IoT) with smart sensing has integrated smart technology into the fabric of daily life. From digital health check-ups and personal property access to financial transactions are conveniently performed with the help of smart technologies, such as smart wearables. Therefore, the security of these devices and the entire IoT-connected cyber–physical space accessible through smart wearables has become a burning question. While the current knowledge-based security approaches (e.g., pattern locks, passwords, or PINs) cause a recall burden to the user, conventional one-time biometric-based methods are not adaptable to the tiny wearables. Therefore, this work presents an implicit IoT authentication utilizing breathing patterns to verify a user through continuous microphone sensing. The core piece of this work is the Breathing data-driven TensorFlow Lite framework-supported on-phone authentication application (i.e., BTL Auth app), which consists of an audio processing pipeline (to perform filtering and feature computation) and an artificial neural network model to verify the target user. From our detailed analysis of sequential and non-sequential neural networks trained with static and dynamic audio features, we can achieve an average accuracy of up to .93 when authenticating a user from breathing patterns. Finally, the BTL Auth app needs around 64 KB of memory and 5 s to verify the user.

Discovery of porcine proteins-binding DNA aptamer through SELEX and proteomics for pork authentication

The authentication of meat products has become a global consumer concern in the food industry. Traditional methods such as PCR and mass spectrometry can identify and differentiate the source of meat, but they are time-consuming and require high-quality extracted DNA or protein for testing. As an alternative, aptamer-based detection tools have been introduced, but their application in food authentication is still new. To date, there is a lack of data on the development of a porcine-specific aptamer that is specifically bound to a heat-stable protein. Hence, this study was conducted to screen, characterize and validate aptamers bound to any pork protein through SELEX process, combined with Next Generation Sequencing (NGS) and Liquid Chromatography Mass Spectrometry (LC-MS) analysis. The putative porcine-specific aptamers were selected after fourteen rounds of selection using centrifugal-ultrafiltration separation technique against five negative controls. The binding affinity test revealed that APT#A1 had the highest binding affinity with a dissociation constant of 27.61 ± 1.92 nM. However, the protein blotting results showed that the selected porcine-bound aptamers were not specific and could also bind to multiple proteins from negative samples. LC-MS analysis showed that the aptamers bound to troponin and tropomyosin subunits, and these proteins have potential as target markers for future authentication studies. Future research can focus on developing aptamers with higher specificity towards porcine protein and validating their feasibility as a practical tool for food authentication in real meat-based food samples.

Semi-Fragile Reversible Watermarking for 3D Models Using Spherical Crown Volume Division

Aiming at the large distortion and low tampering localization accuracy of the existing semi-fragile reversible watermarking for 3D mesh models, a novel semi-fragile reversible watermarking for 3D models using spherical crown volume division is proposed. The crown volume of a sphere is divided to reduce the embedding distortion. The possible geometric and topological transformations are separately considered in the watermark generation, and the vertices of the one-ring neighbourhood are grouped to improve the tampering localization accuracy. Experimental results show that the proposed scheme can achieve better localization accuracy and lower embedding distortion than some state-of-the-art algorithms. It has good potential for the applications in integrity authentication for 3D mesh models.

Multiple-Channel Information Encryption Based on Quantum Dot Absorption Spectra.

Large-capacity information encryption has attracted significant interest in the information age. The diversity and controllability of spectra have positioned them to be widely applied for information encryption. Current spectra-based information encryption methods commonly rely on either spectral alteration induced by external stimuli or the utilization of narrowband channels within spectra. However, these methods encounter a common challenge in attaining both high security and large capacity simultaneously. To address these issues, we propose a multiple-channel information encryption system based on quantum dot (QD) absorption spectra. The diversity of QD absorption spectra and their broadband features ensure that the encrypted spectra can hardly be decrypted without knowing the correct channel matrix. Meanwhile, the large capacity is realized through the combination of multiple QD spectral channels with a theoretical maximum capacity of 24.0 bits in a single spectrum. In order to optimize the performance of our proposed system, the selection principle of the channel matrix is established to achieve the rapid identification of the optimal channel matrix in several milliseconds. The additivity of QD spectral channels and the consistency of QD spectra are also explored to minimize the impact of errors on information decryption. Furthermore, two spectral encryption scenarios of spatial pattern and spectral pattern are applied to demonstrate the feasibility, showcasing their ability to achieve both a high level of security and large capacity. Owing to the advantages offered by QD spectra, the QD spectra-based information system exhibits excellent potential for broader applications in information storage, authentication, and computing.

Quantum authentication method based on key-controlled maximally mixed quantum state encryption

Quantum authentication is a fundamental first step that ensures secure quantum communication. Although various quantum authentication methods have been proposed recently, their implementation efficiency is limited. This paper proposes a key-controlled maximally mixed quantum state encryption (MMQSE) method using only a single qubit, unitary operation, minimized quantum transmissions, and a single qubit measurement, which improves implementation feasibility and operation efficiency. We applied it to representative quantum authentication applications, namely, quantum identity and message authentication. The security of our authentication schemes was verified by analyzing the relationship between the integral ratio of Uhlmann’s fidelity and probability of successful eavesdropping. Moreover, we demonstrate the higher authentication efficiency of the proposed scheme in a real quantum-channel noise environment. The upper bound of the valid noise rate was quantified using the integral ratio of Uhlmann’s fidelity in a noise environment. Finally, the optimal number of authentication sequences was estimated.

CBDC-PUF: A Novel Physical Unclonable Function Design Framework Utilizing Configurable Butterfly Delay Chain Against Modeling Attack

Physical unclonable function (PUF) is a promising security-based primitive, which provides an extremely large number of responses for key generation and authentication applications. Various PUFs have been developed as central building blocks in cryptographic protocols and security architectures, however, the existing PUFs and their improvements are still vulnerable to modeling attacks (MA) with refined machine learning algorithms. In this article, a configurable butterfly delay chain-based PUF design framework is proposed to meet the requirements of randomness, reliability, uniqueness, and MA-resistance metrics. A configurable butterfly delay chain is introduced to create multiple pairs of symmetric paths and a strong PUF relying on the intrinsic delay fluctuations of two identical paths is built. Furthermore, a secure hash function is used to insert non-linearities into the PUF, and a BCH-based error correction algorithm is utilized to recover the actual responses under noisy environments. The proposed PUF is implemented on Xilinx FPGAs and three machine learning algorithms are used to evaluate the resistance against MA. Experimental results show that the randomness, reliability, and uniqueness of the proposed PUF are close to the ideal value (49.6%, 99.9%, and 49.9%, respectively), and the prediction accuracy reaches 50% that indicating a desirable resilient to MA.

Hybrid Raman and Laser-Induced Breakdown Spectroscopy for Food Authentication Applications.

The issue of food fraud has become a significant global concern as it affects both the quality and safety of food products, ultimately resulting in the loss of customer trust and brand loyalty. To address this problem, we have developed an innovative approach that can tackle various types of food fraud, including adulteration, substitution, and dilution. Our methodology utilizes an integrated system that combines laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. Although both techniques emerged as valuable tools for food analysis, they have until now been used separately, and their combined potential in food fraud has not been thoroughly tested. The aim of our study was to demonstrate the potential benefits of integrating Raman and LIBS modalities in a portable system for improved product classification and subsequent authentication. In pursuit of this objective, we designed and tested a compact, hybrid Raman/LIBS system, which exhibited distinct advantages over the individual modalities. Our findings illustrate that the combination of these two modalities can achieve higher accuracy in product classification, leading to more effective and reliable product authentication. Overall, our research highlights the potential of hybrid systems for practical applications in a variety of industries. The integration and design were mainly focused on the detection and characterization of both elemental and molecular elements in various food products. Two different sets of solid food samples (sixteen Alpine-style cheeses and seven brands of Arabica coffee beans) were chosen for the authentication analysis. Class detection and classification were accomplished through the use of multivariate feature selection and machine-learning procedures. The accuracy of classification was observed to improve by approximately 10% when utilizing the hybrid Raman/LIBS spectra, as opposed to the analysis of spectra from the individual methods. This clearly demonstrates that the hybrid system can significantly improve food authentication accuracy while maintaining the portability of the combined system. Thus, the successful implementation of a hybrid Raman-LIBS technique is expected to contribute to the development of novel portable devices for food authentication in food as well as other various industries.

A Survey on Fingerprinting Technologies for Smartphones Based on Embedded Transducers

Smartphones are a vital technology, they improve our social interactions, provide us a great deal of information and bring forth the means to control various emerging technologies, like the numerous IoT devices that are controlled via smartphone apps. In this context, smartphone fingerprinting from sensor characteristics is a topic of high interest not only due to privacy implications or potential use in forensics investigations, but also because of various applications in device authentication. In this work we review existing approaches for smartphone fingerprinting based on internal components, focusing mostly on camera sensors, microphones, loudspeakers and accelerometers. Other sensors, i.e., gyroscopes and magnetometers, are also accounted, but they correspond to a smaller body of works. The output of these transducers, which convert one type of energy into another, e.g., mechanical into electrical, leaks through various channels such as mobile apps and cloud services, while there is little user awareness on the privacy risks. Needless to say, miniature physical imperfections from the manufacturing process make each such transducer unique. One of the main intentions of our study is to rank these sensors according to the accuracy they provide in identifying smartphones and to give a clear overview on the amount of research that each of these components triggered so far. We review the features which can be extracted from each type of data and the classification algorithms that have been used. Last but not least, we also point out publicly available datasets which can serve for future investigations.

Investigation of 26 Sterols in Commercial Goji Berries (Lycium Species) and the Application in Food Authentication by Gas Chromatography‐Mass Spectrometry

Investigation of 26 Sterols in Commercial Goji Berries <i>(Lycium</i> Species) and the Application in Food Authentication by Gas Chromatography‐Mass Spectrometry