Search Results (6,116)

Anonymous credential (AC) systems are privacy-preserving authentication mechanisms that allow users to prove that they have valid credentials anonymously. These systems provide a powerful tool for several practical applications, such as anonymous payment systems in e-commerce, preserving robust privacy protection for users. Most existing AC systems are constructed using traditional number-theoretic approaches, making them insecure under quantum attacks. With four decades of research in anonymous credential systems, there is a need for a comprehensive review that identifies the design structures of AC systems, organizes the research trends, and highlights unaddressed gaps for the future development of AC, especially bringing AC to post-quantum cryptography. This work is a complete study describing AC systems, as well as their architecture, components, security, and performance. Additionally, real-world implementations of various applications are identified, analyzed, and compared according to the design structure. Lastly, the challenges hindering the shift toward the quantumly secure lattice-based AC designs are discussed. Full article

The increasing trend in the popularity of argan oil (AGO), a multi-beneficial health and cosmetic product, can leave it prone to adulteration. The overall goal of this study was to utilize an attenuated total reflectance Fourier-transform infrared spectroscopic and chemometric methods, including partial least squares (PLS), principal component regression (PCR), and artificial neural network (ANN) for the authentication of AGO in the presence of other oil adulterants, avocado oil (AVO), pumpkin seed oil (PSO), flaxseed oil (FSO), and walnut seed oil (WNO). All three chemometrics methods were able to effectively quantify the FSO adulterant concentration across all statistical models, with the most optimal results in the ANN model as applied in the testing set data (RMSEP = 1.454 %v/v, R² = 0.821). Comparable results were also obtained for PLS (RMSEP = 1.727 %v/v, R² = 0.807) and PCR (RMSEP = 1.731 %v/v, R² = 0.846). Full article

There is no doubt that leadership is one of the most researched and disseminated topics in recent years, and over time, some distinguished models have developed a solid foundation and a reputable structure. From this perspective, this study analyzes the evidence of validity and reliability of a scale that assesses leadership styles in non-profit institutions. The study had an instrumental design. The sample consisted of 272 workers from nine Latin American countries, aged between 19 and 68 years (M = 34.08 and SD = 8.61), recruited through non-probabilistic sampling. A validity and reliability analysis of the scale confirmed the nine items and three original factors (servant, empowering and shared leadership). The KMO test reached a high level (0.898 > 0.70), and the Bartlett test reached a highly significant level (Sig. = 0.000). The scale also showed good internal consistency (α = 0.918 to 0.956; CR = 0.918 to 0.957; AVE = 0.755 to 0.880). Likewise, for the Confirmatory Factor Analysis, a measurement adjustment was performed, obtaining excellent and acceptable fit indices for Model 2 (CMIN/DF = 1.794; CFI = 0.993; SRMR = 0.023; RMSEA = 0.054; Pclose = 0.369). This study provides a brief and useful tool to measure leadership styles in Latin America, as a scale used specifically for this context would allow for a more accurate and valid assessment. This is crucial for generating effective organizational interventions, fostering the development of authentic leaders, and improving the competitiveness of non-profit institutions. Full article

Face images hold exceptional significance in contemporary society, serving as direct identifiers due to their rich personal attributes, enhancing daily life and work efficiency. However, advancements in deep learning and image processing have led to the proliferation of sophisticated face forgery software, rendering detection increasingly challenging. We propose a novel face tampering detection method utilizing a spatial attention-enhanced bidirectional convolutional neural network to address this. This approach synergizes the strengths of dense convolutional and depthwise separable networks for superior image feature extraction, thereby improving the accuracy of authentic and manipulated face detection. Furthermore, the network is trained to initially localize tampered regions within face images by integrating a spatial channel-based attention module as supervisory input. On three widely used public face forgery datasets, our method achieves an AUC of no less than 96.45%. The experimental results validate the effectiveness of our method in accurately detecting and initially localizing face tampering. Full article

Estimating the post mortem interval (PMI) is a crucial and contentious issue in forensic research, particularly in criminal cases. Traditional methods for PMI estimation are limited by constraints and inaccuracies. Circular RNA (circRNA), formed through exon or intron looping to create a complete circular structure without a 5′ end cap and a 3′ poly(A) tail, exhibits exceptional stability, abundance, and tissue-specific characteristics that make it potentially valuable for PMI estimation. However, research on the exploration or application of circRNA in PMI estimation has been limited. This study aims to investigate the correlation between circRNA and PMI. In this study, liver tissue samples were collected from mice at six different time points at 4 °C, 18 °C, 25 °C, and 35 °C, respectively. The reference gene 28S rRNA and the biomarker circRnf169 were successfully screened. Quantitative PCR was employed to examine the correlation between circRnf169 levels and PMI. At 4 °C, the level of circRnf169 decreased with prolonged PMI, whereas at 18 °C, 25 °C, and 35 °C, the circRnf169 RNA was degraded rapidly, indicating that circRnf169 is suitable for PMI estimation at low temperatures or early PMI. These findings suggest the establishment of mathematical model for early PMI based on circRnf169 using liver tissue, which may serve as a reliable marker. Further research is required in order to develop more markers in mice and/or to validate these mathematical models in human samples. Full article

The COVID-19 pandemic has profoundly impacted the global tourism industry, forcing tourism entrepreneurs to adapt and innovate in order to recover. This systematic review aims to identify scientific evidence on tourism entrepreneurship experiences in Latin America during the COVID-19 pandemic. Specifically, the review seeks to uncover key challenges faced by tourism entrepreneurs, the locations and types of tourism most affected, and to draw lessons from these experiences. The authors followed the PRISMA protocol, identifying 15 research studies on tourism entrepreneurship in Latin America. The review analyzed articles from seven key databases—Scopus, Web of Science, Scielo, EBSCO, Proquest, Gale Academic Onefile, and LA Referencia—focusing on studies that examined tourism entrepreneurship within the context of COVID-19. These studies employed diverse methodologies, including case studies, surveys, and data analysis. The results show tourism entrepreneurs in Latin America faced challenges like economic crises, business closures, and unemployment. They responded with biosecurity protocols, domestic tourism, and digital tools such as online platforms and QR codes. A shift toward sustainable models like ecotourism highlighted local development and conservation. The pandemic spurred innovation and resilience, with adaptability, digital transformation, and collaboration, driving recovery. Sustainable practices and authentic experiences are key to long-term success. Full article

Camera arrays typically use image-stitching algorithms to generate wide field-of-view panoramas, but parallax and color differences caused by varying viewing angles often result in noticeable artifacts in the stitching result. However, existing solutions can only address specific color difference issues and are ineffective for pinhole images with parallax. To overcome these limitations, we propose a parallax-tolerant weakly supervised pixel-wise deep color correction framework for the image stitching of pinhole camera arrays. The total framework consists of two stages. In the first stage, based on the differences between high-dimensional feature vectors extracted by a convolutional module, a parallax-tolerant color correction network with dynamic loss weights is utilized to adaptively compensate for color differences in overlapping regions. In the second stage, we introduce a gradient-based Markov Random Field inference strategy for correction coefficients of non-overlapping regions to harmonize non-overlapping regions with overlapping regions. Additionally, we innovatively propose an evaluation metric called Color Differences Across the Seam to quantitatively measure the naturalness of transitions across the composition seam. Comparative experiments conducted on popular datasets and authentic images demonstrate that our approach outperforms existing solutions in both qualitative and quantitative evaluations, effectively eliminating visible artifacts and producing natural-looking composite images. Full article

This article investigates the performance of the Galileo Open Service Navigation Message Authentication (OSNMA) system in real-life environments prone to RF interference (RFI), jamming, and/or spoofing attacks. Considering the existing data that indicate a relatively high number of RFI- and spoofing-related incidents reported in Eastern Europe, this study details a data-collection campaign along various roads through urban, suburban, and rural settings, mostly in three border counties in East and South-East of Romania, and presents the results based on the data analysis. The key performance indicators are determined from the perspective of an end user relying only on Galileo OSNMA authenticated signals. The Galileo OSNMA signals were captured using one of the few commercially available GNSS receivers that can perform this OSNMA authentication algorithm incorporating the satellite signals. This work includes a presentation of the receiver’s operation and of the authentication results obtained during test runs that experienced an unusually high number of RFI-related incidents, followed by a detailed analysis of instances when such RFI impaired or fully prevented obtaining an authenticated position, velocity, and time (PVT) solution. The results indicate that Galileo OSNMA demonstrates significant robustness against interference in real-life RF-degraded environments, dealing with both accidental and intentional interference. Full article

Recently, the application of fog-computing technology to vehicular ad hoc networks (VANETs) has rapidly advanced. Despite these advancements, challenges remain in ensuring efficient communication and security. Specifically, there are issues such as the high communication and computation load of authentications and insecure communication over public channels between fog nodes and vehicles. To address these problems, a lightweight and secure authenticated key agreement protocol for confidential communication is proposed. However, we found that the protocol does not offer perfect forward secrecy and is vulnerable to several attacks, such as privileged insider, ephemeral secret leakage, and stolen smart card attacks. Furthermore, their protocol excessively uses elliptic curve cryptography (ECC), resulting in delays in VANET environments where authentication occurs frequently. Therefore, this paper proposes a novel authentication protocol that outperforms other related protocols regarding security and performance. The proposed protocol reduced the usage frequency of ECC primarily using hash and exclusive OR operations. We analyzed the proposed protocol using informal and formal methods, including the real-or-random (RoR) model, Burrows–Abadi–Nikoogadam (BAN) logic, and automated validation of internet security protocols and applications (AVISPA) simulation to show that the proposed protocol is correct and secure against various attacks. Moreover, We compared the computational cost, communication cost, and security features of the proposed protocol with other related protocols and show that the proposed methods have better performance and security than other schemes. As a result, the proposed scheme is more secure and efficient for fog-based VANETs. Full article

The rise of deepfakes—synthetic media generated using artificial intelligence—threatens digital content authenticity, facilitating misinformation and manipulation. However, deepfakes can also depict real or entirely fictitious individuals, leveraging state-of-the-art techniques such as generative adversarial networks (GANs) and emerging diffusion-based models. Existing detection methods face challenges with generalization across datasets and vulnerability to adversarial attacks. This study focuses on subsets of frames extracted from the DeepFake Detection Challenge (DFDC) and FaceForensics++ videos to evaluate three convolutional neural network architectures—XCeption, ResNet, and VGG16—for deepfake detection. Performance metrics include accuracy, precision, F1-score, AUC-ROC, and Matthews Correlation Coefficient (MCC), combined with an assessment of resilience to adversarial perturbations via the Fast Gradient Sign Method (FGSM). Among the tested models, XCeption achieves the highest accuracy (89.2% on DFDC), strong generalization, and real-time suitability, while VGG16 excels in precision and ResNet provides faster inference. However, all models exhibit reduced performance under adversarial conditions, underscoring the need for enhanced resilience. These findings indicate that robust detection systems must consider advanced generative approaches, adversarial defenses, and cross-dataset adaptation to effectively counter evolving deepfake threats. Full article

Cybersecurity, complimenting authentication, has become the backbone of the Internet of Things. In the authentication process, the word authentication is of the utmost importance, as it is the door through which both Mr. Right Guy and Mr. Wrong Guy can pass. It is the key to opening the most important and secure accounts worldwide. When authentication is complete, surely there will be passwords. Passwords are a brain-confusing option for the user to choose when making an account during the registration/sign-up process. Providing reliable, effective, and privacy-preserving authentication for individuals in mobile networks is challenging due to user mobility, many attack vectors, and resource-constrained devices. This review paper explores the transformation and modern mobile authentication schemes, categorizing them into password, graphical, behavioral, keystroke, biometric, touchscreen, color, and gaze-based methodologies. It aims to examine the strengths and limitations focused on challenges like security and usability. Standard datasets and performance evaluation measures are also discussed. Finally, research gaps and future directions in this essential and emerging area of research are discussed. Full article

Background/Objectives: Shrimp surimi-based products (SSPs) are composed of minced shrimp meat and are highly susceptible to food fraud as fish surimi. This study employed a double-gene metabarcoding approach to authenticate SSPs sold on Chinese e-commerce platforms. Methods: 16S rRNA and 12S rRNA genes were amplified and sequenced from 24 SSPs. Mislabeling was evaluated based on the correspondence between the ingredients (only those of animal origin) reported on the products’ labels and the molecular results. Results: Overall, 87.50% of SSPs (21/24) were found to be mislabeled. The replacement of Penaeus vannamei with other shrimp species was particularly noteworthy. Interestingly, in some SSPs, the primary species detected in terms of sequence abundance were not shrimp but fish, pork, chicken, and cephalopods, raising concerns regarding both health risks and ethical issues related to SSP consumption. The 12S rRNA sequencing results revealed that fish species like Gadus chalcogrammus, Evynnis tumifrons, and Priacanthus arenatus were added to some SSPs in significant proportions, with certain products relying on fish priced from “Low” to “High” levels to substitute higher-cost shrimp. Notably, many fish species in SSPs were highly vulnerable to fishing, raising sustainability concerns. Overall, the high mislabeling rate in SSPs, as well as the detection of endangered fish species (Pangasianodon hypophthalmus), underscores significant quality control issues. Conclusions: DNA metabarcoding has proven to be an effective tool for ingredient authentication in processed seafood. Full article

Cordyceps sinensis is a second-class, nationally protected, medicinal fungi and serves as a functional nutrient in China. C. sinensis is extremely scarce due to its peculiar growing environment and the extensive gathering practices carried out by humans. A large number of counterfeit products for this fungi have also emerged in the market. At present, there is a lack of research on the differential metabolites of C. sinensis and its counterfeit products. The current study used an LC-MS non-targeted metabolomics method to compare the differences in metabolites between C. sinensis and other Cordyceps. The results indicated that there were significant differences in the metabolites between C. sinensis and the others. The 18 superclasses were found to have differences, involving lipids, organic acids, nucleosides, carbohydrates, amino acids, vitamins, and their derivatives. Compared with the other four groups of Cordyceps, 8 metabolites with significant differences were screened. In addition, the types and abundance of different metabolites of nucleosides of C. sinensis were superior compared to other Cordyceps (e.g., 5-Methyldioxycytidine, didanosine, cytidine, etc.). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the metabolism of arginine and proline, and glycerophosphate metabolism were the two significant differences in the metabolic pathways between C. sinensis and other Cordyceps. The research results provide a reference for identifying the authenticity of C. sinensis using non-targeted metabolic methods. Full article

Face verification and identification traditionally follow a symmetric matching approach, where the same model (e.g., ResNet-50 vs. ResNet-50) generates embeddings for both gallery and query images, ensuring compatibility. However, real-world scenarios often demand asymmetric matching, especially when query devices have limited computational resources or employ heterogeneous models (e.g., ResNet-50 vs. SwinTransformer). This asymmetry can degrade face recognition performance due to incompatibility between embeddings from different models. To tackle this asymmetric face recognition problem, we introduce the Learnable Anchor Embedding (LAE) model, which features two key innovations: the Shared Learnable Anchor and a Light Cross-Attention Mechanism. The Shared Learnable Anchor is a dynamic attractor, aligning heterogeneous gallery and query embeddings within a unified embedding space. The Light Cross-Attention Mechanism complements this alignment process by reweighting embeddings relative to the anchor, efficiently refining their alignment within the unified space. Extensive evaluations of several facial benchmark datasets demonstrate LAE’s superior performance, particularly in asymmetric settings. Its robustness and scalability make it an effective solution for real-world applications such as edge-device authentication, cross-platform verification, and environments with resource constraints. Full article

This study develops a sustainable evaluation framework for Yixing Zisha pottery design schemes generated by Artificial Intelligence Generated Content (AIGC) technology, emphasizing the integration of cultural heritage preservation with innovation. As a traditional Chinese craft and a recognized element of intangible cultural heritage (ICH), Yixing Zisha pottery is celebrated for its cultural depth and unique design techniques. Guided by emotional design theory, the framework assesses aesthetic, functional, and emotional dimensions through hierarchical analysis. Using the Delphi method and Analytic Hierarchy Process (AHP), primary and secondary indicators were identified and weighted based on expert consensus. AIGC technology, underpinned by advanced AI algorithms, generates culturally authentic yet innovative design solutions, striking a balance between tradition and modernity. The findings reveal that this approach enhances design diversity, functionality, and efficiency while fostering global cultural awareness. By providing practical guidance for integrating AIGC technology into traditional craftsmanship, the research offers a replicable model for other traditional crafts and contributes to the theoretical advancement of sustainable cultural heritage practices. By bridging the gap between digital innovation and heritage preservation, this study addresses the critical need for sustainable strategies in the creative industries. Full article