Search Results (494)

Achieving reliable navigation is critical for GNSS receivers subject to spoofing attacks. Utilizing the inherent sparsity and inconsistency of spoofing signals, this paper proposes an anti-spoofing framework for GNSS receivers to detect, classify, and recover positions from spoofing attacks without additional devices. A sparse decomposition algorithm with non-negative constraints limited by signal power magnitudes is proposed to achieve accurate spoofing detections while extracting key features of the received signals. In the classification stage, these features continuously refine each channel of the receiver’s code tracking loop, ensuring that it tracks either the authentic or counterfeit signal components. Moreover, by leveraging the inherent inconsistency of spoofing properties, we incorporate the Hausdorff distance to determine the most overlapped position sets, distinguishing genuine trajectories and mitigating spoofing effects. Experiments on the TEXBAT dataset show that the proposed algorithm detects 98% of spoofing attacks, ensuring stable position recovery with an average RMSE of 6.32 m across various time periods. Full article

The applications of magnetic particles in anti-counterfeiting and anti-absorbing coatings and other functional materials are becoming increasingly widespread. However, due to their high density, the magnetic particles rapidly settle in organic resin media, significantly affecting the quality of the related products. Thereby, reducing the density of the particles is essential. To achieve this goal, high-density magnetic particles were coated onto the surface of hollow silica using anion–cation composite technology. Further, the silane coupling agent N-[3-(trimethoxysilyl)propyl]ethylenediamine was bonded to the surface of magnetic particles to form an amino-covered interfacial layer with a pH value of 9.28, while acrylic acid was polymerized and coated onto the surface of hollow silica to form a carboxyl-covered interfacial layer with a pH value of 4.65. Subsequently, the two materials were compounded to obtain a low-density composite magnetic material. The morphologies and structural compositions of the magnetic composite materials were studied by FTIR, SEM, SEM-EDS, XRD, and other methods. The packing densities of the magnetic composite materials were compared using the particle packing method, thereby solving the problem of magnetic particles settling in the resin solution. Full article

Honey is globally recognized for its substantial nutritional and therapeutic properties. However, its high market value makes it susceptible to counterfeiting, negatively impacting consumers and beekeepers. This paper presents a blockchain-based framework to monitor the honey trade supply chain, ensuring authenticity. The framework employs an oracle component to verify honey quality and origin using IoT data. Additionally, it integrates fungible and non-fungible tokens to track honey batches. The study evaluates the economic feasibility of this approach, demonstrating that the cost of performing a trade is less than USD 1, with the oracle component achieving an average accuracy rate of 90% in detecting falsified sensor data. Full article

Hierarchical surface structuring is a critical aspect of advanced materials design, impacting fields ranging from optics to biomimetics. Among several laser-based methods for complex structuring of photo-responsive surfaces, the broadband vectorial interferometry proposed here offers unique performances. Such a method leverages a polychromatic laser source, an unconventional choice for holographic encoding, to achieve deterministic multiscale surface structuring through interference light patterning. Azopolymer films are used as photosensitive substrates. By exploring the interaction between optomechanical stress modulations at different spatial periodicities induced within the polymer bulk, we demonstrate the emergence of hierarchical Fourier surfaces composed of multiple deterministic levels. These structures range from sub-micrometer to tens of micrometers scale, exhibiting a high degree of control over their morphology. The experimental findings reveal that the optical encoding scheme significantly influences the resulting topographies. The polarization light patterns lead to more regular and symmetric hierarchical structures compared to those obtained with intensity patterns, underscoring the role of vectorial light properties in controlling surface morphologies. The proposed method is fully scalable, compatible with more complex recording schemes (including multi-beam interference), and it is applicable to a wide range of advanced technological fields. These include optics and photonics (diffractive elements, polarimetric devices), biomimetic surfaces, topographical design, information encoding, and anti-counterfeiting, offering a rapid, reliable, and versatile strategy for high-precision surface structuring at a submicrometric scale. Full article

The palm-vein recognition system has garnered attention as a biometric technology due to its resilience to external environmental factors, protection of personal privacy, and low risk of external exposure. However, with recent advancements in deep learning-based generative models for image synthesis, the quality and sophistication of fake images have improved, leading to an increased security threat from counterfeit images. In particular, palm-vein images acquired through near-infrared illumination exhibit low resolution and blurred characteristics, making it even more challenging to detect fake images. Furthermore, spoof detection specifically targeting palm-vein images has not been studied in detail. To address these challenges, this study proposes the Fourier-gated feature-fusion network (FGFNet) as a novel spoof detector for palm-vein recognition systems. The proposed network integrates masked fast Fourier transform, a map-based gated feature fusion block, and a fast Fourier convolution (FFC) attention block with global contrastive loss to effectively detect distortion patterns caused by generative models. These components enable the efficient extraction of critical information required to determine the authenticity of palm-vein images. In addition, fractal dimension estimation (FDE) was employed for two purposes in this study. In the spoof attack procedure, FDE was used to evaluate how closely the generated fake images approximate the structural complexity of real palm-vein images, confirming that the generative model produced highly realistic spoof samples. In the spoof detection procedure, the FDE results further demonstrated that the proposed FGFNet effectively distinguishes between real and fake images, validating its capability to capture subtle structural differences induced by generative manipulation. To evaluate the spoof detection performance of FGFNet, experiments were conducted using real palm-vein images from two publicly available palm-vein datasets—VERA Spoofing PalmVein (VERA dataset) and PLUSVein-contactless (PLUS dataset)—as well as fake palm-vein images generated based on these datasets using a cycle-consistent generative adversarial network. The results showed that, based on the average classification error rate, FGFNet achieved 0.3% and 0.3% on the VERA and PLUS datasets, respectively, demonstrating superior performance compared to existing state-of-the-art spoof detection methods. Full article

Traditional seed supply chains face several hidden risks. Certain regulatory departments tend to focus primarily on entity circulation while neglecting the origin and accuracy of data in seed quality supervision, resulting in limited precision and low credibility of traceability information related to quality and safety. Blockchain technology offers a systematic solution to key issues such as data source distortion and insufficient regulatory penetration in the seed supply chain by enabling data rights confirmation, tamper-proof traceability, smart contract execution, and multi-node consensus mechanisms. In this study, we developed a system that integrates blockchain and neural networks to provide seed traceability services. When uploading seed traceability information, the neural network models are employed to verify the authenticity of information provided by humans and save the tags on the blockchain. Various neural network architectures, such as Multilayer Perceptron, Recurrent Neural Network, Fully Convolutional Neural Network, and Long Short-term Memory model architectures, have been tested to determine the authenticity of seed traceability information. Among these, the Long Short-term Memory model architecture demonstrated the highest accuracy, with an accuracy rate of 90.65%. The results demonstrated that neural networks have significant research value and potential to assess the authenticity of information in a blockchain. In the application scenario of seed quality traceability, using blockchain and neural networks to determine the authenticity of seed traceability information provides a new solution for seed traceability. This system empowers farmers by providing trustworthy seed quality information, enabling better purchasing decisions and reducing risks from counterfeit or substandard seeds. Furthermore, this mechanism fosters market circulation of certified high-quality seeds, elevates crop yields, and contributes to the sustainable growth of agricultural systems. Full article

A metasurface capable of flexibly manipulating electromagnetic waves to realize holograms presents significant potential in millimeter-wave imaging systems and data storage domains. In this study, full-space three-dimensional holograms are realized from a reflection–transmission integrated reconfigurable metasurface, which can achieve nearly 360° phase coverage in reflection space and 180° phase coverage in transmission space. By adjusting the voltage applied to the constituting electronically tunable meta-atoms of the metasurface, an octahedron hologram constituted by three hologram images in different focal planes is generated in the reflection space at 6.25 GHz. Moreover, a diamond hologram, also composed of three hologram images in different focal planes, is achieved in the transmission space at 6.75 GHz. Both the numerical simulation and experimental measurement are performed to validate the full-space holograms implemented by the modified weighted Gerchberg–Saxton (WGS) algorithm with specific phase distribution in different imaging planes. The obtained results pave the way for a wide range of new applications, such as next-generation three-dimensional displays for immersive viewing experiences, high-capacity optical communication systems with enhanced data encoding capabilities, and ultra-secure anti-counterfeiting solutions that are extremely difficult to replicate. Full article

In this work, methyl viologen (MV) was adsorbed into the nanopores of Si/Al H-β-zeolite via cation exchange. The resulting MV@β-zeolite possessed absorption/fluorescence dual-mode and photo/chemical synergistic stimuli-responsive chromism. Owing to the acidic surrounding provided by β-zeolite, the chromism of MV required the synergistic stimuli of UV irradiation and a chemical reductant (such as Na₂SO₃). UV irradiation induced single electron transfer from the chemical reductant to MV@β-zeolite, leading to enhanced absorption at 610 nm together with a daylight color change from pale yellow to blue. Meanwhile, the nanopores of β-zeolite inhibited aggregation-caused quenching of MV, enabling MV to emit cyan fluorescence at 500 nm. After the single electron transfer of the chemical reductant under UV irradiation, the cyan fluorescence of MV@β-zeolite was quenched. Additionally, MV@β-zeolite exhibited a short stimulus response time (250 s) and good color change reversibility. These findings in this work provide valuable insights into the design of multi-mode and synergistic stimuli-responsive viologen-based chromic materials, particularly for applications in secure high-throughput information storage, high-level anti-counterfeiting and multi-target multi-mode sensing. Full article

Traditional systems in real life lack transparency and ease of use due to their reliance on centralization and large infrastructure. Furthermore, many sectors that rely on information technology face major challenges related to data integrity, trust, and counterfeiting, limiting scalability and acceptance in the community. With the decentralization and digitization of energy transactions in smart grids, security, integrity, and fraud prevention concerns have increased. The main problem addressed in this study is the lack of a secure, tamper-resistant, and decentralized mechanism to facilitate direct consumer-to-prosumer energy transactions. Thus, this is a major challenge in the smart grid. In the blockchain, current consensus algorithms may limit the scalability of smart grids, especially when depending on popular algorithms such as Proof of Work, due to their high energy consumption, which is incompatible with the characteristics of the smart grid. Meanwhile, Proof of Stake algorithms rely on energy or cryptocurrency stake ownership, which may make the smart grid environment in blockchain technology vulnerable to control by the many owning nodes, which is incompatible with the purpose and objective of this study. This study addresses these issues by proposing and implementing a hybrid framework that combines the features of private and public blockchains across three integrated layers: user interface, application, and blockchain. A key contribution of the system is the design of a novel consensus algorithm, Proof of Energy, which selects validators based on node roles and randomized assignment, rather than computational power or stake ownership. This makes it more suitable for smart grid environments. The entire framework was developed without relying on existing decentralized platforms such as Ethereum. The system was evaluated through comprehensive experiments on performance and security. Performance results show a throughput of up to 60.86 transactions per second and an average latency of 3.40 s under a load of 10,000 transactions. Security validation confirmed resistance against digital signature forgery, invalid smart contracts, race conditions, and double-spending attacks. Despite the promising performance, several limitations remain. The current system was developed and tested on a single machine as a simulation-based study using transaction logs without integration of real smart meters or actual energy tokenization in real-time scenarios. In future work, we will focus on integrating real-time smart meters and implementing full energy tokenization to achieve a complete and autonomous smart grid platform. Overall, the proposed system significantly enhances data integrity, trust, and resistance to counterfeiting in smart grids. Full article

This paper studies a supply chain comprising a supplier, a third-party remanufacturer (TPR), and a retailer. The retailer sells both genuine and remanufactured products (i.e., Model O). Leveraging information advantages, the retailer may engage in brand deception by mislabeling remanufactured products as genuine to obtain extra profits (i.e., Model BD). AI-powered anti-counterfeiting technologies (AIT) (i.e., Model BA) and revenue-sharing contracts (i.e., Model C) are considered countermeasures. The findings reveal that (1) brand deception reduces (increases) sales of genuine (remanufactured) products, prompting the supplier (TPR) to lower (raise) wholesale prices. The asymmetric profit erosion effect highlights the gradual erosion of profits for the supplier, retailer, and TPR under brand deception. (2) The bi-interval adaptation effect indicates that AIT is particularly effective in industries with low adaptation resistance. When both the relabeling rate and industry adaptation resistance are low (high), Model BA (Model O) achieves a triple win. (3) Sequentially, when the industry adaptation resistance is low, AIT can significantly improve total profits, consumer surplus (CS), and social welfare (SW). Compared to Model BD, revenue-sharing offers slight advantages in CS but notable disadvantages in SW. Full article

The coronavirus pandemic has spread globally, affecting over 700 million people and resulting in over 7 million deaths. In response, global pharmaceutical companies and disease control centers have urgently sought effective treatments and vaccines. However, the rise of counterfeit drugs has become a significant concern amid this urgency. To standardize the legal provision and usage of genetic resources, the United Nations Development Program (UNDP) introduced the Nagoya Protocol. Despite advancements in drug research, the production process remains tedious, complex and vulnerable to fraud. FairChain addresses this pressing challenge by creating a transparent ecosystem that builds trust among all stakeholders throughout the Drug Development Life Cycle (DDLC) by using decentralized, immutable, and transparent blockchain technology. This makes FairChain the first digital health tool to implement the principles of the UNDP’s Nagoya Protocol among all stakeholders throughout all DDLC stages, starting with sample collection, to discovery and development, to preclinical research, to clinical development, to regulator review, and ending with post-market monitoring. Therefore, FairChain allows pharmaceutical companies to document the entire drug production process, landowners to monitor bio-samples from their land, doctors to share clinical research, and regulatory agencies such as the Food and Drug Authority to oversee samples and authorize production. FairChain should enhance transparency, foster trust and efficiency, and ensure a fair and traceable DDLC. To date, no blockchain-based framework has addressed the integration of traceability, auditability, and Nagoya Protocol compliance within a unified system architecture. This paper introduces FairChain, a system that formalizes these requirements in a modular, policy-aligned, and verifiable digital trust infrastructure. Full article

The private sector in Tanzania has played an essential role in improving coverage and access to mosquito nets. This follow-up study assessed the overall market share for untreated and insecticide-treated nets (ITNs) and misleading or counterfeit ITN products in commercial markets. This study was conducted from March to April 2024 in ten regions in Tanzania. The study used mixed methods: (1) a quantitative survey among sampled outlets supported by photographic documentation of all net products and (2) key informant interviews of retailers and wholesalers. We assessed the relationship between market share and population access using ANOVA and Pearson correlation. No counterfeit or misleading nets were found, consistent with results from 2017, 2021, and 2022 surveys. Untreated nets dominated all markets, comprising 99% of all products observed and 99% of estimated net sales 3 months before the survey. Legitimate ITNs were crowded out from the studied markets. Leaked nets from free distributions were present but extremely limited (1%) and at their lowest level of the survey rounds. Untreated nets were more expensive than leaked ITNs for both regular- and queen-size nets. Despite ongoing efforts, increasing the share of legitimate ITNs remains a significant challenge in a context of large-scale public sector distributions. Full article

Global Navigation Satellite Systems (GNSS) are widely used for positioning, navigation, and timing (PNT) applications, making them a critical infrastructure component. However, GNSS signals and receivers are vulnerable to several attacks that can expose the users to serious threats. The GNSS spoofing attack, for example, is one of the most widespread in this domain and is used to manipulate positioning and timing data by transmitting counterfeit signals. Thus, in this study, we propose a method for analyzing and detecting anomalies in RINEX observation data that is associated with spoofing attacks. The proposed method is based on Long Short-Term Memory (LSTM) networks and focuses on the observation parameters defined by the RINEX standard, which are computed in the Measurements block and subsequently used in the Navigation block of a GNSS receiver architecture. Attack detection involves processing GNSS observation codes and learning the temporal dependencies necessary to identify anomalies associated with GNSS signal spoofing. During the testing phase, the proposed method was applied to GNSS observation codes affected by spoofing, using an LSTM-based reconstruction approach. An ensemble strategy across grouped observation codes was used to identify temporal inconsistencies indicative of anomalies. Full article

The World Health Organization (WHO) Global Benchmarking Tool (GBT) classifies regulatory systems into four maturity levels, with Maturity Level 3 (ML3) signifying a stable and effective regulatory environment. As of January 2025, eight African nations—Egypt, Ghana, Nigeria, Rwanda, Senegal, South Africa, Tanzania, and Zimbabwe—have attained ML3 status, marking a significant milestone in the continent’s regulatory landscape. Achieving ML3 confers critical benefits, including reducing substandard and falsified medicines, which enhances public health safety and fosters trust in healthcare systems. This progress encourages local manufacturing, diminishing reliance on imported medicines and promoting economic development. Furthermore, ML3 NRAs are better equipped to address public health emergencies, enabling swift approvals for vaccines and therapeutics while upholding safety standards. Nonetheless, challenges persist, including fragmented regulatory systems, the prevalence of counterfeit medicines, and limited resources. Overcoming these hurdles necessitates enhanced organizational capacity, investments in training, and the promotion of collaboration among NRAs. There is an urgent call for greater political commitment and resource allocation to strengthen regulatory systems across Africa. Achieving and maintaining ML3 status is essential for enhancing medicine regulation, supporting local manufacturing, and improving public health outcomes across the continent. While progress has been made, sustained efforts are crucial to tackling existing challenges and harnessing the full potential of advanced regulatory frameworks. Full article

The so-called “counterfeit monks and nuns” 僧尼偽濫 is regarded as an important reason for the “Huichang Persecution of Buddhism” 會昌滅佛, but it reflects the central views of the Tang Dynasty. When we delve into the local society of the Mid-to-Late Tang period, we find that they developed their own narrative logic. From the perspective of the imperial court, Li Deyu 李德裕 criticized Wang Zhixing 王智興 for establishing an ordination altar in Sizhou 泗州 for personal gain. However, in the biographical inscription of monk Mingyuan 明遠 in Sizhou, Wang Zhixing is portrayed as a key figure who collaborated with Mingyuan to ensure the survival of the Kaiyuan Monastery 開元寺, with the inauguration of the ordination altar 戒壇 serving as a necessary means to obtain financial resources. In fact, Mingyuan had previously undertaken a similar operation at the Lingju Monastery 靈居寺 in Liuhe County 六合縣, Yangzhou 揚州. The inscription of the Lingju Monastery Stele 大唐揚州六合縣靈居寺碑 reflects the cooperation between local monks and secular people at that time. During the process of rebuilding the monasteries, Mingyuan cleverly exploited the cult of the divine monk Sengqie 僧伽 within the Society of Jianghuai 江淮. The cult of Sengqie had become a national belief during the Mid-to-Late Tang period, and the existence of the Sengqie pagoda 僧伽塔 made the Kaiyuan Monastery in Sizhou uniquely significant. Later on, Youxuan 幽玄 also carried out similar initiatives by establishing an ordination altar for the restoration at the Baoli Monastery 寶曆寺 in Hongzhou 洪州. If we set aside the shadow of the overarching theme of the Huichang Persecution of Buddhism on the history of Buddhism during the Mid-to-Late Tang period, we may uncover a more vibrant picture of local Buddhism. Full article