Search Results (152)

The identification/classification of textile fibres is essential in manufacturing, forensic science, cultural heritage preservation, and recycling. Conventional methods, including solubility tests, optical microscopy, and chromatographic techniques, are often destructive, labour-intensive, and limited in scope. Vibrational spectroscopy, particularly near-infrared (NIR), Fourier-transform infrared (FTIR), and Raman spectroscopy, has emerged as a rapid, non-destructive, and accurate alternative for fibre analysis. However, multi-composition textiles, dyes, finishing agents, and ageing effects frequently cause overlapping spectral features, hampering direct interpretation. This review examines the combined use of vibrational spectroscopy and chemometrics for textile fibre discrimination. It critically evaluates the performance of different spectroscopic techniques in classifying natural, synthetic, and blended fibres. The role of multivariate analysis methods, such as PCA, PLS, LDA, SIMCA, and machine learning algorithms, in improving spectral interpretation and classification accuracy is highlighted. Key factors affecting model robustness, including spectral pre-processing, sample heterogeneity, moisture, and colour, are also discussed. The integration of spectroscopy with chemometrics provides a robust, scalable, and sustainable solution for fibre identification, supporting quality control, fraud detection, and circular economy initiatives. This approach demonstrates significant potential for both research and industrial applications. Full article

As document forgery schemes become increasingly sophisticated, organizations face mounting challenges in authenticating seals found on official documents. In this study, we collaborated with law enforcement agencies in Taiwan to develop an AI-driven system that supports the rapid identification of forged seals. Instead of relying on manual inspection, the system leverages deep neural networks to analyze overall and fine visual features of seal images. By integrating advanced image enhancement, similarity measurement, and feature comparison modules, the system efficiently filters and ranks potential matches from a dedicated police database. Evaluation on a dataset containing several hundred forged seal images demonstrates that the system achieves greater than 90% accuracy for detecting counterfeit seals. The solution not only reduces the time and effort required for verification but also provides investigators with immediate access to relevant case histories, thereby strengthening the overall fraud prevention workflow. Full article

This study empirically investigates the performance of Elastic Machine Learning, an industrial, unsupervised anomaly detection tool, in the identification of fraudulent behavior in banking transactions. Using AI-generated datasets that were designed to simulate realistic banking environments, the analysis examines three distinct fraud-related scenarios: (i) abnormal associations between a single account and multiple IP addresses, (ii) bursts of cross-border transactions within short time windows, and (iii) unusually high transaction values relative to historical behavior. The results show that the Elastic platform consistently detects anomalous patterns across all examined scenarios by flagging suspicious behavior during the fraud window in real time. This study provides the first empirical assessment of the operational behavior of an industrial, unsupervised anomaly detection platform across multiple fraud-related scenarios in the banking sector, offering practical insights for real-time fraud monitoring and early-warning systems, while supporting institutional resilience and the robustness of the financial system. Full article

In this study, a multiplex PCR assay was developed for the rapid and accurate identification of three Chionoecetes species (Chionoecetes bairdi, C. opilio, and C. japonicus) available in global seafood markets. The morphological similarity between imported female C. bairdi from Japan and native C. opilio in Korea complicates visual discrimination and raises concerns over potential mislabeling. To address this issue, mitochondrial cytochrome c oxidase subunit I (COI) gene sequences were analyzed to identify species-specific SNPs, and primers were designed accordingly. Singleplex PCR confirmed species-specific amplification among the three target species, and the optimal annealing temperature was determined. The multiplex PCR simultaneously amplified distinct fragments of 598 bp (C. bairdi), 401 bp (C. opilio), and 194 bp (C. japonicus), with no nonspecific amplification or primer–dimer formation. Sensitivity testing revealed a detection limit of 0.01 ng/µL for all three species, defined as the lowest DNA concentration at which species-specific bands were consistently observed in at least two out of three replicates. These results demonstrate that the developed multiplex PCR is a reliable, rapid, and cost-effective tool for accurate species identification, supporting sustainable resource management, preventing seafood fraud, and ensuring safe distribution in both Korea and global seafood markets. Full article

Honey fraud represents a persistent and analytically challenging form of food adulteration, driven by globalised supply chains, strong economic incentives and asymmetries in regulatory oversight and analytical capacity. Conventional physicochemical, spectroscopic and isotopic methods provide legally robust tools for routine control, yet increasingly struggle to detect sophisticated adulteration strategies that are compositionally optimised to mimic authentic honey profiles. These challenges are amplified in a global context, where heterogeneous enforcement landscapes and fragmented analytical infrastructures create exploitable vulnerabilities across international trade networks. This narrative review synthesises current knowledge on honey fraud typologies and critically evaluates established analytical approaches alongside emerging omics-based authentication strategies, including genomics, metabolomics, proteomics and microbiome profiling. Omics-based approaches extend authenticity assessment beyond single-marker paradigms by capturing multidimensional biological and compositional signatures, thereby improving sensitivity to subtle and system-aware fraud (i.e., adulteration strategies that adapt to prevailing analytical detection methods and regulatory thresholds) strategies. To maintain evidentiary clarity, this review explicitly distinguishes between analytically demonstrated vulnerabilities, technically feasible adulteration scenarios and fraud practices documented in regulatory or enforcement contexts. Advanced technology-driven strategies are therefore discussed as potential system-level risks rather than confirmed large-scale honey fraud cases. This differentiation not only safeguards evidentiary precision but also highlights the structural limits of purely analytical solutions. Beyond analytical performance, honey authentication is framed as a systemic challenge embedded in global food systems. This review highlights the need for integrated, data-driven and scalable authentication frameworks that align analytical innovation with reference harmonisation, governance structures and international regulatory cooperation to support resilient and globally robust honey authenticity control. Full article

With the rapid growth of live-streaming e-commerce and digital marketing, abnormal marketing behaviors have become increasingly concealed, coordinated, and intertwined across heterogeneous data modalities, posing substantial challenges to data-driven platform governance and early risk identification. Existing approaches often fail to jointly model cross-modal temporal semantics, the gradual evolution of weak abnormal signals, and organized group-level manipulation. To address these challenges, a data-driven multimodal abnormal behavior detection framework, termed MM-FGDNet, is proposed for large-scale live-streaming environments. The framework models abnormal behaviors from two complementary perspectives, namely temporal evolution and cooperative group structure. A cross-modal temporal alignment module first maps video, text, audio, and user behavioral signals into a unified temporal semantic space, alleviating temporal misalignment and semantic inconsistency across modalities. Building upon this representation, a temporal fraud pattern modeling module captures the progressive transition of abnormal behaviors from early incipient stages to abrupt outbreaks, while a cooperative manipulation detection module explicitly identifies coordinated interactions formed by organized user groups and automated accounts. Extensive experiments on real-world multi-platform live-streaming e-commerce datasets demonstrate that MM-FGDNet consistently outperforms representative baseline methods, achieving an AUC of 0.927 and an F1 score of 0.847, with precision and recall reaching 0.861 and 0.834, respectively, while substantially reducing false alarm rates. Moreover, the proposed framework attains an Early Detection Score of 0.689. This metric serves as a critical benchmark for operational viability, quantifying the system’s capacity to shift platform governance from passive remediation to proactive prevention. It confirms the reliable identification of the “weak-signal” stage—rigorously defined as the incipient phase where subtle, synchronized deviations in interaction rhythms manifest prior to traffic inflation outbreaks—thereby providing the necessary time window for preemptive intervention against coordinated manipulation. Ablation studies further validate the independent contributions of each core module, and cross-domain generalization experiments confirm stable performance across new streamers, new product categories, and new platforms. Overall, MM-FGDNet provides an effective and scalable data-driven artificial intelligence solution for early detection of coordinated abnormal behaviors in live-streaming systems. Full article

For high-value-added products such as matcha, scientific confirmation of the origin is essential for quality assurance and fraud prevention. In this study, three nondestructive analytical techniques, specifically fluorescence (FF), near-infrared (NIR), and Fourier transform infrared (FT-IR) spectroscopy, were combined with machine learning algorithms to accurately identify the origin of Japanese matcha. FF data were analyzed using convolutional neural networks (CNNs), whereas NIR and FT-IR spectral data were analyzed using k-nearest neighbors (KNNs), random forest (RF), logistic regression (LR), and support vector machine (SVM) models. The FT-IR–RF model demonstrated the highest accuracy (99.0%), followed by the NIR–KNN (98.7%) and FF–CNN (95.7%) models. Functional group absorption in FT-IR, moisture and carbohydrates in NIR, and amino acid and polyphenol fluorescence in FF contributed to the identification. These findings indicate that the selection of an algorithm appropriate for the characteristics of the spectroscopic data is effective for improving accuracy. This method can quickly and nondestructively identify the origin of matcha and is expected to be applicable to other teas and agricultural products. This new approach contributes to the verification of the authenticity of food and improvement in its traceability. Full article

Food fraud, particularly in the olive oil sector, represents a pressing concern within the agri-food industry, with implications for consumer trust and product authenticity. Certified products like Protected Designation of Origin (PDO) Extra Virgin Olive Oil (EVOO) are premium products that undergo strict quality controls, must comply with specific production regulations, and generally have a higher market price. These characteristics make them particularly vulnerable to economically motivated adulteration. In this study, the adulteration of PDO EVOO with Olive Pomace Oil (POO) and Olive Oil (OO) was investigated through a combined analytical approach. A traditional technique, gas chromatography–mass spectrometry (GC-MS) combined with solid-phase microextraction (SPME), was employed alongside an innovative method based on an electronic nose equipped with metal oxide semiconductor (MOX) sensors. GC-MS analysis enabled the identification of characteristic volatile compounds, providing a detailed chemical fingerprint of the different oil samples. Concurrently, the MOX sensor array successfully detected variations in the volatile profiles released by the adulterated oils, demonstrating its potential as a rapid and cost-effective screening tool. The complementary use of both techniques highlighted the reliability of MOX sensors in differentiating authentic PDO EVOO from adulterated samples and underscored their applicability in routine quality control and fraud prevention strategies. Full article

Financial statement fraud is a socio-technical risk that arises from coupled organizational, informational, and regulatory processes. To address the Identification Paradox in financial fraud detection, where existing models cannot simultaneously recognize both chronic manipulation and acute outbreaks in financial data, this study proposes the Causal–Temporal Asynchrony (CTA) theory as a process-oriented conceptual framework that guides feature construction and model design in a predictive setting. CTA defines fraud motive as a chronic, multi-period accumulation and fraud action as an acute, single-year event. To operationalize CTA within a predictive setting, we build a deployable Decoupling-Fusion System that encodes CTA as an Acute–Chronic Binary Feature Dimensions schema and performs detection via Decoupling-Fusion FraudNet. Within this system, parallel Long Short-Term Memory networks (LSTM) capture chronic motive signals from longitudinal sequences, while parallel Convolutional Neural Networks (CNN) and a Feed-forward Neural Network (FNN) identify acute action signals from multimodal snapshots; the resulting asynchronous probabilities are integrated via an adaptive decision-level fusion mechanism. Empirical tests on China’s A-share market (2001–2021) show the system (AUC = 0.967) outperforms baseline models. Furthermore, eXplainable AI analysis reveals patterns consistent with the classic fraud triangle (pressure, opportunity and rationalization). This study develops a theory-grounded decision-support system that unifies acute and chronic evidence streams and provides a deployable blueprint for continuous auditing and governance. Full article

Detection of financial fraud remains a constant challenge due to the dynamic and highly imbalanced nature of transaction data. This paper proposes the Graph-Temporal Contrastive Transformer (GTCT) framework for modeling both structural dependencies between accounts and temporal evolution in transactional behaviors. We propose a model that combines three components: a graph encoder for modeling relationships between accounts, a temporal encoder for learning sequential patterns in transactions, and a contrastive learning objective that enhances the robustness of representations when supervision is limited. To assess the contribution of each component individually, we systematically remove one module at a time. As shown, an exclusion of the contrastive loss resulted in reduced recall and AUC from 0.867 and 0.982 to 0.805 and 0.948, respectively, indicating the importance of self-supervised learning of representations in fraud detection. Similarly, removing the graph encoder decreased the F1-score from 0.876 to 0.786, which confirmed that modeling transaction structures between accounts is crucial for the identification of complex fraud rings. The exclusion of the temporal encoder led to a more drastic drop in recall (0.743) and AUC (0.905), indicating that capturing the temporal dynamics of transactions is relevant. By comparing all variants, the full GTCT model attained the highest accuracy (0.975) and AUC (0.982), thus showing superior robustness in the detection of sophisticated and evolving financial fraud patterns. Full article

In recent years, blockchain technology has gained widespread attention for its immutable and distributed ledger mechanism that ensures security and transparency among all participants. However, the decentralized nature of the blockchain network consequently presents its unique challenges in detecting fraudulent activities that may be executed by malicious actors. The traditional detection methods, such as rule-based systems, may not be sufficient to capture the complex and evolving nature of these activities. This paper explores the application of machine learning and transaction graph representation to detect suspicious accounts on the World Asset Exchange (WAX) blockchain. By leveraging dynamic subgraph embedding generation and contrastive representation learning, the proposed approach primarily targets the identification of suspicious transaction behaviors indicative of fraudulent activity. The contrastive representation learning approach enhances the generation of subgraph embeddings through a contrastive loss function to effectively discriminate between potentially fraudulent and legitimate transaction behavior by optimizing the distances in the embedding space. This process significantly enhances the classification accuracy, particularly for the imbalanced minority class often seen in fraud detection scenarios. The results of the experimental validations indicate the presence of potentially fraudulent activities and highlight the effectiveness of the anomaly ranking mechanism in identifying new, previously unseen cases. Full article

Insurance fraud, characterized by false or exaggerated claims, is a major economic crime worldwide, undermining trust between insurance companies and their customers. Detecting these cases is a priority issue nowadays. This paper presents a fuzzy inference system for the early identification of suspicious claims in the compulsory motor liability insurance market. The study focuses exclusively on cases involving two privately owned passenger cars where no personal injury, but only property damage, occurred. A Mamdani-type inference system was created, using simple independent input parameters: the value (in EUR) and the age of the vehicle (in years) and the payment period of the insurance contract. The last parameter was introduced as a qualitative factor. These were linked to the risk level resulting from the characteristics of the vehicles involved in the incident. For this purpose, real insurance data were used. Full article

Reliable financial reporting is critical for maintaining market confidence and guiding stakeholders’ decision-making, yet traditional audit methods often fail to detect sophisticated fraud schemes that are hidden within large volumes of transactional data. This systematic literature review synthesizes 43 empirical and theoretical studies published between 2010 and 2024 that utilize data analytics techniques for the prevention and detection of fraud in financial statements. Following the PRISMA guidelines, we conducted a four-phase review—identification, screening, eligibility assessment, and inclusion—to ensure transparency and reproducibility. Our analysis categorizes techniques into supervised machine learning classifiers (e.g., decision trees and neural networks), statistical anomaly detection methods, network-based analyses, and real-time monitoring frameworks. We evaluate each approach’s comparative effectiveness, highlight persistent challenges such as data imbalance, model interpretability, and governance constraints, and also trace evolving methodological trends over time. The review reveals that integrating predictive analytics and continuous monitoring into accounting information systems can transform audits from reactive investigations into proactive fraud prevention mechanisms. We conclude by proposing a future research agenda focusing on developing explainable AI models for audit applications, establishing robust data governance frameworks to support automated monitoring, and conducting longitudinal field studies to assess the real-world impact of analytics-driven controls. Full article

Generative artificial intelligence (AI) and persistent empirical gaps are reshaping the cyber threat landscape faster than Zero-Trust Architecture (ZTA) research can respond. We reviewed 10 recent ZTA surveys and 136 primary studies (2022–2024) and found that 98% provided only partial or no real-world validation, leaving several core controls largely untested. Our critique, therefore, proceeds on two axes: first, mainstream ZTA research is empirically under-powered and operationally unproven; second, generative-AI attacks exploit these very weaknesses, accelerating policy bypass and detection failure. To expose this compounding risk, we contribute the Cyber Fraud Kill Chain (CFKC), a seven-stage attacker model (target identification, preparation, engagement, deception, execution, monetization, and cover-up) that maps specific generative techniques to NIST SP 800-207 components they erode. The CFKC highlights how synthetic identities, context manipulation and adversarial telemetry drive up false-negative rates, extend dwell time, and sidestep audit trails, thereby undermining the Zero-Trust principles of verify explicitly and assume breach. Existing guidance offers no systematic countermeasures for AI-scaled attacks, and that compliance regimes struggle to audit content that AI can mutate on demand. Finally, we outline research directions for adaptive, evidence-driven ZTA, and we argue that incremental extensions of current ZTA that are insufficient; only a generative-AI-aware redesign will sustain defensive parity in the coming threat cycle. Full article

Accurate differentiation between Coilia brachygnathus and Coilia nasus is imperative for the effective management of fisheries, the conservation of aquatic ecosystems, and the mitigation of commercial fraud. Current morphological identification remains challenging due to their high morphological similarity—particularly for processed samples—while conventional molecular methods often lack the speed or specificity required for field applications or high-throughput screening. In this study, a novel integrated approach was developed and validated, combining TaqMan quantitative real-time PCR (qPCR). for precise genotyping of C. brachygnathus and C. nasus with Recombinase Polymerase Amplification coupled with Lateral Flow Dipstick (RPA-LFD) for rapid on-site screening. First, species-specific RPA-LFD assays were designed to target the mitochondrial COI gene sequence. This enabled visual detection within 10 min at 37 °C, with a sensitivity of 10² copies/μL, and required no complex equipment. A dual TaqMan MGB qPCR assay was further developed by validating stable differentiating SNPs (chr21:3798155, C/T) between C. brachygnathus and C. nasus, using FAM/VIC dual-labeled MGB probes. Results showed that this assay could distinguish the two species in a single tube: for C. brachygnathus, Ct values in the FAM channel were significantly earlier than those in the VIC channel (ΔCt ≥ 1), with a FAM detection limit of 125 copies/reaction; for C. nasus, only VIC channel amplification was observed, with a detection limit as low as 12.5 copies/reaction. Validation with 171 known tissue samples demonstrated 100% concordance with expected species identities. This integrated approach effectively combines the high accuracy and quantitative capacity of TaqMan qPCR for confirmatory laboratory genotyping with the speed, simplicity, and portability of RPA-LFD for initial field or point-of-need screening. This reliable, efficient, and user-friendly technique provides a powerful tool for resource management, biodiversity monitoring, and ensuring the authenticity of high-quality C. brachygnathus and C. nasus. Full article