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Image copy‐move forgery, where an image region is copied and pasted within the same image, is a simple yet widely employed manipulation. In this paper, we rethink copy‐move forgery detection from the perspective of multi‐task learning and summarize two characteristics of this problem: (1) Homology and (2) Manipulated traces. Consequently, we propose a multi‐task forgery detection network (MTFDN) for image copy‐move forgery localization and source/target distinguishment. The network consists of a hard‐parameter sharing feature extractor, global forged homology detection (GFHD) and local manipulated trace detection (LMTD) modules. The difference of feature distribution between the GFHD module and the LMTD module is significantly reduced by sharing parameters. Experimental results on several benchmark copy‐move forgery datasets demonstrate the effectiveness of our proposed MTFDN. [ABSTRACT FROM AUTHOR]

A method for banknote identification has been developed. As a prerequisite for this process, an ATM stores records captured from accepted banknotes, including customer's information for identification. The proposed method is implemented when a counterfeit banknote is detected. The ATM identifies a record corresponding to the counterfeit banknote among the stored records, assisting in the identification of the customer who used it. This identification is carried out by exploiting characteristic differences between counterfeit and genuine banknotes. These differences are defined in terms of positions and magnitudes of variations. An experiment using simulated banknotes was conducted to assess the effectiveness of the proposed method. Additionally, another experiment involving digitally synthesized banknotes was conducted to determine the performance limits of the proposed method. [ABSTRACT FROM AUTHOR]

Key Clinical Message: When self‐administration with counterfeit or mislabeled medicine is suspected, comprehensive laboratory analysis should be preferred over immunoassay screening to avoid false negative results. Carisoprodol, which was formerly a popular muscle relaxant drug in many countries, has reappeared on illegal drug markets, and may cause an itching, purple‐colored rash, even after a single dose. [ABSTRACT FROM AUTHOR]

Federated learning (FL), celebrated for its privacy‐preserving features, has been revealed by recent studies to harbour security vulnerabilities that jeopardize client privacy, particularly through data reconstruction attacks that enable adversaries to recover original client data. This study introduces a client‐level handwriting forgery attack method for FL based on generative adversarial networks (GANs), which reveals security vulnerabilities existing in FL systems. It should be stressed that this research is purely for academic purposes, aiming to raise concerns about privacy protection and data security, and does not encourage illegal activities. Our novel methodology assumes an adversarial scenario wherein adversaries intercept a fraction of parameter updates via victim clients’ wireless communication channels, then use this information to train GAN for data recovery. Finally, the purpose of handwriting imitation is achieved. To rigorously assess and validate our methodology, experiments were conducted using a bespoke Chinese digit dataset, facilitating in‐depth analysis and robust verification of results. Our experimental findings demonstrated enhanced data recovery effectiveness, a client‐level attack and greater versatility compared to prior art. Notably, our method maintained high attack performance even with a streamlined GAN design, yielding increased precision and significantly faster execution times compared to standard methods. Specifically, our experimental numerical results revealed a substantial boost in reconstruction accuracy by 16.7%, coupled with a 51.9% decrease in computational time compared to the latest similar techniques. Furthermore, tests on a simplified version of our GAN exhibited an average 10% enhancement in accuracy, alongside a remarkable 70% reduction in time consumption. By surmounting the limitations of previous work, this study fills crucial gaps and affirms the effectiveness of our approach in achieving high‐accuracy client‐level data reconstruction within the FL context, thereby stimulating further exploration into FL security measures. [ABSTRACT FROM AUTHOR]

Summary: This article presents a tracing mechanism for group signatures answering the security threats of malicious authorities and users' forgeries. The proposal weakens the high trust placed on the centralized tracing party in previous group signatures by decentralizing tracing power using a multiple tracer setting and limiting the tracers' access using attribute‐based encryption and the requirement of the group manager's agreement. We allow the group manager to control tracers identifying his group users. Instead of a centralized tracer, our setting has multiple tracers possessing attribute sets. Thus, after getting the group manager's permission, a tracer should satisfy the access policy in a given signature to identify the signer. On the other hand, our group signature scheme decentralizes the tracing key generation and removes the group manager's tracing ability. Thus, it ensures that only the attribute‐satisfying and permitted tracers can identify the signer. Moreover, this article delivers security against malicious users. It presents a verification process of access policy of the signatures to prevent users from utilizing invalid attributes for signing. In addition, the article delivers a collaborative tracing mechanism to satisfy attribute sets that a tracer fails to fulfill alone for identifying a signer. Thus, our tracing mechanism ensures security against malicious authorities and group users in group signatures. The article gives the general construction of the scheme and discusses the security. [ABSTRACT FROM AUTHOR]

Videos are considered as most trustworthy means of communication in the present digital era. The advancement in multimedia technology has made video content sharing and manipulation very easy. Hence, the video authenticity is a challenging task for the research community. Video forensics refer to uncovering the forgery traces. The detection of spatiotemporal object‐removal forgery in surveillance videos is crucial for judicial forensics, as the presence of objects in the video has significant information as legal evidence. The author proposes a passive max–median averaging motion residual algorithm for revealing the forgery traces, successfully giving visible object‐removal traces followed by a deep learning approach, YOLO‐V8, for forged region localization. YOLO‐V8 is the latest deep learning model, which has a wide scope for real‐time application. The proposed method utilizes YOLO‐V8 for object‐removal forgery in surveillance videos. The network is trained on the SYSU‐OBJFORG dataset for object‐removal forged region localization in videos. The fine‐tuned YOLO‐V8 successfully classifies and localizes the object‐removal tampered region with an F1‐score of 0.99 and a precision of 0.99. The observed high confidence score of the bounding box around the forged region makes the model reliable. This fine‐tuned YOLO‐V8 would be a better choice in real‐time applications as it solves the complex object‐based forgery detection in videos. The performance of the proposed system is far better than the existing deep learning approach. [ABSTRACT FROM AUTHOR]

"Lookalike" products, including counterfeits and copycats, are increasingly common in today's global marketplace. Four lab experiments were conducted involving 653 participants, and data were analyzed through SPSS 26. Based on social representation (SR) theory and self‐construal theory, this research examines how advertising for original brands impacts purchase intent for counterfeits and copycats differently. The results suggest that ads for the original product using value‐expressive (vs. utilitarian) appeals result in lower intent to purchase copycat versions. However, ads for the original product using utilitarian (vs. value‐expressive) appeals lead to lower purchase intent for counterfeit versions. The results also show that consumer need fulfillment mediates the interactive effect between ad appeal type and product type. The current research provides useful insights for marketers or retailers grappling with ongoing competitive threats posed by lookalike products and underscores the strategic utilization of various advertising tactics to reduce the demand for copycats and counterfeits. [ABSTRACT FROM AUTHOR]

Counterfeiting has become a major social problem because of the consequences for consumers, businesses and governments. The objective is to describe the current state of counterfeiting research, define the main thematic areas, identify gaps and highlight key findings. A bibliometric analysis of the growing number of publications addressing the counterfeiting problem in the existing literature is conducted. The articles are taken from a search of the Scopus database. After excluding some scientific‐technological subareas, 1984 papers are analyzed, to which 23 outstanding papers were added at a later stage. In more recent years, most of the articles focus on the development of technologies. Four conceptual areas have been identified: technological development, social behavior, economic aspects and legal aspects. The latter two are the least studied. Also identified in the social area are two topics of interest that have been little addressed, namely, the effect of prior purchase experience and the development of theories describing consumer behavior towards counterfeits. The value derived from this research is the identification of new lines of research with the economic and legal area, it would be of interest to initiate a research program that studies what proportion of counterfeits are sanctioned, to which interest group the sanction is directed and if the sanction is proportional to the damage caused to society and the company. In addition, with regard to the study of counterfeit consumer behaviour, its development can provide information to guide the definition of public awareness strategies and help to adapt companies' marketing campaigns. [ABSTRACT FROM AUTHOR]

We have successfully developed an advanced and highly secure anti‐counterfeiting device based on quantum‐dot light‐emitting diodes (QLEDs). This novel device exhibits several key attributes, including affordability, non‐replicability, ease of detection, and exceptional security. By incorporating both electroluminescence and photoluminescence modes, our device offers versatility in satisfying different levels of anti‐counterfeiting needs. To enhance its security further, we have integrated physically unclonable functions (PUFs) and combined them with a pattern recognition system. This integration ensures the device's uniqueness and enables it to meet the most stringent anti‐counterfeiting requirements. The achieved results demonstrate the significant potential of our device in combating counterfeiting and safeguarding various industries. [ABSTRACT FROM AUTHOR]

Upconversion (UC) phosphors exhibiting luminescence color tuning (LCT) through variations in infrared excitation intensity offer great potential for high‐security anti‐counterfeiting applications. However, the current LCT capability is limited to high excitation intensities, hindering developments of non‐invasive counterfeit detection. In this study, two orders of magnitude reduction are achieved in excitation intensities for LCT in YF3:Yb/Er, accomplished by attaining an unprecedentedly efficient three‐photon excited red emission for mixing with the two‐photon excited green emission. To enable this breakthrough, deoxygenation techniques are employed during sample preparations, which surprisingly prevent concentration quenching of Yb3+ ions, facilitating efficient three‐photon excitation of the red emission for Yb3+ concentrations ≥ 30% even at excitation intensities as low as 10 mW cm−2. At excitation intensities of 100 mW cm−2, the three‐photon excitation contributes to 91–94% of the red emission, resulting in an 11–17‐fold increase in the red‐to‐green intensity ratio. This low‐excitation‐induced LCT, shifting from green to orange, showcases its potential for anti‐counterfeiting. Furthermore, present YF3:Yb/Er phosphors demonstrate an impressive UC quantum yield of 7.8%, surpassing the popular NaYF4:Yb/Er phosphor (5.6%) under the same excitation intensity of 31.8 W cm−2. These findings represent a significant advancement in highly efficient UC fluoride phosphors, promising diverse applications across various fields. [ABSTRACT FROM AUTHOR]

The color filter array of the camera is an effective fingerprint for digital forensics. Most previous color filter array (CFA)-based forgery localization methods perform under the assumption that the interpolation algorithm is linear. However, interpolation algorithms commonly used in digital cameras are nonlinear, and their coefficients vary with content to enhance edge information. To avoid the impact of this impractical assumption, a CFA-based forgery localization method independent of linear assumption is proposed. The probability of an interpolated pixel value falling within the range of its neighboring acquired pixel values is computed. This probability serves as a means of discerning the presence and absence of CFA artifacts, as well as distinguishing between various interpolation techniques. Subsequently, curvature is employed in the analysis to select suitable features for generating the tampering probability map. Experimental results on the Columbia and Korus datasets indicate that the proposed method outperforms the state-of-the-art methods and is also more robust to various attacks, such as noise addition, Gaussian filtering, and JPEG compression with a quality factor of 90. [ABSTRACT FROM AUTHOR]

Summary: The ability to detect image forgeries is a crucial component in solving many problems, especially social ones that arise in legal proceedings, on Facebook, and so forth. Generally, forgery detection attempts to predict the artifacts through evaluating the differences in texture properties of an image. One division of an image is copied in a comparable image, typically at a different position, in a process known as copy move forgery. The two main types of copy move forgery prediction are keypoint‐based and block‐based techniques. While the block‐based approach takes more time, the keypoint‐based model performs poorly. The goal of this research was to develop a method for detecting image counterfeiting called the Squirrel Henry Gas Solubility Optimization algorithm‐based Deep Maxout Network (SHGSO‐based DMN). Here, Steerable Pyramid Transform (SPT) decomposition is done in order to obtain quantity of multi‐scale and multi‐oriented sub bands. For the purpose of detecting forgeries, the Deep Maxout Network which was constructed using the SHGSO model is deployed. With accuracy of 0.9507, True Negative Rate (TNR) of 0.9568, and True Positive Rate (TPR) of 0.9517, the new method outperformed other current forgery detection techniques. [ABSTRACT FROM AUTHOR]

During the COVID‐19 pandemic, the United States released the CDC‐issued vaccination card to document the status of immunizations. It eventually was used as a verification system to allow patrons to safely access public venues. This is/was apparently a simple printed piece of paper with minimal security features which made it an easy target for counterfeiters with little expertise required. Those unvaccinated who wished to enter areas that required proof of the COVID‐19 vaccine were soon finding alternative illegal methods to do so which led to public health concerns. This research was divided into two parts. Part A aimed to forensically identify the security features with a video spectral comparator and a stereomicroscope. Ten "known" blank cards were collected directly from dispensaries that administered the vaccine and 68 "unknowns" from individuals who received the COVID‐19 vaccine. Part B aimed to evaluate the authentication systems employed by countries outside the United States to propose a model on how the vaccination card could be improved and what security features should be included for future instances requiring proof of immunization. The results revealed the CDC document was produced with minimal security features making the document prone to counterfeiting. Furthermore, based on the information collected from other countries' vaccination certificates, it is recommended to develop a dual authentication system, with digital and printable aspects, that contains a QR code linked to a database. Other security features may include a non‐optical brightener substrate, security fibers, a watermarked logo, offset printing, and a geographical color‐coded system. [ABSTRACT FROM AUTHOR]

Persistent luminescent materials can continue to emit light after stopping excitation, and their stored energy is gradually released during the decay time after persistent emitting, which makes the color and luminance changes in slow decay and plays an important role in protecting information and combating forgery. When combined with other anti‐counterfeiting technologies to form a multi‐modal and multi‐process anti‐counterfeiting design, it perfectly hides the real information and has a broad application prospect in anti‐counterfeiting logo design. A brief overview of the design of three different persistent luminescence‐based anti‐counterfeiting applications in unimodality to achieve information encryption in the time dimension. The energy stored in the trap is studied to be released by different physical stimuli and applied to stimulus‐like responsive persistent luminescent materials, and the design of persistent luminescence anti‐counterfeiting applications based on many different processes in dual‐modality, obtaining the dynamic anti‐counterfeiting effect of multiple processes in multiple modes. This work reviews the research background of persistent luminescent materials in the field of anti‐counterfeiting in recent years, compares the important breakthrough progress of persistent luminescent materials applied to anti‐counterfeiting research, and finally puts forward the current problems, future challenges, and development directions. [ABSTRACT FROM AUTHOR]

Envy fuels luxury consumption—both real and fake. Through three studies, we show that benign envy motivates online shoppers to purchase authentic luxury products, whereas malicious envy encourages counterfeiting. By triggering envy through its antecedents of deservingness and pride, we show that benign (malicious) envy motivates a general tendency towards moral (immoral) behavior, which has distinct downstream effects on consumer attitude towards luxury. Benign envy increases belief in the moral virtue of hard work, thereby inspiring consumers to aspire and strive for original luxury products. Whereas malicious envy triggers moral disengagement—dissolving the moral dilemma associated with counterfeiting. Malicious envy also makes consumers appraise luxury corporations as immoral—fueling the desire to punish the brand through counterfeiting. The effects hold for both brand‐generated envy and incidental envy. The findings have important implications for luxury branding to help control consumer demand for counterfeits. [ABSTRACT FROM AUTHOR]

The widespread dissemination of high-fidelity fake faces created by face forgery techniques has caused serious trust concerns and ethical issues in modern society. Consequently, face forgery detection has emerged as a prominent topic of research to prevent technology abuse. Although, most existing face forgery detectors demonstrate success when evaluating high-quality faces under intra-dataset scenarios, they often overfit manipulation-specific artifacts and lack robustness to postprocessing operations. In this work, we design an innovative dual-branch collaboration framework that leverages the strengths of the transformer and CNN to thoroughly dig into the multimodal forgery artifacts from both a global and local perspective. Specifically, a novel adaptive noise trace enhancement module (ANTEM) is proposed to remove high-level face content while amplifying more generalized forgery artifacts in the noise domain. Then, the transformer-based branch can track long-range noise features. Meanwhile, considering that subtle forgery artifacts could be described in the frequency domain even in a compression scenario, a multilevel frequency-aware module (MFAM) is developed and further applied to the CNN-based branch to extract complementary frequency-aware clues. Besides, we incorporate a collaboration strategy involving cross-entropy loss and single center loss to enhance the learning of more generalized representations by optimizing the fusion features of the dual branch. Extensive experiments on various benchmark datasets substantiate the superior generalization and robustness of our framework when compared to the competing approaches. [ABSTRACT FROM AUTHOR]

Generative Adversarial Networks (GANs) emerged thanks to the development of deep neural networks. Forgery images generated by various variants of GANs are widely spread on the Internet, which may be damage personal credibility and cause huge property losses. Thus, numerous methods are proposed to detect forgery images, but most of them are designed to detect forgery faces. Therefore, a method to detect forgery images of various scenes is proposed. In this work, central difference convolution and vanilla convolution (CDC‐Mix) are mixed after considering the depth and width features of neural networks and analyzing the influence of attention on network performance. Based on CDC‐Mix, a separable convolution (SeparableCDC‐Mix) is proposed. The proposed method consists of three parts: (1) CDC‐Mix and SeparableCDC‐Mix are used to extract the gradient information and texture features; (2) CDCM is used to extract the multi‐scale information of the image; (3) multi‐scale fusion module (MS‐Fusion) is used to fuse the multi‐scale information from different locations of the network. A large number of experiments have been carried out on several datasets generated by GAN, and the experimental results show that the proposed method has a great improvement compared with the existing advanced methods. [ABSTRACT FROM AUTHOR]

As the importance of reliable multimedia content increases in today's society, image forensics is a growing field of research. The act of copying and pasting specific parts of an image, known as copy‐move forgery, may be utilized for illegal or unethical purposes. Just as with other vision‐related technologies, the accuracy of forensic analysis depends on having an appropriate image representation. Most existing feature extraction techniques do not accurately reflect the underlying image content leading to reduced performance. In this article, to detect the copy‐move forgery attack, the Generic Radial Harmonic Fourier Moment (GRHFM) is proposed for reliable and distinctive image representation. The algorithm has the ability to effectively manipulate the distribution of zeros to emphasize certain image regions. Additionally, the relationships between complex exponentials and trigonometric functions are exploited to efficiently compute and easily implement the transform kernels. The efficacy of the algorithm is illustrated through experiments on dense‐domain‐based matching patterns. Experimental results on five benchmarking databases prove the effectiveness of the proposed approach compared with the state‐of‐the‐art methods. According to the average scores, the proposed method demonstrates superior accuracy in overall localization performance. The F1 score, precision, and recall percentage values obtained are 92.5, 95.44, and 91.96, respectively. Robustness experiments on more challenging attacks are also conducted on FAU dataset. Results show that the proposed framework satisfies invariance to the various image variations, and thus an enhanced robustness compared to the previous methods. Moreover, the advantage of reasonable computational cost implies its potential use in real‐world forensic applications. [ABSTRACT FROM AUTHOR]

Copy‐move forgeries are a conceptual sort of image retouching that involves duplicating a portion of an image and moving it to a different position within the original image, whether by modifying the duplicated part or just moving it altogether. To identify cloned portions that have been reproduced into the same digitized image, the suggested hybrid features in this article combine using the Hessian and Raw patch features on gray‐level images. Using a suggested model that combines patch features built on key points detected by the Hessian detector on gray‐level image, localization of duplicated and pasted portions of the manipulated image were found. After using the combined features in the matching step, the parallelism condition was applied together with the random sample consensus method to eliminate mismatches. Two databases, GRIP and the image manipulation dataset (IMD), were used for the detection and characterization, and the empirical findings show that the approach was successful in achieving an F1 score 100% for the GRIP database. Additionally, with the IMD database, it produced a 92.13% F1 score. The proposed method was also shown to be effective by obtaining high F1 scores in images where noise, JPEG compression, and scaling attacks were applied to make forgery detection difficult. [ABSTRACT FROM AUTHOR]

The utilization of handwritten electronic signatures has expanded in various application scenarios, leading to an increased demand for identification. Unlike handwriting signatures, handwritten electronic signatures offer the advantage of extracting dynamic feature data, including writing pressure, velocity, and acceleration. In this study, the Fourier transform was employed to extract 18 characteristics from the time domain and frequency domain of writing pressure, velocity, and acceleration. The experimental findings revealed distinguishable differences between genuine signatures and random forgeries in writing pressure. However, no statistically significant differences were observed in writing velocity and writing acceleration. Moreover, significant differences were detected in most characteristics when comparing genuine signatures with freehand imitation forgeries and tracing imitation forgeries. The canonical discriminant analysis was performed between the genuine and Non‐genuine signatures; the cross‐validation estimated the discriminating power of these characteristics with a satisfactory result. The study proposed a new approach to analyzing handwritten electronic signatures using time‐domain and frequency‐domain characteristics and demonstrated its effectiveness in the examination. [ABSTRACT FROM AUTHOR]

Tridentinosaurus antiquus represents one of the oldest fossil reptiles and one of the very few skeletal specimens with evidence of soft tissue preservation from the Cisuralian (Early Permian) of the Italian Alps. The preservation and appearance of the fossil have puzzled palaeontologists for decades and its taphonomy and phylogenetic position have remained unresolved. We reanalysed T. antiquus using ultraviolet light (UV), 3D surface modelling, scanning electron microscopy coupled with energy dispersive spectroscopy (SEM‐EDS), micro x‐ray diffraction (μ‐XRD), Raman and attenuated total reflectance Fourier transformed infrared (ATR‐FTIR) spectroscopy to determine the origin of the body outline and test whether this represents the remains of organically preserved soft tissues which in turn could reveal important anatomical details about this enigmatic protorosaur. The results reveal, however, that the material forming the body outline is not fossilized soft tissues but a manufactured pigment indicating that the body outline is a forgery. Our discovery poses new questions about the validity of this enigmatic taxon. [ABSTRACT FROM AUTHOR]

The quality of counterfeit items has increased dramatically, with modern global manufacturing being able to duplicate the materials, construction, and visual features of items. Detection of fraudulent coinage can parallel authentication of food, beverages, and manufactured goods by studying product‐inherent features. Counterfeit detection is performed by comparing an Example group with a Questioned group. A model is developed for both groups using standard tests on individual pieces. Coin weight is used here as an illustration. The model should also follow the natural science of the system. In this case, the manufacturing process variation is known and steady, and the underlying distribution is known or can be determined from authentic pieces. The proposed detection method uses testing of many individual pieces, then using reverse‐quality‐engineering methods to identify possible sources. This strategy looks at the variation between individual pieces to determine the process capability of a machine, assembly line, or plant to create product consistency for a manufacturer. Fraudulent items may be manufactured within specification, but demonstrate a manufacturing process capability different than that of the authentic manufacturer. In this report, we examine the model previously reported and use reconstruction techniques to re‐create the evidence set to validate the model, increase model accuracy, and confirm the conclusion previously reached, showing that the Questioned set is likely over 37% non‐conforming by weight. In this case, the decision outcome of the analysis was improved by using additional methods not included in the modeling software package originally used. [ABSTRACT FROM AUTHOR]

Document forgery is a significant issue in Korea, with around ten thousand cases reported every year. Analyzing paper plays a crucial role in examining questionable documents such as marketable securities and contracts, which can aid in solving criminal cases of document forgery. Paper analysis can also provide essential insights in other types of criminal cases, serving as an important clue for solving cases such as the source of a blackmail letter. The papermaking process generates distinct forming fabric marks and formations, which are critical features for paper classification. These characteristics are observable under transmitted light and are created by the forming fabric pattern and the distribution of pulp fibers, respectively. In this study, we propose a novel approach for paper identification based on hybrid features. This method combines texture features extracted from images converted using the gray‐level co‐occurrence matrix (GLCM) approach and a convolutional neural network (CNN), with another set of features extracted by the CNN using the same images as input. We applied the proposed method to classification tasks for seven major paper brands available in the Korean market, achieving an accuracy of 97.66%. The results confirm the applicability of this method for visually inspecting paper products and demonstrate its potential for assisting in solving criminal cases involving document forgery. [ABSTRACT FROM AUTHOR]

Background: Rotary nickel–titanium (NiTi) instruments are made to exacting standards and are costly to manufacture, and quality control is paramount. Consequently, unauthorized factories make counterfeit instruments that are less expensive and may therefore be attractive to dentists. Little information exists about the metallurgy and manufacturing quality of such instruments. There is the potential for counterfeit instruments to be at higher risk of fracture during treatment, thereby compromising clinical outcomes. The aim of this study was to evaluate physical and manufacturing properties of genuine and counterfeit ProTaper Next™ and Mtwo® rotary NiTi instruments. Methods: This study investigated the metallurgical properties, manufacturing quality, microhardness and number of cycles to failure of two commonly used rotary NiTi systems and compared them with counterfeit products purporting to be genuine articles. Results: Counterfeit instruments were found to be inferior in manufacturing standards and were less resistant to cyclic fatigue when compared to genuine instruments. Conclusions: Counterfeit rotary NiTi instruments may be less efficient at preparing root canals and may be at higher risk of fracture during endodontic treatment. Dentists must be aware that, although less expensive, counterfeit instruments may be of dubious manufacturing quality and at higher risk of fracture if used in patients. © 2023 Australian Dental Association. [ABSTRACT FROM AUTHOR]

Consumers increasingly use online pharmacies. However, illicit online pharmacies endanger consumer welfare with unapproved and counterfeit drugs. By linking Food and Drug Administration (FDA) warning letter content with observations of the 1108 websites cited in those letters, we identify factors associated with FDA‐requested compliance and active/inactive website status. One in five online pharmacies failed to comply with FDA recommendations. Findings support the cost/benefit, social norm, and instrumental theories of compliance. Compliance declines with cost (e.g., when the FDA requests changes to more drug categories). Organized crime's social norms encourage law breaking; thus, illicit pharmacies comply less than other online pharmacies. Instrumental theory suggests perceived severity and certainty of penalties influences compliance. In support, when political will and attention focused on the opioid crisis, the FDA cited primarily opioid‐related violations and these opioid‐related letters increased compliance and website closures. Finally, we recommend improved consumer protection efforts to address internet pharmaceutical distribution challenges. [ABSTRACT FROM AUTHOR]

Creating security materials that carry distinct information with numerous optical characteristics enables enhanced anticounterfeiting levels to deter forgery ranging from currencies to pharmaceuticals. Circularly polarized long afterglow (CPLA) has attracted extensive attention in anticounterfeiting owing to its multiple and unusual optical properties (i.e., long afterglow and circular polarization), introducing intensity variation into the time dimension. However, constructing a visualized CPLA—meeting the requirements toward practical applications—is still a challenge. Here, a cementation‐coupling strategy is designed to combine chiral photonic membranes with phosphor films for building full‐colored CPLA architectures. The CPLA systems achieve desired performance with a largest afterglow dissymmetry value (gCPLA) of −0.67 and a longest visualization time (tvis) of 40 min simultaneously, satisfying practical demands for information security. As‐prepared CPLA materials are further patterned to work as optical labels in security fields. The resulting multidimensional variations in both time and intensity increase the complexity of information encryption, offering potentials for advanced security technologies. [ABSTRACT FROM AUTHOR]

With the rapid development of manipulation technologies, the generation of Deep Fake videos is more accessible than ever. As a result, face forgery detection becomes a challenging task, attracting a significant amount of attention from researchers worldwide. However, most previous work, consisting of convolutional neural networks (CNN), is not sufficiently discriminative and cannot fully utilise subtle clues and similar textures during the process of facial forgery detection. Moreover, these methods cannot simultaneously consider accuracy and time efficiency. To address such problems, we propose a novel framework named FPC‐Net to extract some meaningful and unnatural expressions in local regions. This framework utilises CNN, long short‐term memory (LSTM), channel groups loss (CG‐Loss) and adaptive feature fusion to detect face forgery videos. First, the proposed method exploits spatial features by CNN, and a channel‐wise attention mechanism is employed to separate channels. Specifically, with the help of channel groups loss, the channels are divided into two groups, each representing a specific class. Second, LSTM is applied to learn the correlation of spatial features. Finally, the correlation of features is mapped into other latent spaces. Through a lot of experiments, the results are that the detection speed of the proposed method reaches 420 FPS and the auc scores achieve best performance of 99.7%, 99.9%, 94.7%, and 82.0% on Raw Celeb‐DF, Raw Face Forensics++, F2F and NT datasets respectively. The experimental results demonstrate that the proposed framework has great time efficiency performance while improving the detection performance compared with other frame‐level methods in most cases. [ABSTRACT FROM AUTHOR]

The combined effect of coercion (public and private pressure), self‐interest (competitive advantage) and conviction (intrinsically motivated or genuine) explain why environmental issues have become a key priority for companies. While research has explored coercion and competitive advantage, the role of conviction has received little attention. This paper aims to address this gap. Conviction, which has been correlated with institutional and individual drivers, offers more stable results and a potential multiplier effect as good examples are disseminated by imitation throughout an industry. While the role of imitation has drawn increasing attention of business management studies, it has received scant attention in research on sustainability. This is the second gap this paper aims to address, contributing to the literature on sustainability in two ways: firstly, we will further explore the role of conviction in environmental sustainability; secondly, employing a compartmental model of epidemic propagation, used for other social phenomena, we model the role genuine examples by leaders can play within an industry and the dissemination of good behavior by imitation. Defining genuine conviction as the voluntary mitigation of the externalities of a firm's operations beyond that required by law or the market, substantiated in the responsible behavior of its leaders, our model discovers patterns that policymakers could use to create a "sustainability epidemic". We suggest mathematical models of epidemics can be applied to sustainability, offering a fresh perspective on the phenomenon of imitation, allowing us to discover how some cultural and social habits can be used. [ABSTRACT FROM AUTHOR]

This article looks at how the biometric surveillance of migrants in the European Union interweaves border‐passing with the embodied capacity to pass as, or against, one's bureaucratic image. Despite biometrics' promise to eliminate photographic mediation, photo IDs are embedded in biometric regulation, which further entangles bodies and photographs. Based on ethnographic research in the Turkish city of Izmir, the migrant hotspot of Chios in Greece, and Athens' counterfeit document market, this article examines the material, embodied, and performative experiments migrants employ to pass borders while negotiating their bureaucratic images (both authentic and fake). Photographs emerge as a regime of practice through which European citizenship is enacted and exercised. [ABSTRACT FROM AUTHOR]

The advancements in Image editing techniques produce realistic‐looking artificial images with ease. These images can easily circumvent the forensic systems making the authentication process more tedious and difficult. To overcome this problem, we introduce a modern convolutional neural network (CNN) named ForensicNet, inspired by the recent developments in computer vision. The three major contributions of our CNNs are inverted bottleneck, separate downsampling layers, and using depth‐wise convolutions for mixing information in the spatial dimension. The inverted bottlenecks help improve accuracy and reduce network parameters/FLOPs. The separate downsampling layers help converge the network. The normalization layers also help stabilize training whenever the spatial resolution is changed. The depth‐wise convolution is a grouped convolution where the number of groups and channels are the same. The experiments show that ForensicNet outperforms the state‐of‐the‐art methods by a large margin. [ABSTRACT FROM AUTHOR]

Face forgery represented by DeepFake technique has raised severe societal concerns. Due to the different scales of tampering traces and the different resolutions of face images, adopting common processing pipelines and standard form of convolutional neural networks (CNNs) will lead to problems such as omission, redundancy, and bias when extracting key discriminative features. To solve the above issues, unlike most existing methods that treat face forensics as a vanilla binary classification task, the authors instead reformulate it as a multi‐scale object detection problem and propose a novel framework called MSFRNet based on multi‐scale feature extraction. Concretely, to alleviate the issues of features omission and redundancy, the authors construct a two‐stream prediction network, where the shallow branch discovers small‐scale objects such as tiny noise by capturing low‐level features with higher resolution and more details, while the deep stream exploits larger receptive fields to detect large‐scale blocky artefacts. Moreover, a multi‐scale feature extraction module is designed to enrich feature representations in each stream. To solve the problem of features bias and ensure that unbiased feature representations are learned, more appropriate data augmentation approaches are proposed by introducing counterfactual causal reasoning. Extensive experiments demonstrate that our framework outperforms most ordinary binary classifiers and achieves positive performance. [ABSTRACT FROM AUTHOR]

SUMMARY: Due to the tremendous growth of video editing software, it has become extremely simple to introduce malicious content by manipulating multimedia data. This may include modification of videos either by adding or deleting selective frames with malicious intentions. Hence, it is essential to find the forged frames of the videos by introducing efficient and reliable video forensic methods. This article presents an automatic intra‐frame video forgery detection strategy based on a hybrid optimization tuned deep‐convolutional neural network (deep‐CNN) classifier. The significance of the proposed method lies in developing the proposed raven‐finch optimization algorithm that tunes the weights of the deep‐CNN to exhibit enhanced detection accuracy. The proposed raven‐finch optimization algorithm combines raven search agents and finches search agents, possessing the benefits of both search agents. The features of the input video frames act as the input to the deep‐CNN classifier that detects forgery. The performance of the proposed raven‐finch‐based deep CNN method is analyzed in terms of the performance indices, such as accuracy, sensitivity, and specificity. It is attained to be 97.56%, 95.48%, and 96.38%, respectively, which shows the superiority of the proposed method for intra‐frame forgery detection. [ABSTRACT FROM AUTHOR]

A review of periodical literature for 2021 is presented, with topics that include private-order institution in global trade, forgery in late Georgian London, and patenting behaviour during Britain's industrial revolution.

Copy–move forgery poses a significant threat to social life and has aroused much attention in recent years. Although many copy‐move forgery detection (CMFD) methods have been proposed, the most existing CMFD methods are short of adaptability in detecting images, which leads to the limitation on detection effects. To solve this problem, the paper proposes a novel keypoint‐based CMFD method: second‐keypoint matching and double adaptive filtering (SMDAF). Motivated by image matching based on keypoint, the second‐keypoint matching method is designed to match keypoints extracted from copy–move forgery images, which can be used for both the single‐CMFD and the multiple‐CMFD. Then, a double adaptive filter (DAF) based on the AdaLAM algorithm and the KANN‐DBSCAN clustering algorithm to filter wrong keypoint matches adaptively are proposed, according to the distinct distribution of keypoints in each image. Finally, the forgery regions are presented by finding their convex hulls and padding them. Compared with existing methods, extensive experiments show that the SMDAF method significantly provides more efficiency in detecting images under simulated real‐world conditions, has better robustness when facing images with different post‐treatment attacks, and is more effective in distinguishing images that look copy–move forged but are real. [ABSTRACT FROM AUTHOR]

One of the most notable events in the history of palaeoanthropology was the description of 'Piltdown Man', a hoax that took 40 years to uncover. At the centre of this episode was Sir Arthur Smith Woodward, who was duped by the 'discoverer' and fossil forger, Charles Dawson. Smith Woodward never doubted the authenticity of this find and died before the dénouement of the Piltdown episode. His last major work was a summary of Piltdown Man and its associated 'science'. The Earliest Englishman is well‐written and crafted, and still is worthy of being read at the present day, 70 years since the forgery was exposed. [ABSTRACT FROM AUTHOR]

Source printer identification represents an interesting modality for document forgery detection. Establishing the identity of the printer that was employed to print a questioned document allows concluding its authenticity. This paper investigates the effectiveness of deep visual features (learned using convolutional neural networks) in characterization of the source printer. Images of printed documents are divided into small patches as well as characters for extraction of features. An off‐the‐shelf recognition engine is also integrated, allowing experiments in text‐dependent as well as text‐independent modes. Experiments are carried out on a standard data set of documents from 20 different printers and identification rates of 95.52% and 98.06% are reported using patches and characters, respectively. Furthermore, the discriminating power of different characters, as well as their combinations, is also being studied. Unlike many existing techniques, which rely on pre‐segmented characters and report results by comparing same characters only, the proposed technique works on complete images of printed documents and reports high identification rates. [ABSTRACT FROM AUTHOR]

Counterfeiting of banknotes remains a severe threat to economic security and social stability. The characterization of banknote has mainly relied on the assessment of various security features applied to the surface of the note. However, the surface features are easy to forge and contain insufficient information to discover the source. In this paper, a novel approach for banknote characterization has been proposed by employing spectral‐domain optical coherence tomography (SD‐OCT) that can provide structural and optical features. Three groups of counterfeit Chinese 100 Yuan banknotes produced by different printing manners and one group of authentic banknotes were examined by SD‐OCT without any sample preparation and four distinct areas were selected for imaging. High‐resolution tomographic and three‐dimensional (3D) volumetric OCT images were obtained and a set of features were first revealed to characterize the banknotes qualitatively and quantitatively. The results demonstrated that SD‐OCT was effective to detect and classify different types of counterfeit banknotes and could potentially be used to link counterfeit banknotes to their sources in a fast, simple and nondestructive manner. [ABSTRACT FROM AUTHOR]

Given that counterfeiting cannot be easily eliminated, we develop four approaches by which luxury wine producers can leverage the opportunities of being counterfeited. Counterfeit wines can increase brand exposure and awareness and serve as gateways for their genuine counterparts. Genuine producers can exploit the existence of counterfeit wines to develop innovative wine‐based experiences that cannot be counterfeited and boost innovation strategies. [ABSTRACT FROM AUTHOR]

The study identified contrasting experiences of purchasing and owning luxury fashion counterfeit items: while some participants carefully considered and were ultimately satisfied with their counterfeit purchase, others impulsively purchased and later reappraised and regretted their decision. The findings underlined the importance of consumer perceptions of quality to luxury and luxury counterfeit fashion products. They showed how education about the unethical features of the counterfeit industry could change consumer behavior. [ABSTRACT FROM AUTHOR]

Information recording and encryption/decryption functions are essential due to the prevalence of counterfeiting activities and information leakage in the current age. However, the development of high‐resolution information recording and multistage information protection systems to achieve high data security levels, such as self‐erasing encrypted data and time‐controlled data handling, remains limited. Herein, inspired by the information‐recording structure of paper, a multiresponsive nanofiber‐reinforced poly(N‐isopropylacrylamide) (PNIPAM) hydrogel (NCPN hydrogel) with improved mechanical properties, solvent‐induced high‐resolution reversible information recording, self‐encryption, and multi‐decryption capabilities, is proposed. Due to the unique hydrophilic and hydrophobic structures of the hydrogel matrix, ethanol and other polar analogs can be applied as special inks to record information by changing the lower critical solution temperature to achieve the repeatable transmittance variation. The recorded information can be erased via water wiping or ethanol volatilization. Additionally, self‐encryption can be achieved and adjusted based on the ethanol volatilization time and concentration difference, and confidential information can be further decrypted in a water environment or under a thermal stimulus. Furthermore, several stable, repeatable, and fast‐response hydrogel‐based information‐recognition systems are designed and investigated. Therefore, the designed hydrogel‐based informational platform provides a universal information‐handling system allowing for the reversible recording of information, with self‐encryption and multidecryption capabilities. [ABSTRACT FROM AUTHOR]

A group of six coins from Ithaca in the British Museum collection has been analysed using a bench Bruker Artax X‐ray fluorescence spectrometer on a polished surface. The coins can be stylistically divided into two variants. The analysis highlighted that the two variants are chemically different, with one suspected of being a modern forgery. The first records of coins stylistically similar to the ones suspected of being forgeries are dated to the 19th century. This paper hypothesises that these suspected forgeries have been produced by an expert forger remelting other, more common, ancient coins. We suggest that the forger was an expert smith and also a numismatist and/or antiquarian who was possibly aware of the most recent studies dedicated to the chemical composition of ancient coins. This paper presents the hypothesis that the forger may have melted ancient coins or other artefacts to produce modern forgeries with an 'ancient' composition. [ABSTRACT FROM AUTHOR]

The availability of high‐quality, low‐price counterfeits in many luxury markets threatens the role of luxury goods as a status symbol. If those counterfeits look and feel the same as the authentic counterparts, as many professional authenticators observe, and they are available at a fraction of the price of authentic goods, why would self‐interested consumers purchase authentic luxury goods? Then, the future of luxury goods is called into question. In this paper, we propose that the presence of high‐quality, low‐price counterfeits can, surprisingly, motivate the wealthy consumers to pursue what we term as the "maximalist luxury" strategy. In the presence of these counterfeits, the wealthy can resort to signaling their status by purchasing the maximum number of luxury goods available and put their copious consumption on display, while in the absence of such counterfeits, the wealthy consumers only need to purchase the minimum number of luxury goods to stand out. This new signaling mechanism then highlights the importance of product line decisions by a luxury brand in combating counterfeits and provides a number of managerial insights about how to maintain the role of luxury goods as a status symbol through pricing, adjusting the product line, and limiting its products' functionality. [ABSTRACT FROM AUTHOR]

Summary: It is prevalent nowadays for data owners to outsource their data to the cloud. Most of the traditional searchable schemes are proposed under the honest and curious model, lacking verification of integrity and correctness of the retrieval results. Because the cloud server is not completely trustworthy and there may be improper execution of the users' retrieval requests, it is necessary to verify the retrieval results returned by the cloud server. In order to address these issues, we propose a verifiable ranked ciphertext retrieval scheme (VRCRS) based on bilinear mapping. For the purpose of ranking the keywords and improving the security of ciphertext retrieval scheme, we utilize the lucene search engine toolkit to score keywords, and improve the traditional inverted index structure for building a secure inverted index structure. By adding dummy words into the dictionary of keywords, the proposed scheme can resist the keywords statistics attack of malicious server. To ensure the correctness and integrity of returned ciphertext, we apply bilinear mapping to generate validation tags for keywords. The theoretical analysis and experimental results show that the proposed scheme is secure and efficient. VRCRS can actually identify the illegal behavior of cloud server such as tampering and forgery. [ABSTRACT FROM AUTHOR]

Drawing on the self‐discrepancy and moral decoupling theories, this study investigates how and when actual‐ideal self‐discrepancy (AISD) leads to consumers' purchase intentions toward counterfeit luxury brands. Across a survey study (Study 1: N = 531) and an experimental study (Study 2: N = 543), this research demonstrates that AISD is positively related to consumers' purchase intention toward counterfeit luxury products, and this relationship is mediated by the need for self‐enhancement. Furthermore, moral decoupling moderates this relationship, such that the mediated relationship is stronger when moral decoupling is high rather than low. The findings provide strategic insights for brand managers and policy makers to reduce demands for counterfeit products and tackle the counterfeit industry effectively. [ABSTRACT FROM AUTHOR]

The emergence of photo editing applications, like Adobe Photoshop, has manipulated the operation of digital images into a simple task. However, these manipulations of images misrepresent the content of the original image for misleading the public. Various copy move forgery detection techniques are developed, but these show less robustness on the image with noise and blurring. This article develops an optimization‐driven deep learning technique for image forgery detection. The purpose is to develop a copy‐move image forgery detection technique using a deep neuro‐fuzzy network and a newly developed optimization algorithm. Here, adaptive partitioning is adapted using a rectangular search for splitting the image into different parts. In addition, the features like local Gabor XOR pattern and Texton features are extracted from the partition. Furthermore, the forgery is detected using the deep neuro‐fuzzy network. Finally, the deep neuro‐fuzzy network training is performed using the proposed multi‐verse invasive weed optimization (MVIWO) technique. The proposed MVIWO method will be newly designed by integrating the multi‐verse optimizer and invasive weed optimization technique. Thus, the copy‐move image forgery detection is effectively performed using the proposed MVIWO‐based deep neuro‐fuzzy network. The developed MVIWO‐based deep neuro‐fuzzy network offers superior performance with the highest specificity of 93.54%, highest accuracy of 94.01%, and highest sensitivity of 97.75%. [ABSTRACT FROM AUTHOR]

This study examines the impact of idealism, possession‐based happiness, and the attitude toward the legality of counterfeits on the willingness to buy counterfeit luxury brands in Turkey and Slovenia. Data are collected from 205 consumers in Slovenia and 224 consumers in Turkey and analysed using Partial Least Square Structural Equation Modeling. The results confirm a negative impact of idealism on the attitude toward the legality of counterfeits and a positive impact of this attitude on the willingness to purchase counterfeit luxury brands. Turkish consumers show a positive effect of possession‐based happiness on the attitude toward counterfeits. The impact of idealism and possession‐based happiness on the willingness to purchase counterfeit luxury brands among Slovenian or Turkish consumers was not found. The study contributes to the literature by providing a cross‐cultural focus on specific personal antecedents, which the current literature has limited knowledge of and conflicting findings. [ABSTRACT FROM AUTHOR]

Document forgeries that involve modification of the materials used, such as ink and paper, provide evidence of any malpractices being performed. Forensic specialists use different techniques to identify and classify these samples; however, the most preferred method is to use nondestructive techniques to avoid any potential damage to the original specimen under investigation. Hyperspectral imaging has already been explored in several application domains and used as a powerful method in forensic investigations to extract information about the materials under examination. To precisely classify the material information and utilize the hyperspectral imaging technique's potential, we probed the potential of some hybrid spectral similarity measures to classify different commonly used paper samples. A comparison of these methods is quantitatively presented in this article. Hybrid spectral similarity algorithms are tested on forensic analysis of paper data. We compared the classification capabilities of various hybrid spectral similarity algorithms on hyperspectral data of 40 different paper samples. The overall accuracy (OA), kappa K̂, Z‐score of kappa (ZK̂), and the 95% confidence interval of kappa (CI(K̂)) are used for comparison. The SID‐SAM and SID‐SCA produced an overall accuracy of 88% and 87%, respectively, which is highest among the hybrid spectral similarity measures tested. [ABSTRACT FROM AUTHOR]

With recent advancements in image processing and printing technology, home printers have improved in performance and grown more widespread. As such, they have been increasingly used in counterfeiting and forgery. Most counterfeit bills in Korea have been created using home scanners and printers. The identification of printer model is thus necessary to rapidly track down criminals and solve crimes. Household printers can be largely divided into inkjet and laser printers. These two types of printers print halftone textures instead of continuous images. This study proposed a technique of printer classification based on halftone textures that can be observed in printed documents. Since halftone textures are expressed as periodic lattices, the images were transformed via FFT, which is highly effective at expressing periodicity. ResNet, known for its superior gradient flow, was used for training. The experiment was conducted on 12 color laser jets and 2 inkjets. Scans of bills printed by each printer were used, and halftone texture analysis was performed on these images for printer model classification. Each image was cropped into several parts; one of the cropped parts was analyzed. The analysis showed that laser printers could be 100% distinguished from inkjet printers. An accuracy of 98.44% was achieved in make classification. When 50 cropped images were used instead of a single image, the technique achieved 100% accuracy in model classification. The proposed technique is non‐destructive; it offers high accessibility and efficiency as it can be performed using a scanner alone, without requiring additional optical equipment. [ABSTRACT FROM AUTHOR]

The excessive prescription of benzodiazepines is putting more people at risk of dependence on these drugs and is exacerbating the fatal overdose toll of opioids. A rapid and sensitive SERS method has been developed for trace detection of select benzodiazepines in low‐dosage suspect counterfeit tablets, capsules, and injectable solutions using two different portable handheld Raman spectrometers equipped with either a 785‐nm laser or a 1064‐nm laser. A total of 169 samples and blanks were examined using five handheld Raman spectrometers, which provided data set of 729 examinations. The extraction/SERS procedures yielded true positive rates above 90% for alprazolam, diazepam, and midazolam using the 1064‐nm device and yielded true positive rates above 95% for alprazolam, clonazepam, diazepam, estazolam, midazolam, and temazepam using the 785‐nm device; however, the extraction/SERS procedures yielded true positive rates below 60% for lorazepam and triazolam. The minimum concentration (Cmin) of the benzodiazepine standards that reproducibly yielded a positive match ranged from 1 to 10 μg/ml using the 1064‐nm laser device and from 0.5 to 50 μg/ml using the 785‐nm laser device. For the analysis of authentic and suspect counterfeit tablets containing these benzodiazepines, the measured Cmin ranged between 10 and 15 µg per tablet or capsule for 1064‐nm laser device and 1–100 µg per tablet or capsule for 785‐nm laser device. The developed methods are simple, rapid, and ideal for screening suspect benzodiazepine‐containing pharmaceutical products at satellite laboratories located within or near international mail facilities and express courier hubs. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging has shown tremendous growth over the past three decades. Hyperspectral imaging was evolved through remote sensing. Along, with the technological enhancements hyperspectral imaging has outgrown, conquering over other various application areas. In addition to it, data enriched data cubes with abundant spectral and spatial information works as perk for capturing, analyzing, reviewing, and interpreting results from data. This review concentrates on emerging application areas of hyperspectral imaging. Emerging application areas are selected in ways where there is a vast scope for future enhancements by exploiting cutting edge technology, that is, deep learning. Applications of hyperspectral imaging techniques in some selected areas (remote sensing, document forgery, history and archaeology conservation, surveillance and security, machine vision for fruit quality inspection, medical imaging) are focused. The review pivots around the publicly available datasets and features used domain wise. This review can act as a baseline for deep learning and machine vision experts, historical geographers, and scholars by providing them a view of how hyperspectral imaging is implemented in multiple domains along with future research prospects. This article is categorized under:Technologies > Machine LearningTechnologies > Prediction [ABSTRACT FROM AUTHOR]