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Consumers' perception of product value when purchasing is a critical issue relative to online platform commerce. This is especially true when the brand manufacturer authorises only a third-party reseller to sell through the platform, a practice that may provide opportunities to counterfeiters. To address this issue, we establish a theoretical model in which one brand manufacturer wholesales its products to a third-party reseller who then sells through an online platform. The platform acts as a marketplace, while multiple counterfeiters also exist on the platform, which may deceive consumers. In particular, we consider that the manufacturer can encroach with a direct channel and that the platform can adopt blockchain technology to help consumers identify authenticity. The results show that the platform could induce the brand manufacturer to encroach by deploying blockchain technology. Furthermore, an interesting result indicates that although either manufacturer encroachment or blockchain technology used alone can effectively boycott counterfeits, the coexistence of two strategies can raise the demand for counterfeit products. Finally, we find that when the brand manufacturer encroaches on the platform and the platform adopts blockchain technology, the brand manufacturer, platform, reseller and social welfare can achieve Pareto improvement, while the consumer surplus may be worse off. [ABSTRACT FROM AUTHOR]

The extensive health-promoting effects of Citri Reticulatae Pericarpium (CRP) have attracted researchers' interest. The difference in storage time, varieties and origin of CRP are closely related to the content of bioactive compounds they contain. The consitituent transformation mediated by environmental microorganisms (bacteria and fungi) and the production of new bioactive components during the storage process may be the main reason for 'the older, the better' of CRP. In addition, the gap in price between different varieties can be as large as 8 times, while the difference due to age can even reach 20 times, making the 'marketing young-CRP as old-CRP and counterfeiting origin' flood the entire market, seriously harming consumers' interests. However, so far, the research on CRP is relatively decentralized. In particular, a summary of the microbial transformation and authenticity identification of CRP has not been reported. Therefore, this review systematically summarized the recent advances on the main bioactive compounds, the major biological activities, the microbial transformation process, the structure, and content changes of the active substances during the transformation process, and authenticity identification of CRP. Furthermore, challenges and perspectives concerning the future research on CRP were proposed. [ABSTRACT FROM AUTHOR]

The fight against counterfeiting is a crucial concern for economies around the world, underscoring the urgent need for effective measures to ensure the legitimacy of products and documents. Luminescent security inks have emerged as effective tools in this battle. The third generation of these inks, incorporating stimuli‐responsive organic materials, enables unique efficiency. In this work, the stimuli‐responsive behavior of three trialkylated triazatruxene dimers (TRI1, TRI2, and TRI3), endowed with N‐alkyl chains of different length is investigated. Among them, TRI3, with three flexible dodecyl chains, shows remarkable stimuli‐responsive fluorescence that is attributed to thermally or shear‐induced phase transitions between a tetragonal columnar phase and a discotic‐nematic liquid crystal phase. Such transformation involves the breakdown of highly emissive J‐aggregates with a strong influence on the resulting emissive color. Interestingly, these stimuli‐triggered transformations are spontaneously reversed, which renders this compound very interesting in the search of third‐generation security inks. [ABSTRACT FROM AUTHOR]

Nowadays, the abuse of deepfakes is a well-known issue since deepfakes can lead to severe security and privacy problems. And this situation is getting worse, as attackers are no longer limited to unimodal deepfakes, but use multimodal deepfakes, i.e., both audio forgery and video forgery, to better achieve malicious purposes. The existing unimodal or ensemble deepfake detectors are demanded with fine-grained classification capabilities for the growing technique on multimodal deepfakes. To address this gap, we propose a graph attention network based on heterogeneous graph for fine-grained multimodal deepfake classification, i.e., not only distinguishing the authenticity of samples, but also identifying the forged types, e.g., video or audio or both. To this end, we propose a positional coding-based heterogeneous graph construction method that converts an audio-visual sample into a multimodal heterogeneous graph according to relevant hyperparameters. Moreover, a cross-modal graph interaction module is devised to utilize audio-visual synchronization patterns for capturing inter-modal complementary information. The de-homogenization graph pooling operation is elaborately designed to keep differences in graph node features for enhancing the representation of graph-level features. Through the heterogeneous graph attention network, we can efficiently model intra- and inter-modal relationships of multimodal data both at spatial and temporal scales. Extensive experimental results on two audio-visual datasets FakeAVCeleb and LAV-DF demonstrate that our proposed model obtains significant performance gains as compared to other state-of-the-art competitors. The code is available at https://github.com/yinql1995/Fine-grained-Multimodal-DeepFake-Classification/. [ABSTRACT FROM AUTHOR]

Childhood is a significant period that affects a person’s health throughout their whole life. Children deserve to receive quality medical care. It is also the responsibility of parents to be aware of treatment methods and to ensure that their children receive the best medical and pharmaceutical care. An essential point of drug therapy in children is the administration of drugs without a doctor’s prescription. The aim of this article is to research the importance of pharmaceutical care provided in counseling parents in the purchase of over-the-counter (OTC) drugs. Material and methods. A review of the scientific literature was performed in the databases of PubMed and Google Scholar. The study was carried out in the period from 01.02.2024 to 31.03.2024 using key words: “pharmaceutical care”, “children”, “OTC drugs”, “adverse drug reactions”. Results. OTC drugs are widely available, allowing patients to self-medicate and also parents to give them to their children. Children get sick much more often and have an elevated body temperature compared to adults. OTC drugs are considered safe for use in pediatric patients when used according to the instructions on their label. Non-prescription drugs also include analgesics such as Aspirin and Paracetamol, some of the medicines for coughs, hay fever, etc. Conclusion. The pharmacist provides information about medicines, their correct use and the possibility of adverse drug reactions and/or interactions with food and other medicines and is also a responsible participant in the provision of primary health care. [ABSTRACT FROM AUTHOR]

Recently, it has become difficult to recognize and easier to misuse digital images due to the large number of editing tools available. Detecting forgeries in images is crucial for security and forensic purposes. Therefore, this research implements a deep learning (DL) method of convolutional autoencoder (CAE) which improves colorization-based image forgery detection by leveraging spatial and color information, increasing the detection accuracy. At first, the pre-processed input forgery images are used with the wiener filtering-contrast restricted improved histogram equalization (WE-CLAHE) technique. Hybrid dual-tree complex wavelet trigonometric transform (H-DTCWT) and VGG-16 are used to extract effective features from the clustered data. Improved horse herd optimization (IHH) is employed to reduce the dimensionality of a feature. At last, the CAE model is implemented to significantly recognize the image forgery. The accuracy of CASIA V1 and GRIP datasets of 99.95% and 99.97%, respectively is achieved. Hence, this implemented method obtains a high forgery detection performance than the existing methods. [ABSTRACT FROM AUTHOR]

Fingerprint liveness detection systems have been affected by spoofing, which is a severe threat for fingerprint-based biometric systems. Therefore, it is crucial to develop some techniques to distinguish the fake fingerprints from the real ones. The software based techniques can detect the fingerprint forgery automatically. Also, the scheme shall be resistant against various distortions such as noise contamination, pixel missing and block missing, so that the forgers cannot deceive the detector by adding some distortions to the faked fingerprint. In this paper, we propose a fingerprint forgery detection algorithm based on a suggested adaptive thresholding pattern. The anisotropic diffusion of the input image is passed through three levels of the wavelet transform. The coefficients of different layers are adaptively thresholded and concatenated to produce the feature vector which is classified using the SVM classifier. Another contribution of the paper is to investigate the effect of various distortions such as pixel missing, block missing, and noise contamination. Our suggested approach includes a novel method that exhibits improved resistance against a range of distortions caused by environmental phenomena or manipulations by malicious users. In quantitative comparisons, our proposed method outperforms its counterparts by approximately 8% and 5% in accuracy for missing pixel scenarios of 90% and block missing scenarios of size 70 × 70 , respectively. This highlights the novelty approach in addressing such challenges. [ABSTRACT FROM AUTHOR]

Image splicing is a common and informal way to generate forgery images. Two or more than two images merged into one image is called image splicing. Many researchers in the image splicing area continue to face the challenge of splice forgery image recognition. This is why it's crucial to regularly update image forgery detection systems so that they are continuously updated and require more research in this area. The IFC-TC, DVMM, Columbia, and CASIA datasets have been used to evaluate the splicing recognition result of the proposed technique. In this technique initially, the features are extracted by using the DWT and LBP techniques for computing different parameters. Initially, images undergo three layers of DWT, and splitting the image into low- and sub-bands high-frequency occurs after the first level. In addition, by splitting a low-frequency sub-band twice, more information can be extracted, resulting in a three-level DWT. The different parameters computed using LBP and DWT are PPV, TPR, FPR, F-1 score, and accuracy for splicing forgery detection. The different parameters derived from DWT and LBP techniques are compared individually as well as combined. The SVM classifier is used to distinguish among original and spliced image identification. The PPV, TPR, FPR, F-1 Score, and accuracy parameters are 98.88%, 97.59%, 87.51%, 98.53%, and 98.31%, respectively, for the IFC-TC dataset. The PPV, TPR, FPR, F-1 Score, and accuracy parameters are 97.92%, 98.65%, 89.69%, 98.20%, and 98.59%, respectively, for the DVMM dataset. The same PPV, TPR, FPR, F-1 Score, and accuracy parameters were calculated for the Columbia and CASIA datasets, and the results were compared using the existing technique. [ABSTRACT FROM AUTHOR]

The issue of counterfeiting has always been a major challenge in today's society, putting forward high requirements for anti‐counterfeiting technology. Here, a promising 2D/3D anti‐counterfeiting security system is developed based on anthracene‐functionalized poly(styrene‐butadiene‐styrene) (SBS‐CAN), which possessed the physical unclonable self‐wrinkling patterns, fluorescence, and shape memory behavior. By controlling the dimerization of anthracene groups with ultraviolet light and external pre‐stretched operation, 2D information with surface wrinkle and fluorescence is achieved, allowing for the design of security systems based on actual needs. Furthermore, 3D structures constructed based on the shape memory behavior can further be utilized to encrypt these 2D information, enhancing the security level and weakening the cloneable risk of surface wrinkle and fluorescence for further. This programmable 2D/3D anti‐counterfeiting system has multiple advantages over the commonly used fluorescent wrinkled tag or fluorescent shape memory label, providing an intelligent choice for high‐security information carriers. [ABSTRACT FROM AUTHOR]

The malicious use of deepfake videos seriously affects information security and brings great harm to society. Currently, deepfake videos are mainly generated based on deep learning methods, which are difficult to be recognized by the naked eye, therefore, it is of great significance to study accurate and efficient deepfake video detection techniques. Most of the existing detection methods focus on analyzing the discriminative information in a specific feature domain for classification from a local or global perspective. Such detection methods based on a single type feature have certain limitations in practical applications. In this paper, we propose a deepfake detection method with the ability to comprehensively analyze the forgery face features, which integrates features in the space domain, noise domain, and frequency domain, and uses the Inception Transformer to learn the mix of global and local information dynamically. We evaluate the proposed method on the DFDC, Celeb-DF, and FaceForensic++ benchmark datasets. Extensive experiments verify the effectiveness and good generalization of the proposed method. Compared with the optimal model, the proposed method with a small number of parameters does not use pre-training, distillation, or assembly, but still achieves competitive performance. The ablation experiments evaluate the role of each component. [ABSTRACT FROM AUTHOR]

Microelectronics integrity is a critical issue for many industries including the Department of Defense (DoD). The military systems the DoD operates are particularly vulnerable to counterfeiting, with potentially costly or even catastrophic consequences. Counterfeits, regardless of production intent (malign or ersatz), raise significant concerns for industry and the DoD because they often demonstrate operational performance shortcomings, have lower reliability, or make components or organizations more vulnerable to attack. This article uses a systems dynamics modeling approach to explore the economics of counterfeiting for a sample system, the interactions between counterfeiters and the US Navy supply chain, and the impacts of counterfeit surveillance and detection to address the question: Is it more effective to target detection efforts at the component level or at the Line Replaceable Units level? A case study of an engine control module for the LM2500 propulsion turbine used on US Navy Arleigh Burke class guided missile destroyer (DDG‐51) platforms is provided to demonstrate the approach. [ABSTRACT FROM AUTHOR]

Quantum secret sharing holds an important place in quantum cryptography. In this paper, a threshold changeable dynamic quantum secret sharing scheme with cheating identification is firstly proposed based on the Chinese Remainder Theorem. On the premise of not altering the shared secret and the private shares of the original participants, our scheme realizes the dynamic updating of participants and for the first time achieves the changeable threshold in the quantum environment, which greatly improves the flexibility and practicality of the scheme. In addition, we generalize the entanglement swapping equations of Bell states in 2-dimension to d-dimension. During the reconstruction phase, our scheme can timely detect and identify the cheating behaviors based on the randomized components and the entanglement swapping equations of d-dimensional Bell states. Meanwhile, the randomized components ensure privacy protection for shares and avoid the interference of invalid shares when recovering the secret. Security analysis shows that our scheme is resistant to not only a series of typical external attacks but also forgery, collusion, and dishonest revoked participant attacks. Compared with the existing schemes, our scheme is not only more secure and efficient but also has lower computational consumption. [ABSTRACT FROM AUTHOR]

This paper proposes a verifiable dynamic multi-dimensional quantum secret sharing scheme utilizing a generalized Hadamard gate. The dealer simultaneously distributes quantum and classical information to participants in a single distribution using a generalized Hadamard gate and a quantum SUM gate. To detect the malicious behavior of participants, the dealer prepares a sequence of checking particles. The participants retrieve the secret quantum state and classical information utilizing a generalized Hadamard gate and single-particle measurement. Additionally, the authenticity of secrets is ensured using a public hash function. While adding or removing participants, the dealer does not require assistance from other participants. The proposed protocol effectively thwarts eavesdroppers and participants from performing several types of attacks, including collusion, forgery, denial, and revoked dishonest participant attacks. The proposed protocol yields greater reliability, simplicity, versatility, and practicality. [ABSTRACT FROM AUTHOR]

In the field of quantum cryptography, quantum secret sharing (QSS) holds substantial importance, with identity authentication emerging as a key strategy for safeguarding information. It efficiently certifies the identification of both persons involved in the conversation, which contributes to increased security measures. Our proposed, (m , n) threshold quantum secret sharing (QSS) scheme introduces a unique approach to mutual identity authentication, utilizing mutually unbiased bases. Alice employs a symmetric bivariate polynomial to distribute the secret share among all participants in this algorithm. For secret reconstruction, a trusted participant, Bob 1 , shares a GHZ state with a qualified group of m participants, including himself. Each participant applies QFT and a unitary transformation (related to their part of the shared secret) on their particle. During the secret recovery phase, the scheme ensures that secrets exclusively held by participants remain undisclosed and are not transmitted, reinforcing the security of the communication process. As a result, external eavesdroppers are left empty-handed in their attempt to access information about secrets during this phase. Our protocol surpasses in terms of security, effectiveness, and practicality, proving its resilience against intercept–resend attacks, collision attacks, collective attacks, entangle-measure attacks, and forgery attacks according to a comprehensive security analysis. [ABSTRACT FROM AUTHOR]

Breathtaking advances in face forgery techniques produce visually untraceable deepfake videos, thus potential malicious abuse of these techniques has sparked great concerns. Existing deepfake detectors primarily focus on specific forgery patterns with global features extracted by CNN backbones for forgery detection. Due to inadequate exploration of content and texture features, they often suffer from overfitting method-specific forged regions, thus exhibiting limited generalization to increasingly realistic forgeries. In this paper, we propose a Wavelet-guided Texture-Content HiErarchical Relation (WATCHER) Learning framework to delve deeper into the relation-aware texture-content features. Specifically, we propose a Wavelet-guided AutoEncoder scheme to retrieve the general visual representation, which is aware of high-frequency details for understanding forgeries. To further excavate fine-grained counterfeit clues, a Texture-Content Attention Maps Learning module is presented to enrich the contextual information of content and texture features via multi-level attention maps in a hierarchical learning protocol. Finally, we propose a Progressive Multi-domain Feature Interaction module in pursuit to perform semantic reasoning on relationship-enhanced texture-content forgery features. Extensive experiments on popular benchmark datasets substantiate the superiority of our WATCHER model, consistently trumping state-of-the-art methods by a significant margin. [ABSTRACT FROM AUTHOR]

Face forgery detection (FFD) on digital images has become increasingly challenging with the proliferation of sophisticated manipulation techniques. In this study, we propose a novel approach, named Adaptive Wavelet-based Transformer with Self-paced Auto Augmentation (SA 3 WT), which naturally combines the global representation capabilities of visual transformers with adaptive enhancement of fine-grained artifacts in the frequency domain to effectively capture forgery patterns. In particular, to adequately handle various clues, the network incorporates Wavelet-based Mixed Attention (WMA) Transformer block to better leverage the information residing in all frequency sub-bands and a Residual Reserve Fine-grained Sampler (RRFS) to enhance detailed forgery artifacts while learning hierarchical global representations. By deeply mixing the modeling processes of global representations and fine-grained features throughout the network, the model captures rich forgery clues while simultaneously bypassing the fusion issue arising from their separate extraction. Furthermore, Self-paced Auto Augmentation Strategy (SAAS) facilitates model learning by unifying data augmentation and active learning in a coupled manner. Extensive experiments conducted on several benchmarks demonstrate the superiority of SA 3 WT compared to state-of-the-art methods. The ablation studies and cross-dataset evaluations confirm the significance of the specifically designed modules, in terms of both effectiveness and generalization. Our findings suggest that the pure visual transformers also provide a promising direction for advanced forgery detection in real-world scenarios. [ABSTRACT FROM AUTHOR]

In the past few years, there has been a great deal of concern about medicine counterfeiting. Manufacturing and distributing of fake and falsified drugs are prohibited and pose a risk to public health. Depending on how strictly a nation's laws and processes are adhered to, the severity of this issue varies greatly between nations. Thus, stopping the production of fake medications has become crucial, particularly in developing and impoverished nations. To prevent medicine counterfeiting, this research aims to: (1) Identify the main obstacles facing the current pharmaceutical supply chain system (2) Suggest an Ethereum framework based on blockchain and (3) Assess the suggested framework. Based on the suggested framework, a prototype was created, and the prototype was assessed using an evaluation study. According to the assessment study, the average block time is 15s, and the average block execution time is 4s. Once more, compared to the current system, the suggested framework was judged to be practical, safe, scalable, and customer-focused. [ABSTRACT FROM AUTHOR]

Copyright of Jurisprudence Faculty Journal / Magallaẗ Kulliyyaẗ Al-fiqh is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

While most current image forgery localization (IFL) deep learning models focus primarily on the foreground of tampered images, they often neglect the essential complementary background semantic information. This oversight tends to create significant gaps in these models' ability to thoroughly interpret and understand a tampered image, thereby limiting their effectiveness in extracting critical tampering traces. Given the above, this paper presents a novel contrastive learning and edge-reconstruction-driven complementary learning network (CECL-Net) for image forgery localization. CECL-Net enhances the understanding of tampered images by employing a complementary learning strategy that leverages foreground and background features, where a unique edge extractor (EE) generates precise edge artifacts, and edge-guided feature reconstruction (EGFR) utilizes the edge artifacts to reconstruct a fully complementary set of foreground and background features. To carry out the complementary learning process more efficiently, we also introduce a pixel-wise contrastive supervision (PCS) method that attracts consistent regions in features while repelling different regions. Moreover, we propose a dense fusion (DF) strategy that utilizes multi-scale and mutual attention mechanisms to extract more discriminative features and improve the representational power of CECL-Net. Experiments conducted on two benchmark datasets, one Artificial Intelligence (AI)-manipulated dataset and two real challenge datasets, indicate that our CECL-Net outperforms seven state-of-the-art models on three evaluation metrics. [ABSTRACT FROM AUTHOR]

JPEG image manipulation localization aims to accurately classify and locate tampered regions in JPEG images. Existing image manipulation localization schemes usually consider diverse data streams of spatial domain, e.g. noise inconsistency and local content inconsistency. They, however, easily ignore an objective scenario: data stream features of spatial domain are hard to directly apply to compressed image format, e.g., JPEG, because tampered JPEG images may contain severe re-compression inconsistency and re-compression artifacts, when they are re-compressed to JPEG format. As a result, the traditional localization schemes relying on general data streams of spatial domain may result in a large number of false detection of tampered region in JPEG images. To address the above problem, we a new JPEG image manipulation localization scheme, in which a wide-receptive-field attention network is designed to effectively learn JPEG compressed artifacts. We firstly introduce the wide-receptive-field attention mechanism to re-construct U-Net network, which can effectively capture contextual information of JPEG images and analyze tampering traces from different image regions. Furthermore, a flexible JPEG compressed artifact learning module is designed to capture the image noise caused by JPEG compression, in which the weights can be adjusted flexibly based on image quality, without the need for decompression operations on JPEG images. Our proposed method can significantly strength the differentiation capability of detection model for tampered and non-tampered regions. A series of experiments are performed over different image sets, and the results demonstrate that the proposed scheme can achieve an overall localization performance for multi-scale JPEG manipulation regions and outperform most of state-of-the-art schemes in terms of detection accuracy, generalization and robustness. [ABSTRACT FROM AUTHOR]

The widespread and easy-to-use digital tools for manipulating images have made it easier for people to tamper with images, which have increased the number of false or fraudulent images that are shared over the internet and social networks. The massive issue of image forgery requires practical and accurate solutions. In response, a number of deep learning and computer vision techniques have been created to identify digital picture forgeries. This review paper gives a new viewpoint by highlighting recent developments and the need for updated insights, in contrast to previous reviews on deep learning techniques for image forgery detection. This study focuses on how current algorithms employ different deep learning techniques to obtain more accurate results by analyzing the state-of-the-art in deep learning-based copy-move image forgery detection (CMFD). Notably, this review provides a comprehensive classification of the most recent deep learning-based copy-move image forgery detection methods and also gives a succinct summary of deep learning detection techniques. Additionally, the study gives a new comparison between different deep learning algorithms for CMFD and explores widely-used datasets for image forgery detection, enabling a thorough understanding of the problem through a comprehensive analysis and synthesis of the existing literature. Further, this review aims to identify new directions, fill in knowledge gaps, and encourage further research in this important area. [ABSTRACT FROM AUTHOR]

For Deepfake detection, many existing works use the cross-entropy loss to enforce the classifier network to learn the mapping relationship from the RGB domain to the class domain, lacking an explicit constraint to guide the feature extraction network to learn discriminative features from an input image. This constrains the feature representation capability to expose deepfake. In this work, we analyze the feature extraction network in terms of both difference and similarity capabilities and propose a new constraint called similarity loss (SL) to improve the detection performance of the convolutional neural network (CNN) based detector. Moreover, according to the experimental results of the SL on data augmentation effectiveness, we propose a simple yet efficient framework, which is called as efficient similarity representation learning (ESRL), for deepfake detection. Extensive experiments on three public datasets (namely FF++, DFDC, and Celeb-DF) show that the feature extraction network trained with the help of SL can map forged faces and real faces to different feature embedding and map the same type of forged faces to similar feature embedding. [ABSTRACT FROM AUTHOR]

The distributed authentication of a large number of resource-constrained power terminal devices (PTDs) is crucial for ensuring the security of the power IoT communication. However, existing solutions are inadequate in terms of security (such as vulnerability to single point failures or insider attacks) and communication costs, which do not meet the requirements of the power IoT environment. Therefore, we propose a zero-trust-based mutual authentication scheme in cloud-edge-end collaboration power IoT environment based on blockchain technology. The proposed solution involves deploying a distributed multi-point zero-trust engine around dense PTDs to collect real-time identity information. Through the maintenance of an alliance blockchain, the edge server securely shares and traces identity information, preventing tampering and effectively blocking threats related to lost terminals during authentication. Additionally, a lightweight ECC key management scheme ensures the security of authentication information. The proposed scheme effectively defends against common attacks such as replay attacks and identity forgery attacks through informal security analysis, while its security is evaluated using the Canetti and Krawczyk (CK) adversary models. Performance evaluation results demonstrate that the proposed scheme achieves effectiveness without the compromising required security attributes, which obtains up to 58% improvement 58% improvement in computation delay and a 56.9% improvement in communication costs over previous state-of-the-art methods. [ABSTRACT FROM AUTHOR]

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]

Frame deletion detection is a challenging task in the field of digital video forensics. This paper proposes a deep-learning-based frame deletion detection method for single-shot videos. We capture traces of frame deletion forgery from both adjacent and long-range continuous frames. Specifically, we propose a novel multi-scale difference convolutional neural network (MSDCNN) structure, which models different levels of inter-frame variations. Then, we use the long-short-term memory network (LSTM) to capture the long-term variation pattern of multi-scale differential features. The proposed method is a simple and principled frame deletion detection framework with a small computational cost. According to the experiments, the proposed framework can achieve a more advanced performance of frame deletion detection than traditional methods and methods based on 3D convolutions. [ABSTRACT FROM AUTHOR]

Digital signatures are one of the key cryptographic components for providing authenticity and non-repudiation. To circumvent the need of certificates, Shamir in 1984 introduced identity-based signature (IBS). Nearly all of the existing state-of-the-art IBS schemes are relying on the number-theoretic hardness assumptions. Unfortunately, these hard problems are insecure and face a threat in quantum world. Thus, it is high time to design and analyze IBS algorithms that can resist quantum attacks and provide long-term security. Quantum cryptography is one such technique to provide quantum-safe IBS. In this paper, we cryptanalyze the quantum cryptography-based IBS of Huang et al. (Huang et al. in Quantum Inf Process 22(1):36, 2022). We show that the design in (Huang et al. in Quantum Inf Process 22(1):36, 2022) is not secure against public key generator forgery attack, collusion attacks, and intercept and resend attacks. Next, we modify the design of (Huang et al. in Quantum Inf Process 22(1):36, 2022) and propose a new quantum IBS (namely qIBS) which is secure against the aforementioned attacks. [ABSTRACT FROM AUTHOR]

Early presentations of the Dead Sea Scrolls in popular media relied on orientalist tropes and a narrow Christian horizon to create relevance and prestige for the scrolls, which contributed to strong religious interest in them and a market for unprovenanced Dead Sea Scrolls fragments and forgeries. Such a strategy also constructed public authority for the Bible scholars working on the scrolls. This article treats the colonialist approach in the earliest popular works on the scrolls in the United States, written by Edmund Wilson, Frank Moore Cross, and Alex Small, and shows how the stories they tell set the stage for religious veneration of the scrolls and popular acclaim of the scholars. [ABSTRACT FROM AUTHOR]

In contexts requiring user authentication, such as financial, legal, and administrative systems, signature verification emerges as a pivotal biometric method. Specifically, handwritten signature verification stands out prominently for document authentication. Despite the effectiveness of triplet loss similarity networks in extracting and comparing signatures with forged samples, conventional deep learning models often inadequately capture individual writing styles, resulting in suboptimal performance. Addressing this limitation, our study employs a triplet loss Siamese similarity network for offline signature verification, irrespective of the author. Through experimentation on five publicly available signature datasets—4NSigComp2012, SigComp2011, 4NSigComp2010, and BHsig260—various distance measure techniques alongside the triplet Siamese Similarity Network (tSSN) were evaluated. Our findings underscore the superiority of the tSSN approach, particularly when coupled with the Manhattan distance measure, in achieving enhanced verification accuracy, thereby demonstrating its efficacy in scenarios characterized by close signature similarity. [ABSTRACT FROM AUTHOR]

To address the issue of diminished LBS privacy protection capabilities in sparse user environments within vehicular networks, this paper proposed a hybrid privacy protection scheme based on homomorphic encryption. The scheme exploited the properties of homomorphic encryption to enable the on-demand generation and indistinguishable authentication of vehicle users' virtual identities. By obfuscating the virtual addresses with the vehicles' real locations, it further provided hybrid privacy protection based on vehicle identity and location. Security analysis demonstrates that, in addition to effectively preserving service privacy, the scheme is traceable against malicious vehicle identities, and it effectively defends against denial-of-service attacks and pseudonym forgery attacks. Performance analysis results indicate that, compared to traditional pseudonym generation methods and k-anonymity techniques, as the privacy factor k increases to 40, the scheme reduces computational overhead by 25% in dynamic pseudonym generation and achieves over 91% privacy protection, this compensates for the deficiencies of k-anonymity and pseudonym techniques in specific environments, making it more suitable for dynamic, low-latency vehicular network communication environments. [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]

In the Votos de San Millán, the prologue to the Becerro Galicano cartulary, two saints –Millán and Santiago– appear to inspire a combined Leonese and Castilian host to victory over a Moorish army at the legendary Battle of Hacinas. The Votos was in all probability composed between May 1194 and July 1195, i. e., just after the Treaty of Tordehumos established a ten-year truce between León and Castile brokered by the papal legate Gregory of St Angelo on behalf of his uncle Celestine III, intended to smooth the way for a victorious Christian alliance against the Almohads. We contend that the legendary account of battle that appears in the Votos is intended as an allegory for the prospective alliance, before this was shattered at the Battle of Alarcos in July 1195. [ABSTRACT FROM AUTHOR]

Metaphysicians who are aware of modern physics usually follow Putnam (1967) in arguing that Special Theory of Relativity is incompatible with the view that what exists is only what exists now or presently. Partisans of presentism (the motto 'only present things exist') had very difficult times since, and no presentist theory of time seems to have been able to satisfactorily counter the objection raised from Special Relativity. One of the strategies offered to the presentist consists in relativizing existence to inertial frames. This unfashionable strategy has been accused of counterfeiting, since the meaning of the concept of existence would be incompatible with its relativization. Therefore, existence could only be relativistically invariant. In this paper, I shall examine whether such an accusation hits its target, and I will do this by examining whether the different criteria of existence that have been suggested by the Philosophical Tradition from Plato onwards imply that existence cannot be relativized. [ABSTRACT FROM AUTHOR]

Similar to religious reverse glass paintings, Gilan's holy shrine paintings are also religious art from the Qajar period. Among these paintings in the matter of religious beliefs and artists' origins, a unity in the style of narration and illustration can be found. Transtextuality, introduced by Gérard Genette, is one of the most important theories in literature and art. He addresses different ways of how a text can remind the reader of a previous one and guide the reader towards it. The main question of the research is which factors that have been used in reverse glass paintings and holy shrine paintings have transtextuality and similarity. The research method is descriptive and analytic and the library collecting content method is taking fiche notes and reading pictures text; the data analyzing is qualitative. Findings are indicative of a similarity and transtextuality relation between holy shrine paintings and reverse glass paintings. Forgery similarity is the application of visual elements and semantic implications, with functioning and imitating form and pretext content. Transposition Transtextuality in the visual structure and semantic implications has a critical role in hypertextuality. Artists have created a new concept by modifying elements and changing the concept in Gilan's holy shrine painting. [ABSTRACT FROM AUTHOR]

The counterfeiting of integrated circuits (ICs) has been a growing issue. Current available methods used to detect counterfeit ICs can be expensive, imprecise, and time-consuming. This paper explores the resonant cavity system: a non-contact, non-destructive method to rapidly differentiate counterfeit ICs from authentic ones. The system captures a unique signature of an IC placed inside it. Data were captured for ICs of various technologies and authenticities. The data included return loss values captured at various transverse electric (TE) modes between 2.8 GHz and 6 GHz. This allowed for the comparison of the effectiveness of the various TE modes in being able to distinguish ICs. The resonant cavity system was able to distinguish most of the ICs at higher TE modes. [ABSTRACT FROM AUTHOR]

The rapid development of generative technologies has made the production of forged products easier, and AI-generated forged images are increasingly difficult to accurately detect, posing serious privacy risks and cognitive obstacles to individuals and society. Therefore, constructing an effective method that can accurately detect and locate forged regions has become an important task. This paper proposes a hierarchical and progressive forged image detection and localization method called HPUNet. This method assigns more reasonable hierarchical multi-level labels to the dataset as supervisory information at different levels, following cognitive laws. Secondly, multiple types of features are extracted from AI-generated images for detection and localization, and the detection and localization results are combined to enhance the task-relevant features. Subsequently, HPUNet expands the obtained image features into four different resolutions and performs detection and localization at different levels in a coarse-to-fine cognitive order. To address the limited feature field of view caused by inconsistent forgery sizes, we employ three sets of densely cross-connected hierarchical networks for sufficient interaction between feature images at different resolutions. Finally, a UNet network with a soft-threshold-constrained feature enhancement module is used to achieve detection and localization at different scales, and the reliance on a progressive mechanism establishes relationships between different branches. We use ACC and F1 as evaluation metrics, and extensive experiments on our method and the baseline methods demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

The article discusses the criminal liability associated with counterfeiting famous trade marks in Italy, highlighting that such actions are punishable under Articles 473 and 474 of the Criminal Code, even if the counterfeit goods are in different market sectors. Topics include the impact of counterfeit goods on public trust, the role of corporate liability under Legislative Decree No. 231, and the criteria for assessing the adequacy of organizational models.

Copy-Move Forgery Detection (CMFD) is a technique that is designed to identify image tampering and locate suspicious areas. However, the practicality of the CMFD is impeded by the scarcity of datasets, inadequate quality and quantity, and a narrow range of applicable tasks. These limitations significantly restrict the capacity and applicability of CMFD. To overcome the limitations of existing methods, a novel solution called IMTNet is proposed for CMFD by employing a feature decoupling approach. Firstly, this study formulates the objective task and network relationship as an optimization problem using transfer learning. Furthermore, it thoroughly discusses and analyzes the relationship between CMFD and deep network architecture by employing ResNet-50 during the optimization solving phase. Secondly, a quantitative comparison between fine-tuning and feature decoupling is conducted to evaluate the degree of similarity between the image classification and CMFD domains by the enhanced ResNet-50. Finally, suspicious regions are localized using a feature pyramid network with bottom-up path augmentation. Experimental results demonstrate that IMTNet achieves faster convergence, shorter training times, and favorable generalization performance compared to existing methods. Moreover, it is shown that IMTNet significantly outperforms fine-tuning based approaches in terms of accuracy and F 1. [ABSTRACT FROM AUTHOR]

Image forgery detection is essential for forensic and security applications. In local locations where key points (KPs) are few, traditional approaches that rely on them suffer from restrictions. To tackle this problem, this paper suggests an innovative strategy that combines pre-processing, feature extraction, and classification methods. We specifically present EFP-AEHO, or Enhanced Flower Pollination-Adaptive Elephant Herd Optimization, for modifying the Deep Belief Network's (DBN) weights. Our method provides better performance by first converting RGB images into the Lab color space, then applying Eigenvalue Asymmetry (EAS) to extract texture features. Its efficacy is highlighted by experimental findings on the F2000 dataset, which show testing accuracy of 99.4%, TPR of 98%, and TNR of 99.12%. [ABSTRACT FROM AUTHOR]

Image forgery detection is essential for forensic and security applications. In local locations where key points (KPs) are few, traditional approaches that rely on them suffer from restrictions. To tackle this problem, this paper suggests an innovative strategy that combines pre-processing, feature extraction, and classification methods. We specifically present EFP-AEHO, or Enhanced Flower Pollination-Adaptive Elephant Herd Optimization, for modifying the Deep Belief Network's (DBN) weights. Our method provides better performance by first converting RGB images into the Lab color space, then applying Eigenvalue Asymmetry (EAS) to extract texture features. Its efficacy is highlighted by experimental findings on the F2000 dataset, which show testing accuracy of 99.4%, TPR of 98%, and TNR of 99.12%. [ABSTRACT FROM AUTHOR]

The coronavirus pandemic (COVID-19) threatens people's health. During the COVID-19 outbreak, people are encouraged to wear masks to reduce the spread of the virus. With the strong demand for masks, it has come a boom in counterfeit production. Combating counterfeit masks is vital and urgent to reduce the risks for public health. Motivated by the actual practices during the COVID-19, we examine how quality inspection and blockchain adoption help combat counterfeit masks. We find that quality inspection may not be always effective, as the government will tolerate the presence of counterfeit masks if the presence of the counterfeits is not significant. Comparing quality inspection with blockchain adoption, when the spread of COVID-19 is mild, authentic mask sellers may be encouraged to use the blockchain technology, which can increase their profits and reduce the social health risk. Furthermore, we extend our model to investigate the impacts of endogenous quality. Both quality inspection and blockchain adoption can induce low-quality mask sellers to enhance thequality level. When the number of counterfeit masks is increasing, encouraging the high-quality mask sellers to adopt the blockchain technology is effective to reduce social health risk when the spread of the coronavirus is rapid. [ABSTRACT FROM AUTHOR]

The complex networks of manufacturers, suppliers, retailers, and customers that make up today's pharmaceutical supply chain span worldwide. As it is, there needs to be more transparency in the traditional pharma supply chain. Also, the global nature of this industry makes it vulnerable to problems caused by a lack of transparency, distrust among involved entities, and reluctance to share data. Such lack of transparency mainly causes concerns regarding pharmaceutical product supply record forgery and counterfeiting of drugs. Supply chain traceability, which means following a product's journey from its manufacturing facility to its final consumers, is critically important, as it necessitates traceability, authenticity, and efficiency at a high level. This study proposes a blockchain-based secure and efficient traceable supply chain infrastructure for pharmaceutical products. Smart contracts are at the heart of the proposed solution, which tracks how all entities supply and record relevant events. Thus, all the involved entities can stay up-to-date on the latest state and guarantee a secure supply against any supply record forgery and counterfeit pharmaceutical products. In addition, we replicate the records in many chunks and use parallel search to achieve efficient traceability, which searches the stored records efficiently on the blockchain network. The comprehensive security analysis with standard theoretical proofs ensures the computational infeasibility of the proposed model. Further, the detailed performance analysis with test simulations shows the practicability of the proposed model. [ABSTRACT FROM AUTHOR]

The paper discusses the inscriptions of two ancient artifacts, the fragment of an Egyptian wine jug (Museum of Fine Arts, Budapest), and a terra sigillata bowl made by Pacatus (Aquincum Museum, Budapest), and seeks to situate them in the history of trade signs. In ancient Egypt, signs predominantly indicated details such as the place and date of production, and the quality of the product, while in Rome trade signs conveyed information concerning the origin of the product through the craftsman or workshop. [ABSTRACT FROM AUTHOR]

Original products (sold by a brand‐owner) are exposed to the dual challenge of imitation products (sold by a copycat) and deceptive counterfeit products (provided by a counterfeiter). In such cases, consumers face higher shopping risks and are more likely to experience post‐purchase regret. Blockchain can prevent the infiltration of deceptive counterfeits and the occurrence of regret, but its adoption can also raise consumer privacy concern. Hence, considering the above factors, we focus on the brand‐owner's blockchain adoption strategy. First, we define two types of regret: counterfeit‐purchasing regret (CPR) and low‐cost‐effective regret (LCR). Second, we consider three scenarios: scenario NR: without blockchain and considering only CPR, scenario NT: without blockchain and considering only LCR, and Scenario B: with blockchain. Finally, we compare the equilibrium results under scenarios NR and NT with those under scenario B, respectively. We interestingly find that blockchain adoption benefits the brand‐owner when (1) privacy concern is low or (2) privacy concern is moderate and the sensitivity factor of CPR is large or the sensitivity factor of LCR is small. Therefore, we advise brand‐owners to intervene in consumers' sensitivity to regret in order to make blockchain adoption work in their favor. [ABSTRACT FROM AUTHOR]

The scientific methodology for forensic examination of handwriting is based on studying the individual handwriting features and characteristics that distinguish its writer from others. The general qualities and the more detailed ones, some of which are "static" and refer to pictorical elements, while others refer to the pen's "dynamic" movement are investigated. Forensic handwriting experts during their daily activities are asked to analyse various features, many of which are suggested by the most prominent authors (connections, dimensions, proportions, shape, inclination, line quality, etc.), some of which are measurable, while others can only be inferred. However, the recent emergence of digitally captured signature (DCS) technology in the forensic field, has enabled the expert to analyse and evaluate new aspects, such as the length of the line. In the present study, an attempt has been made to study the line length and its possible uses in forensics handwriting examination processes. The authors, with different backgrounds and with different writing styles (Latin alphabet - Arabic alphabet), have tried to go further than the so far known study of handwriting for forensic purposes, studying and comparing the genuine lengths of the signature line and those that are forged (simulated).