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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]

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]

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]

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]

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]

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.

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 detection of copy-move forgeries has been of utmost relevance in the field of digital image forensics because of the explosive growth of image altering tools. The paper provides a thorough overview of current developments in copy-move forgery detection methods. Block-based, keypoints-based, and deep learning-based methods represent the three distinct categories into which the methodologies in the survey are divided. The papers in each category are thoroughly analysed, taking into consideration important factors including pre-processing techniques, feature extraction strategies, feature matching methods, and performance evaluation using various metrics and datasets. This survey study provides a thorough overview of the state of the field by methodically synthesizing and assessing the surveyed papers, and it also offers helpful insights for researchers and practitioners working to improve the accuracy and robustness of copy–move forgery detection methods in digital image forensics. [ABSTRACT FROM AUTHOR]

The real threat to the privacy of a plain document exchanged over insecure channels is content manipulation or eavesdropping by unauthorized parties. To protect a transferred document, the architecture proposed in this paper offers encryption, authentication, and scrambling GAN with shuffle confusion (EAGAN) comprehensively with a relatively rapid execution time average of 2s for each colored document. The EAGAN encrypts and signs the document content at the origin point (sender side) and then decrypts and verifies the cipher document at the receiver side, achieving two verification levels and three levels of confidentiality, reinforced with two keys of chaos and equal document content. Each document has a unique hash value (signature or identity) with a QR code watermark to detect forgery, even when the change is small (even by one bit), without depending on any third party. If case the intermediate encrypted document, the neural network model (Decoder-Key), the cipher document, or the initial values of the Chaos Key are leaked, an unauthorized person won't be able to retrieve the document. As proven by the experiments in this paper, EAGAN has characteristics that make it more resistant to security threats. [ABSTRACT FROM AUTHOR]

Sanctity and integrity of digital videos are crucial for the diverse real-world applications. It has significant social and legal implications. The technological advancements are posing new challenges as the video processing software that are typically designed to enhance the visual content, can adversely spawn unauthentic and malicious data that can be potentially hazardous. Robust algorithms are therefore needed to counter the deleterious effects. In this paper, we propose a passive-blind approach to detect and localize multiple kinds of inter-frame forgeries in digital videos like frame insertion, deletion and duplication. The forensic artefacts are designed based on correlation inconsistencies between the histogram-similarity patterns of the adjacent texture-feature encoded video frames. For the empirical evaluation, the algorithm uses texture features such as Histogram of Oriented Gradients (HoG), uniform and rotation invariant Local Binary Pattern (LBP). A customized dataset of 1370 tampered videos is created using the benchmark SULFA dataset due to lack of standard video dataset with inter-frame forgeries. A supervised SVM classifier is trained to detect video tampering where extensive analysis based on different histogram-similarity metrics is carried out with the proposed approach that exhibits an overall accuracy 99%. Further, the proposed method localizes the position of tampered frames in the video. It highlights forged frames using Chebyshev's inequality in case of frame insertion and deletion attacks. A comparative analysis with state-of-the-art methods is also presented that exhibits good efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

The dispute over the authenticity of video has become a hot topic in judicial practice in recent years. Despite detection methods being updated rapidly, methods for determining authenticity have limitations, especially against high-level forgery. Deleting the integral group of pictures (GOP) length in static scenes could remove key information in the video, leading to unjust sentencing. Anyone can conduct such an operation using publicly available software, thus escaping state-of-the-art detection methods. In this paper, we propose a detection method based on noise transfer matrix analysis. A pyramid structure and a weight learning module are adopted to improve the detection rate and reduce the false positive rate. In total, 80 videos were examined through delicate anti-forensic forgery operations to verify the detection performance of the proposed method and three previously reported methods against anti-forensic forgery operations. In addition, two of the latest learning-based methods were included in our experiments to evaluate the proposed method. The experimental results show that the proposed method significantly improves the detection of frame deletion points compared with traditional and learning-based methods, especially in low false positive rate (FPR) intervals, which is meaningful in forensic science. [ABSTRACT FROM AUTHOR]

Facial biometrics are widely used to reliably and conveniently recognize people in photos, in videos, or from real-time webcam streams. It is therefore of fundamental importance to detect synthetic faces in images in order to reduce the vulnerability of biometrics-based security systems. Furthermore, manipulated images of faces can be intentionally shared on social media to spread fake news related to the targeted individual. This paper shows how fake face recognition models may mainly rely on the information contained in the background when dealing with generated faces, thus reducing their effectiveness. Specifically, a classifier is trained to separate fake images from real ones, using their representation in a latent space. Subsequently, the faces are segmented and the background removed, and the detection procedure is performed again, observing a significant drop in classification accuracy. Finally, an explainability tool (SHAP) is used to highlight the salient areas of the image, showing that the background and face contours crucially influence the classifier decision. [ABSTRACT FROM AUTHOR]

In the current digital era, images have become one of the key channels for communication and information. There are multiple platforms where digital images are used as an essential identity, like social media platforms, chat applications, electronic and print media, medical science, forensics and criminal investigation, the court of law, and many more. Alternation of digital images becomes easy because multiple image editing software applications are accessible freely on the internet. These modified images can create severe problems in the field where the correctness of the image is essential. In such situations, the authenticity of the digital images from the bare eye is almost impossible. To prove the validity of the digital images, we have only one option: Digital Image Forensics (DIF). This study reviewed various image forgery and image forgery detection methods based on blind forgery detection techniques mainly. We describe the essential components of these approaches, as well as the datasets used to train and verify them. Performance analysis of these methods on various metrics is also discussed here. [ABSTRACT FROM AUTHOR]

We obtained crystals of novel supramolecular compounds with cucurbit[6]uril which contain aquanitrato complexes of mixed lanthanides. The supramolecular approach was used to produce compounds exhibiting multicolor emission. An X-ray diffraction study demonstrated that the compounds have the following compositions: [{Ln 10.5 Ln 20.5 (H2O)5(NO3)}2CB[6]](NO3)4·HNO3·nH2O (Ln1 = Eu, Ln2 = Pr (1), Nd (2), Sm (3), Gd (4), Tb (5) Dy (6), Er (8)), [{Ln 10.7 Ln 20.3 (H2O)5(NO3)}2CB[6]](NO3)4·HNO3·nH2O (Ln1 = Eu, Ln2 = Ho (7)) and 2[Ln 10.5 Ln 20.5 (H2O)8](NO3)3·CB[6]·2HNO3·mH2O (Ln1 = Eu, Ln2 = Yb (9)). The luminescence properties of compounds 1–9 were investigated in the solid state. The color purity of compound 6 is very close to zero, so it is well suited for white-light generation and can be used for WLED applications. A cool white LED device was fabricated by combining the supramolecular compound 6 and a UV LED chip (365 nm) at a voltage of 3.0 V; the CIE, CCT and CP were (0.328; 0.313), 5736 K and 2%, respectively. Compounds 5 and 6 have different glow colors when irradiated with different wavelengths; modulation of light emission in such compounds can be used to protect against counterfeiting. [ABSTRACT FROM AUTHOR]

Recently, a novel semi-quantum ring signature protocol (Quantum Inf. Process., 22(9), 337(2023)) was proposed. Although it has the properties of semi-quantum protocol, its verification process will fail when more than two users take part in the signature protocol. We show an example, in which four participants including the trusted third-party Trent are used, to prove the signature cannot be verified. Then, an improved semi-quantum ring signature protocol is proposed. In the new ring signature protocol, Trent generates the shared keys with each ring user. According to the shared keys, Trent encodes the GHZ state such that the identity of the true ring signatory can be traced by Trent. What is more, in our protocol, by measuring the received particles, all the ring users can create the same session key to generate a ring signature, which can guarantee the anonymity of the voter in the protocol. The improved ring signature protocol overcomes the drawback of the old one and can be secure against various eavesdropping attacks and forgery attack. Compared with the similar quantum ring signature protocols, ours has better practicability and efficiency. [ABSTRACT FROM AUTHOR]

One of the most pressing challenges in audio forgery detection—a major topic of signal analysis and digital forensics research—is detecting copy-move forgery in audio data. Because audio data are used in numerous sectors, including security, but increasingly tampered with and manipulated, studies dedicated to detecting forgery and verifying voice data have intensified in recent years. In our study, 2189 fake audio files were produced from 2189 audio recordings on the TIMIT corpus, for a total of 4378 audio files. After the 4378 files were preprocessed to detect silent and unsilent regions in the signals, a Mel-frequency-based hybrid feature data set was obtained from the 4378 files. Next, RNN and LSTM deep learning models were applied to detect audio forgery in the data set in four experimental setups—two with RNN and two with LSTM—using the AdaGrad and AdaDelta optimizer algorithms to identify the optimum solution in the unlinear systems and minimize the loss rate. When the experimental results were compared, the accuracy rate of detecting forgery in the hybrid feature data was 76.03%, and the hybrid model, in which the features are used together, demonstrated high accuracy even with small batch sizes. This article thus reports the first-ever use of RNN and LSTM deep learning models to detect audio copy-move forgery. Moreover, because the proposed method does not require adjusting threshold values, the resulting system is more robust than other systems described in the literature. [ABSTRACT FROM AUTHOR]

At present, in addressing security challenges within 5 G-era Vehicular Ad-Hoc Networks (VANET), a new protocol has been designed in this paper, named V2X-GKA (Dynamic Group Authentication and Key Agreement Protocol based on C-V2X). V2X-GKA utilizes cryptographic techniques such as ECDLP and DBDH to effectively mitigate risks associated with certificate forgery and key theft. Through the integration of authentication and group key agreement mechanisms, the protocol facilitates dynamic member management and secure key updates without necessitating complete protocol re-execution. This approach not only ensures both forward and backward security but also optimizes algorithmic processes, rendering it highly suitable for real-time, high-speed mobile environments. Through necessary security analysis, it is confirmed that the V2X-GKA scheme in this paper meets the existing security objectives. Compared with other similar schemes, it exhibits a certain degree of completeness and superiority, particularly in open-channel scenarios. [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]

The exponential progress of image editing software has contributed to a rapid rise in the production of fake images. Consequently, various techniques and approaches have been developed to detect manipulated images. These methods aim to discern between genuine and altered images, effectively combating the proliferation of deceptive visual content. However, additional advancements are necessary to enhance their accuracy and precision. Therefore, this research proposes an image forgery algorithm that integrates error level analysis (ELA) and a convolutional neural network (CNN) to detect the manipulation. The system primarily focuses on detecting copy-move and splicing forgeries in images. The input image is fed to the ELA algorithm to identify regions within the image that have different compression levels. Afterward, the created ELA images are used as input to train the proposed CNN model. The CNN model is constructed from two consecutive convolution layers, followed by one max pooling layer and two dense layers. Two dropout layers are inserted between the layers to improve model generalization. The experiments are applied to the CASIA 2 dataset, and the simulation results show that the proposed algorithm demonstrates remarkable performance metrics, including a training accuracy of 99.05%, testing accuracy of 94.14%, precision of 94.1%, and recall of 94.07%. Notably, it outperforms state-of-the-art techniques in both accuracy and precision. [ABSTRACT FROM AUTHOR]

In the digital age, images are widely used to document events, provide evidence, and communicate information. Ensuring the authenticity of digital images is crucial to maintaining trust and integrity in various domains, including journalism, forensics, legal proceedings, and historical documentation. In this research, a PeCA-DCNN model for digital image detection is proposed, employing a hybrid approach. Initially, the data from image forgery databases is collected and preprocessed to eliminate noise and artefacts. Subsequently, the Viola–Jones algorithm detects frontal faces, and feature extraction is performed using pre-trained models VGG-16 and Resnet-101. To reduce the computational overhead, feature extraction is performed and generates a feature vector. The PeCA algorithm, combined with an adaptive self-boosted DCNN, is used to classify fake and genuine images. The PeCA algorithm enhances model performance by adjusting classifier parameters’ weights and biases. When evaluating the PeCA-DCNN, significant improvements in accuracy, sensitivity, and specificity are obtained with enhancement rates of 1.48, 3.06, and 0.05 in an 80% training scenario, and 3.92, 3.24, and 2.22 in k-fold cross-validation. These results demonstrate the effectiveness of the proposed approach compared to existing techniques. [ABSTRACT FROM AUTHOR]

Image forgery is one of the issues that can create challenges for law enforcement. Digital devices can easily Copy-move images, forging medical photos. In the insurance industry, forensics, and sports, image forgery has become very common and has created problems. Copy-Move Forgery in Medical Images (CMFMI) has led to abuses in areas where access to advanced medical devices is unavailable. The proposed model (SEC) is a three-part model based on an evolutionary algorithm that can detect fake blocks well. In the first part, suspicious points are discovered with the help of the SIFT algorithm. In the second part, suspicious blocks are found using the equilibrium optimization algorithm. Finally, color histogram Matching (CHM) matches questionable points and blocks. The proposed method (SEC) was evaluated based on accuracy, recall, and F1 criteria, and 100, 97.00, and 98.47% were obtained for the fake medical images, respectively. Experimental results show robustness against different transformation and post-processing operations on medical images. [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]

In recent years, significant progress has been made in the field of face forgery and face forgery detection. However, the performance of the detection methods in the unknown environment is far beyond satisfactory due to the feature distribution deviation of different fake face generators. In this paper, we adopt the domain generalization theory to improve the generality of fake face detection. The utilized method augments the original image samples by introducing gradient noise yielded during back-propagation, simulating the forgery features in unknown domains. In the construction of the detection network, we propose a multi-scale synthetic artifact trace tracker (MSATT) to enhance the manipulation traces through multi-scale content suppression. Meanwhile, we observed that the synthesized images present a noticeable color abnormality after going through the proposed MSATT module. Therefore, we designed a color difference perception network (CDPNet) to capture this unique feature. Experimental results demonstrate that both the domain augmentation and the proposed CDPNet can effectively improve the performance of the detection network. The proposed method is competitive with the state-of-the-art face forgery detection methods on both intra- and inter-dataset evaluations. [ABSTRACT FROM AUTHOR]

The current deep learning methods for copy–move forgery detection (CMFD) are mostly based on deep convolutional neural networks, which frequently discard a large amount of detail information throughout convolutional feature extraction and have poor long-range information extraction capabilities. The Transformer structure is adept at modeling global context information, but the patch-wise self-attention calculation still neglects the extraction of details in local regions that have been tampered with. A local-information-refined dual-branch network, LBRT (Local Branch Refinement Transformer), is designed in this study. It performs Transformer encoding on the global patches segmented from the image and local patches re-segmented from the global patches using a global modeling branch and a local refinement branch, respectively. The self-attention features from both branches are precisely fused, and the fused feature map is then up-sampled and decoded. Therefore, LBRT considers both global semantic information modeling and local detail information refinement. The experimental results show that LBRT outperforms several state-of-the-art CMFD methods on the USCISI dataset, CASIA CMFD dataset, and DEFACTO CMFD dataset. [ABSTRACT FROM AUTHOR]

The article considers the issues of the secure operation of a two-level blockchain with a complex mixed consensus protocol, i.e., Proof-of-Stake in the main blockchain (mainchain) and Proof-of-Work in the secondary one (sidechain). This two-level blockchain is constructed on the principle of the Proof-of-Proof protocol, where the safety of the sidechain is ensured by the stability of the mainchain, by referring the mainchain blocks to the sidechain blocks using special transactions. Such a structure allows faster block creation in the sidechain and, accordingly, faster processing of transactions without the loss of security and without increasing the volume of the block. Such a two-level blockchain is of the greatest interest for the creation of a cascade system of state registers, which guarantees the security against the substitution and forgery of documents. The main results of the article are explicit analytical expressions for estimates of probability of double spend attack on a sidechain in a two-level blockchain, under the condition of presence of an adversary in the sidechain and the mainchain. [ABSTRACT FROM AUTHOR]

The article addresses concerns about the legality and safety of drug importation into the U.S., particularly for cancer care and employer health benefit plans. It highlights the ethical and legal responsibilities of medical practices and employers in ensuring drug safety and compliance with U.S. Food and Drug Administration (FDA) standards.

By drawing on ethnographic and historical accounts of migrant smuggling and forgery from the smugglers' perspectives, this essay shifts the focus from a state-centric understanding of smuggling as a criminal activity conducted by 'greedy' individuals and 'mafia rings', to a specific form of practice that operates through a series of concrete material techniques. It argues that smuggling recognises and reworks the material and technological features of borders which are vulnerable to reappropriation. These vulnerabilities inherent to the materialisation of abstract notions such as borders are understood and discussed as 'reproducibility', 'repetition', and 'imitation'. Smuggling not only reconfigures the apparatus of borders to provide support for those who are denied access to mobility, but also can teach us something about a specific mode of critique that I call a material mode of critique. The material mode of critiquing borders goes beyond a discursive mode which mostly locates politicians, academics, and activists as the critics of border politics. This mode can open up new possibilities of thinking about and against borders. [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]

Recent works have demonstrated the great performance of forgery image forensics based on deep learning, but there is still a risk that detectors could be susceptible to unknown illegal attacks, raising growing security concerns. This paper starts from the perspective of reverse forensics and explores the vulnerabilities of current image manipulation detectors to achieve targeted attacks. We present a novel reverse decision aggregate gradient attack under low-frequency constraints (RevAggAL). Specifically, we first propose a novel pixel reverse content decision-making (PRevCDm) loss to optimize perturbation generation with a specific principle more suitable for segmenting manipulated regions. Then, we introduce the low-frequency component to constrain the perturbation into more imperceptible details, significantly avoiding the degradation of image quality. We also consider aggregating gradients on model-agnostic features to enhance the transferability of adversarial examples in black-box scenarios. We evaluate the effectiveness of our method on three representative detectors (ResFCN, MVSSNet, and OSN) with five widely used forgery datasets (COVERAGE, COLUMBIA, CASIA1, NIST 2016, and Realistic Tampering). Experimental results show that our method improves the attack success rate (ASR) while ensuring better image quality. [ABSTRACT FROM AUTHOR]

The rapid evolution of technology has raised questions about the authenticity of digital images due to easy access to software for image manipulation. This challenges the reliability of cybernetic images, making it difficult for analysts to verify their authenticity. Therefore, there is a need to improve image forgery detection techniques. Many existing systems rely on standard pooling and a single approach, limiting the comprehensiveness and demonstration of features acquired. In response, this research introduces a Pixel-Based Optimized Deep Convolutional Neural Network (PBO-DCNN) classification and a Hybrid Boosting Machine method for detecting image fabrications. The technique employs blockwise feature extraction in a block-by-block manner and a Hybrid Boosting Machine segmentation approach to enhance detection. Performance analysis shows that the Hybrid Boosting Machine approach attained an accuracy of 94.74%, a sensitivity of 95.00%, and a specificity of 95.27% on the DSO-1 dataset. Further, the Hybrid Boosting Machine model attained the accuracy, sensitivity, and specificity values of 94.86%, 94.97%, and 93.38% on the DSI-1 dataset concerning the training percentage (TP). Comparative analysis against traditional techniques such as Linear Regression (LR), Deep CNN, SVM, Decision Tree (DT), and Naïve Bayes (NB) classifiers demonstrates the superior performance of the Hybrid Boosting Machine approach. [ABSTRACT FROM AUTHOR]

Certificateless Proxy Signature (CLPS) offers a comprehensive authentication mechanism to ensure the optimal solutions from modern forgery attacks in Industrial Internet of Things (IIoTs) environment. CLPS is part of certificateless public key cryptography (CL-PKC) and has the benefits of eradicating many issues like key distribution problem. Although costly computational processing capabilities are consumed by smart devices during authentication process. This article proposed an authentication scheme for CLPS by using the mathematical cryptographic curve concepts of Hyper Elliptic Curve (HEC) to provide a secure and optimized communication approach in IIoTs environment. The arises key distribution problem in CLPS is also fixed. The scrutinized security analysis of proxy signature and delegation signature were performed to tackle different attacks like Machine-in-the-middle Attack (MiTM), Replay Attack, Key Replacement Attack, Impersonation Attack, and Chosen Message Attack. Due to HEC-DLP the forgery attack cannot succeeded. Finally, the proposed work is formally verified through the AVISPA tool. The comprehensive performance evaluation shows that the total computational cost is reduced to 49.48% and the communication overhead size is reduced to 49.57% with a comparative analysis of existing schemes. Thus, the proposed approach eradicates key distribution problems with an equal security level of RSA and ECC. [ABSTRACT FROM AUTHOR]

Worldwide, counterfeit, illicit, and untaxed alcoholic spirits are responsible for problems with economy, labor, and public health. Estimations of counterfeit spirits range from 25% to 40% of total alcoholic spirits consumed globally. Including knock-on effects, these products cost the EU alone 23,400 lost jobs and at least €3B in lost revenue per year. Annually there is at least €1.2B in lost government revenue. Counterfeit products decrease legitimate sales, both by replacement sales, and by the erosion of consumer product trust and satisfaction of legitimate goods and decrease legitimate manufacturing jobs. We review the worldwide problem, scope, and scale of the spirits counterfeiting problem including specific health issues, and the international plight of reduced labor available resulting directly from production and sale of counterfeited liquor. In addition, we review a wide range of methods and technologies to analytically detect chemically adulterated or substituted products that have been published and group technologies into 4 functional areas highlighting economy, generality, and utility. Approaches to prevention are also discussed. [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]

Purpose: Counterfeit currency ranges from a low-quality colour scanner/printer notes to high-quality counterfeits produced by hostile powers. Detecting counterfeit money notes is a national priority due to its huge negative economic impact. However, no automatic mechanism exists for identifying the source of counterfeit notes, which is a more sophisticated and critical problem. This paper aims to evaluate the procedure or mechanism which are followed by banks for fake not reporting and the effectiveness of this mechanism. Design/methodology/approach: For doing this research, primary data has been collected from bank staff (particularly front desk staff like cashier/teller). A very simple and short questionnaire has been prepared where the researcher just wants to explore that up to what extent bank staff is aware about the clauses of the counterfeit currency note (CCN). Findings: The result shows that in approximately 38% of cases, bank staff return the fake notes to the customer without reporting, and approximately 57%–58% are unaware of the fake currency notes reporting to the nodal bank office and police. Practical implications: Banks themselves have to take stringent actions to ensure that none of the CCNs circulate again in the economy if caught by bank officials. All the flaws in a system should be effectively monitored along with covering the loopholes in the system like lack of training, time-to-time sensitisation of front desk employees, providing protection to employees in case of any kind of threat from customer and reducing the burden of retaining the customer if a customer is at guilty. Originality/value: This is the original work done by the researchers; in fact, the researchers were able to find only a couple of studies related to this kind of in-depth analysis in the literature. This study is done to provide feedback to the authorities on how the system is manipulated for organisational and self-interest. [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]

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Recommender systems are vulnerable to attacks because of their open nature. Counterfeit users give biased ratings to the items due to various objectives that may lead to the loss of user trust. The attackers use certain attack models with specific features. The existing attack detection techniques are typically attack-specific and work only when the attack features are known. They are unable to identify an unknown attack with unfamiliar features. To diminish this problem, in this paper, we propose a generalized solution that filters any attack irrespective of its design and features. We trained the classifiers with the ratings of the known authentic users using one-class SVM and PU learning models for detecting attacks, considering their ability to detect anomalies in the dataset caused by unknown attacks. The openly available MovieLens dataset has been used to assess our designed attack detection method. The experimental results show that all unknown attacks are successfully detected with 100% accuracy. The same detection accuracy is achieved for attacks with known features. [ABSTRACT FROM AUTHOR]