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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 paper describes the discovery of counterfeit coins in Pawłówek from the period between 1070 and 1100. Almost half of the coins were counterfeit and made of copper with various additives. The coins were used for treasure and tax purposes and come from various sources. Investigations using various methods show the diversity of the counterfeits and indicate that they were made by different social groups. [Extracted from the article]

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]

The article substantiates the expediency of involving a specialist during interrogation in the course of investigation of criminal offences in the field of public procurement. It is found that the tactical features of interrogations within criminal investigations in the field of public procurement are largely determined by a number of prerequisites which influence the choice of methods of influencing the interrogated person to ensure that he or she provides truthful and comprehensive testimony.lt is proved that the specifics of interrogation during the investigation of criminal offences in the field of public procurement are determined by the following procedural and forensic properties (features) which determine its essence and peculiarities of conduct: as its task; independence; involvement of specialists; use of forensic techniques; presence of an obligatory participant - the investigator; intellectual activity of the investigator; quantitative factor of subjects and participants in the course of this investigative action; special content of their rights and obligations; increased risk of conflict; mandatory procedural procedure for recording the course of interrogation and results and cultural (for tactical reasons, the possibility of recording its course and results by forensic means). The author establishes that effective interrogation tactics are based on an in-depth analysis of documents which could be forged or false, as well as on the testimony of previously interrogated persons, in particular, witnesses who can confirm or refute the information provided by suspects. The author confirms that when considering the issues of procedural and tactical peculiarities of a specialist's participation in the course of interrogation during the investigation of criminal offences in the field of public procurement, special attention should be paid to effective process management. It is proved that working with documents in the context of criminal investigations requires not only in-depth legal knowledge, but also specialised skills in the field of forensics and other related areas. It is established that the involvement of a specialist by an investigator during interrogation in the course of investigation of criminal offences in the field of public procurement allows for a deeper analysis of evidence, including official documents and procedures, ensures accurate establishment of offences related to forgery, and plays a key role in the preparation and conduct of interrogation. [ABSTRACT FROM AUTHOR]

Real-world face recognition systems are vulnerable to diverse face attacks, ranging from digitally manipulated artifacts to physically crafted spoofing attacks. Existing works primarily focus on using an image classification network to address one type of attack but disregarding another. However, face recognition systems in real-world scenarios always encounter diverse simultaneous attacks, rendering the aforementioned single-attack detecting solution ineffective. Besides, excessive reliance on a classifier might easily fail when encountering face attacks with unknown patterns, as the category-level difference learned by classification backbones cannot generalize well to new attacks. Considering that real data are captured from actual individuals, while attack samples are generated by various distinct techniques, our focus is on extracting compact representations of real faces. This approach allows us to identify the fundamental differences between genuine and attack images, enabling us to address both manipulated artifacts and spoofing attacks simultaneously. Concretely, we propose a dual space reconstruction learning framework that models the commonalities of genuine faces in both spatial and frequency domains. With the learned characteristics of real faces, the model is more likely to segregate diverse attack samples as outliers from genuine images. Besides, we introduce a dynamic filtering module that filters out the redundant information retained by the reconstruction and enhances the critical divergence between the real and the attack to achieve better classification features. Since the training samples only cover limited style variations, which hampers the generalization to unseen domains, we further design a consistency regularized training strategy that mimics distribution shifts during training and imposes specific constraints to encourage style-irrelevant features. Moreover, in view of the lack of accessible benchmarks for unified evaluation of the detection competence against both face forgery and spoofing attacks, we set up a new challenging benchmark, named UniAttack, to foster the exploration of effective solutions to face attack detection. Both qualitative and quantitative results from existing and proposed benchmarks unequivocally demonstrate the superiority of our methods over state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Generalization across various forgeries and robustness against corruption are pressing challenges of forgery detection. Although previous works boost generalization with the help of data augmentations, they rarely consider the robustness against corruption. To tackle these two issues of generalization and robustness simultaneously, in this paper, we propose a novel forgery detection generative adversarial network (FD-GAN), which consists of two generators (a blend-based generator and a transfer-based generator) and a discriminator. Concretely, the blend-based generator and the transfer-based generator can adaptively create challenging synthetic images with more flexible strategies to improve generalization. Besides, the discriminator is designed to judge whether the input is synthetic and predicts the manipulated regions with a collaboration of spatial and frequency branches. And the frequency branch utilizes Low-rank Estimation algorithms to filter out adversarial corruption in the input for robustness. Furthermore, to present a deeper understanding of FD-GAN, we apply theoretical analysis on forgery detection, which provides some guidelines on data augmentations for improving generalization and mathematical support for robustness. Extensive experiments demonstrate that FD-GAN exhibits better generalization and robustness. For example, FD-GAN outperforms 14 existing methods on 3 benchmarks in generalization evaluation, and it separately improves the performance against 6 kinds of adversarial attacks and 7 types of distortions by 16.2% and 2.3% on average in robustness evaluation. [ABSTRACT FROM AUTHOR]

Oscar Wilde's "The Portrait of Mr. WH." is concerned with the question of what it means to believe. Certain aspects and characteristics of belief as depicted in this story rely on a cognitive approach to identify its phenomenological qualities and how it operates. Wilde depicts belief as belonging to a unique kind of creative thought that is non-conscious, irrational, and collective. While cognition is typically understood as controlled, purposeful, goal-directed, independent and private, this discussion introduces two cognitive categories, spontaneous thought and intermental thought, to conceptualize aspects of cognition that are outside conscious control, opaque or inaccessible, aimless, communal, and reliant upon others. These categories reveal the ways that Wilde puts pressure on conventional definitions of, and assumptions about, belief and art in "The Portrait ofMr. W.H.". [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]

Due to the wide use of digital images in the virtual world and the growing attacks on them, passive image authentication schemes became an urgent and necessary need. The forgery attacks threaten the ownership of the digital image. Copy- move forgery detection is the main type of image forgery detection schemes. In this forgery, at least one region is copied and pasted at same image. In the proposed scheme, we proposed anew serial strategy through combining feature-based detector and block-based detector by using Region growing merging Segmentation method (RGMS).Multi scale Hessian-affine detector as primary detector, Polar cosine transform(PCT) as final detector and Simple linear iterative clustering segmentation(SLIC) with Factor Graph to construct RGMS. We designed serial random fitting model to include the true results and exclude the undesired results. Serial random supports single and multi-cloning regions. Our strategy provides facility to reveal and extract the iterative regions under different constraints. In addition, the proposed scheme is able to detect the slight and invisible cloning regions in the smooth digital image. This scheme is invariant to simple and complex attacks. Furthermore, it invariant to affine transformation likes skewness, distortion and perspective changes. Different datasets are used like GRIP, MICC-F600, MICC-F8.The experimental results confirmed that the proposed scheme has high percentage of True positive rates (99%), and low percentage of false positive rates (1.5%). The proposed scheme can be used as passive image forensic scheme in the cybercrimes and court rooms. [ABSTRACT FROM AUTHOR]

The proliferation of fake images generated by deepfake techniques has significantly threatened the trustworthiness of digital information, leading to a pressing need for face forgery detection. However, due to the similarity between human face images and the subtlety of artefact information, most deep face forgery detection methods face certain challenges, such as incomplete extraction of artefact information, limited performance in detecting low-quality forgeries, and insufficient generalization across different datasets. To address these issues, this paper proposes a novel noise-aware multi-scale deepfake detection model. Firstly, a progressive spatial attention module is introduced, which learns two types of spatial feature weights: boosting weight and suppression weight. The boosting weight highlights salient regions, while the suppression weight enables the model to capture more subtle artifact information. Through multiple boosting-suppression stages, the proposed model progressively focuses on different facial regions and extracts multi-scale RGB features. Additionally, a noise-aware two-stream network is introduced, which leverages frequency-domain features and fuses image noise with multi-scale RGB features. This integration enhances the model's ability to handle image post-processing. Furthermore, the model learns global features from multi-modal features through multiple convolutional layers, which are combined with local similarity features for deepfake detection, thereby improving the model's robustness. Experimental results on several benchmark databases demonstrate the superiority of our proposed method over state-of-the-art techniques. Our contributions lie in the progressive spatial attention module, which effectively addresses overfitting in CNNs, and the integration of noise-aware features and multi-scale RGB features. These innovations lead to enhanced accuracy and generalization performance in face forgery detection. [ABSTRACT FROM AUTHOR]

This work proposes a DL model for locating and classifying the forgery images. The proposed work has stages like pre-processing, feature extraction, segmentation, localization, and forgery detection. The input RGB images are converted into an YCbCr colour model in the pre-processing stage. Due to the size of the blocks, the processing time may be increased. Hence, the input images are split into overlapping blocks to reduce the time complexity. Here, a series of residual blocks are utilized for feature extraction, segmentation, and localization. Hybrid DL model Enhanced Mask-RCNN carries out this process. The Enhanced Mask-RCNN integrates the residual network with Mask-RCNN (Mask-region convolutional neural network). In this hybrid network, the residual network is used to extract the features, and the Mask-RCNN is used to segment, locate, and detect the tampered region. Further, the neural network weights are optimized by sandpiper optimization (SO) to enhance the recognition accuracy. The performance of a proposed forgery detection model is compared with three benchmark datasets and attained better accuracy of 0.991, 0.997, and 0.997 on the GRIP, Coverage, and CASIA-V1 datasets, respectively. [ABSTRACT FROM AUTHOR]

With the advancement of deepfake forgery technology, highly realistic fake faces have posed serious security risks to sensor-based facial recognition systems. Recent deepfake detection models mainly use binary classification models based on deep learning. Despite achieving high detection accuracy on intra-datasets, these models lack generalization ability when applied to cross-datasets. We propose a deepfake detection model named Channel-Spatial-Triplet Attention Network (CSTAN), which focuses on the difference between real and fake features, thereby enhancing the generality of the detection model. To enhance the feature-learning ability of the model for image forgery regions, we have designed the Channel-Spatial-Triplet (CST) attention mechanism, which extracts subtle local information by capturing feature channels and the spatial correlation of three different scales. Additionally, we propose a novel feature extraction method, OD-ResNet-34, by embedding ODConv into the feature extraction network to enhance its dynamic adaptability to data features. Trained on the FF++ dataset and tested on the Celeb-DF-v1 and Celeb-DF-v2 datasets, the experimental results show that our model has stronger generalization ability in cross-datasets than similar models. [ABSTRACT FROM AUTHOR]

Recently, remote sensing image forgery detection has received widespread attention. To improve the detection accuracy, we build a novel scheme based on Transformer and model fusion. Specifically, we model this task as a binary classification task that focuses on global information. First, we explore the performance of various excellent feature extraction networks in this task under the constructed unified classification framework. On this basis, we select three high-performance Transformer-based networks that focus on global information, namely, Swin Transformer V1, Swin Transformer V2, and Twins, as the backbone networks and fuse them. Secondly, considering the small number of samples, we use the public ImageNet-1K dataset to pre-train the network to learn more stable feature expressions. At the same time, a circular data divide strategy is proposed, which can fully utilize all the samples to improve the accuracy in the competition. Finally, to promote network optimization, on the one hand, we explore multiple loss functions and select label smooth loss, which can reduce the model's excessive dependence on training data. On the other hand, we construct a combined learning rate optimization strategy that first uses step degeneration and then cosine annealing, which reduces the risk of the network falling into local optima. Extensive experiments show that the proposed scheme has excellent performance. This scheme won seventh place in the "Forgery Detection in Multi-scenario Remote Sensing Images of Typical Objects" track of the 2024 ISPRS TC I contest on Intelligent Interpretation for Multi-modal Remote Sensing Application. [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 proliferation of AI-generated content (AIGC) has empowered non-experts to create highly realistic Deepfake images and videos using user-friendly software, posing significant challenges to the legal system, particularly in criminal investigations, court proceedings, and accident analyses. The absence of reliable Deepfake verification methods threatens the integrity of legal processes. In response, researchers have explored deep forgery detection, proposing various forensic techniques. However, the swift evolution of deep forgery creation and the limited generalizability of current detection methods impede practical application. We introduce a new deep forgery detection method that utilizes image decomposition and lighting inconsistency. By exploiting inherent discrepancies in imaging environments between genuine and fabricated images, this method extracts robust lighting cues and mitigates disturbances from environmental factors, revealing deeper-level alterations. A crucial element is the lighting information feature extractor, designed according to color constancy principles, to identify inconsistencies in lighting conditions. To address lighting variations, we employ a face material feature extractor using Pattern of Local Gravitational Force (PLGF), which selectively processes image patterns with defined convolutional masks to isolate and focus on reflectance coefficients, rich in textural details essential for forgery detection. Utilizing the Lambertian lighting model, we generate lighting direction vectors across frames to provide temporal context for detection. This framework processes RGB images, face reflectance maps, lighting features, and lighting direction vectors as multi-channel inputs, applying a cross-attention mechanism at the feature level to enhance detection accuracy and adaptability. Experimental results show that our proposed method performs exceptionally well and is widely applicable across multiple datasets, underscoring its importance in advancing deep forgery detection. [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]

Purpose: This paper aims to explore the issue of product counterfeiting in Türkiye and assess Türkiye's role in the global supply chain of counterfeit goods. It sheds light on the supply-side dynamics of counterfeiting in the Turkish context. Design/methodology/approach: Interviews were conducted with 46 key experts, including police officers, customs officers and trademark attorneys. The study also incorporated data from a documentary analysis of counterfeit products seized by the Bulgarian Customs. Findings: The findings of this study highlight the significant role of Türkiye in international supply chains, serving as both a manufacturing hub for a wide array of counterfeit products and a crucial transit point for goods bound for European markets. This study suggests that counterfeiting serves as a source of livelihood for many individuals in Türkiye, with counterfeiters often justifying their activities by claiming they contribute to the country's economy through job creation and the influx of foreign currency. Research limitations/implications: While qualitative research is essential for exploring nuanced aspects and gaining in-depth insights, it may not provide the statistical robustness and generalizability associated with larger quantitative studies. Originality/value: This paper is an original contribution to the understanding of product counterfeiting in Türkiye, a major counterfeit-producing country, with potential implications for the future of consumer protection and market integrity. [ABSTRACT FROM AUTHOR]

During fierce competition in the tourism and hospitality industry, value-based marketing strategies are increasingly considered important to attract and retain customers. This study aims to analyze the implementation of value-based marketing strategies in Transformer Center, a tourism and hospitality company located in Batu Tourism City, to improve the company's competitiveness. The research approach used is a case study with a qualitative method through in-depth interviews with management, direct observation and analysis of the company's internal documents. The results of the study show that the implementation of Value-Based Marketing through the development of services that meet the emotional and functional needs of customers has a significant impact on the Company's competitive advantage and is able to increase its attractiveness to a wider market segment. In conclusion, the implementation of Value-Based Marketing that focuses on customer needs can be a key factor in strengthening competitiveness in the dynamic tourism and hospitality sector. [ABSTRACT FROM AUTHOR]

Abstract: Target material sensing in non-invasive and ubiquitous contexts plays an important role in various applications. Recently, a few wireless sensing systems have been proposed for material identification. In this article, we introduce mm-CUR, A Novel Ubiquitous, Contact-free, and Location-aware Counterfeit Currency Detection in Bundles using a Millimeter-Wave Sensor. This system eliminates the need for individual note inspection and pinpoints the location of counterfeit notes within the bundle. We use Frequency Modulated Continuous Wave (FMCW) radar sensors to classify different counterfeit currency bundles on a tabletop setup. To extract informative features for currency detection from FMCW signals, we construct a Radio Frequency Snapshot (RFS) and build signal scalogram representations that capture the distinct patterns of currency received from different currency bundles. We refine the RFS by eliminating multi-path interference, and noise cancellation and apply high pass filters for mitigating the smearing effect with the continuous wavelet transform (CWT). To broaden the usage of mm-CUR, we built a transferable learning model that yields robust detection results in different scenarios. The classification results demonstrated that the proposed counterfeit currency detection system can detect counterfeit notes in 100-note bundles with an accuracy greater than 93%. Compared to the standard CNN and DNN methods, the proposed mm-CUR model showed superior performance in distinguishing each bundle data, even for a limited-size dataset. [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]

Recent advances in generative models and the availability of large-scale benchmarks have made deepfake video generation and manipulation easier. Nowadays, the number of new hyper-realistic deepfake videos used for negative purposes is dramatically increasing, thus creating the need for effective deepfake detection methods. Although many existing deepfake detection approaches, particularly CNN-based methods, show promising results, they suffer from several drawbacks. In general, poor generalization results have been obtained under unseen/new deepfake generation methods. The crucial reason for the above defect is that CNN-based methods focus on the local spatial artifacts, which are unique for every manipulation method. Therefore, it is hard to learn the general forgery traces of different manipulation methods without considering the dependencies that extend beyond the local receptive field. To address this problem, this article proposes a framework that combines Convolutional Neural Network (CNN) with Vision Transformer (ViT) to improve detection accuracy and enhance generalizability. Our method, named HCiT, exploits the advantages of CNNs to extract meaningful local features, as well as the ViT's self-attention mechanism to learn discriminative global contextual dependencies in a frame-level image explicitly. In this hybrid architecture, the high-level feature maps extracted from the CNN are fed into the ViT model that determines whether a specific video is fake or real. Experiments were performed on Faceforensics++, DeepFake Detection Challenge preview, Celeb datasets, and the results show that the proposed method significantly outperforms the state-of-the-art methods. In addition, the HCiT method shows a great capacity for generalization on datasets covering various techniques of deepfake generation. The source code is available at: https://github.com/KADDAR-Bachir/HCiT [ABSTRACT FROM AUTHOR]

The widespread dissemination of Deepfake in social networks has posed serious security risks, thus necessitating the development of an effective Deepfake detection technique. Currently, video-based detectors have not been explored as extensively as image-based detectors. Most existing video-based methods only consider temporal features without combining spatial features, and do not mine deeper-level subtle forgeries, resulting in limited detection performance. In this paper, a novel spatiotemporal trident network (STN) is proposed to detect both spatial and temporal inconsistencies of Deepfake videos. Since there is a large amount of redundant information in Deepfake video frames, we introduce convolutional block attention module (CBAM) on the basis of the I3D network and optimize the structure to make the network better focus on the meaningful information of the input video. Aiming at the defects in the deeper-level subtle forgeries, we designed three feature extraction modules (FEMs) of RGB, optical flow, and noise to further extract deeper video frame information. Extensive experiments on several well-known datasets demonstrate that our method has promising performance, surpassing several state-of-the-art Deepfake video detection methods. [ABSTRACT FROM AUTHOR]

Generative adversarial networks have shown impressive results in the modeling of movies and games, but what if such powerful image generation capability is used to harm the Multimedia? The face replacement methods represented by Deepfakes are becoming a threat to everyone, so the development of image authenticity detection methods has become a top priority. For achieving accurate detection resistant to compression effects, we propose a weighted complementary dual-stream detection method. First, to alleviate the influence of image compression on manipulation detection, we propose the concept of pixel-wise saliency invariance. We map fake images onto saliency maps via Quaternary Fourier Transform, which discovers the invariant properties of image phase spectra on different compressions. Meanwhile, to capture boundary traces more easily, we propose the concept of pixel-wise detail enhancement. We apply Bilateral Filtering to preserve the texture edges of fake images and amplify the fake boundaries. Finally, to take full advantage of the two proposed concepts, a weighted complementary dual-stream network is designed as a classifier to fuse features and identify real and fake. On different benchmarks like FaceForensics++ (FF++), Celeb-DF, and DFDC, the experimental results show that the proposed method has the average best detection accuracy compared to 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 Journal of Babylon Center for Humanities Studies 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.)

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]

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 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]

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]

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]