Search

The evolution of image manipulation techniques has presented a paradoxical scenario in contemporary visual culture. This phenomenon operates as a double-edged sword, offering both creative liberation and ethical dilemmas. Consequently, there is a need to develop automated mechanisms capable of discerning such forged data. The proposed methodology leverages transfer learning, utilising pre-trained deep learning models as a foundation and fine-tuning them specifically for the task of copy-move forgery detection. This approach uses the knowledge learned from large datasets, enhancing the network's ability to discern subtle patterns indicative of copy-move manipulations in images. Further, this research introduces a custom-designed CNN architecture tailored to the intricacies of copy-move forgery, optimising feature extraction and classification. Experimental evaluations conducted on diverse datasets, namely MICC-F220, MICC-F600, MICC-F2000, and CoMoFoD demonstrate the effectiveness of the proposed method with a True Positive Rate (TPR) of 100%. [ABSTRACT FROM AUTHOR]

Copy-move forgery (CMF) is a variety of image faking in which parts of an image are duplicated and added inside the same image. Up to now, many applications have been proposed to expose this type of forgery. These algorithms were developed to detect suspicious images containing CMF. However, when attacks such as compression and noise addition are applied to the image in such forgery operations, CMF detection (CMFD) approaches have difficulty in recognizing the fake images. This study presents a keypoint-based CMFD approach that performs well under various post-transaction changes. This is achieved by extracting scale-invariant feature transform (SIFT) and KAZE features using the colour channels in the HSV colour space instead of greyscale images. After the SIFT and KAZE descriptors have been extracted from each colour channel, they are combined to form the feature that is used in the matching step to detect similar regions. Thus, the proposed CMFD approach is more effective because stronger features are obtained when they are combined. The test findings on the Image Manipulation Dataset (IMD) demonstrated that our technique had successfully identified CMF under a variety of post-transaction alterations. [ABSTRACT FROM AUTHOR]

Saudi Law criminalizes forging of medical reports and certificates, and places it under crimes with light punishment in the penal law for forgery. Because of the wide spread commission of this crime among a category that should not be underrated, and the fact that a lot of people are ignorant of the legal consequence of being indicted for such an offence, it has become imperative to do a detailed research on this crime and to know the legal pillars of it and to evaluate the extent of adequacy of it light punishment in preventing its spread. The research aims at studying this crime according to Saudi Law and knowing the legal principles regulating its various aspects such as its meaning, pillars, legal consequence, and penalties. The researcher used the descriptive approach in reviewing the Saudi law, In addition to the analytical approach to the law articles related to the subject . The major research findings include: that the legal clauses criminalizing forging of medical reports and certificates did not differentiate between medical report and medical certificate. That the law equates the penalty of this crime for all professionals such as doctors, nurses, administrators in medical agencies and ordinary people. The research recommends the need for severe penalty in situation where the crime occurred with the knowledge of a general employee or in situation where the general employees commits this crime together with bribery. [ABSTRACT FROM AUTHOR]

The NC Office of the Chief Medical Examiner regularly assumes jurisdiction over deaths that are suspicious, unusual or unattended by a medical professional. In recent years, the presence of counterfeit pills is occasionally suggested by investigatory notes and/or scene findings that document reported consumption of prescription drugs, or prescription drugs on scene, which are not reflected in the final autopsy findings after toxicological analysis of the decedent's blood samples. Counterfeit pill consumption is a major public health hazard worthy of attention from the forensic toxicology community. Seventy-five cases from January 2020 to December 2022 serve as a convenience sample of cases where prescription pills including formulations of alprazolam, oxycodone and hydrocodone were specifically referenced during the death scene investigation as recently consumed, yet an unexpected substance was found during toxicological analysis rather than the expected pharmaceutical drug. Of note, novel benzodiazepines detected included flualprazolam, etizolam, clonazolam metabolite (8-aminoclonazolam), bromazolam, flubromazolam and desalkylflurazepam. Decedents' ages ranged from 16 to 69, across 33 different NC counties. Case notes indicated that eight of the decedents obtained pills through direct personal relationships, six decedents obtained them from "the street" and one decedent likely purchased pills online. Pills were largely consumed orally or through insufflation. Seven case reports contained indication that decedents knew or suspected the counterfeit nature of their pills. This study describes the context and characteristics of 2020–2022 suspected counterfeit pill-involved deaths in NC to further the understanding of the forensic science community, law enforcement partners, public health stakeholders and those potentially at risk through the consumption of counterfeit pills. [ABSTRACT FROM AUTHOR]

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

Recently, with the emergence of many image editing tools (photoshop, Topaz studio, etc.), the authenticity of images has been severely challenged. However, the performance of some existing traditional feature extraction methods and detection methods based on convolutional neural network (CNN) is poor, and the information provided by the features extracted from the network is limited and single. In this paper, an end-to-end ringed residual U-Net is proposed to detect image splicing forgery by blending features of non-natural regions. Some regions with significant differences from the image background are defined as non-natural regions(such as the irregular border at the splicing of images). In this paper, a feature enhancement module for non-natural regions is constructed, which the image through the pooling of four different scales, and these features are then combined with the original image and input to the backbone network for processing, aiming to highlight regions of the image that differ significantly from the background. Therefore, after adding the feature enhancement module for non-natural regions to the end-to-end ring residual U-Net, more attention will be paid to the tampering regions in the feature extraction stage, image manipulation detection and localization will also become more accurate. Compared with some mainstream methods, this method achieves better performance on the three standard datasets(CASIA2.0, NIST2016, COLUMBIA). In addition, it has excellent robustness under JPEG compression attack and noise corruption attack. [ABSTRACT FROM AUTHOR]

Oxford, Bodleian Library MS Arch. Selden. B. 24 has attracted attention in recent years for the witness it bears to the reception of Chaucer in late medieval Scotland. Its most striking example is the spurious seventy-nine-line ending attached to the Parliament of Fowls, edited afresh for this article. This article teases apart the work of the spurious ending's author from that of the Selden copyist and investigates how, when, and why this peculiar Scottish rewriting of the end of Chaucer's Parliament came about. That the spurious ending is indeed a Scottish composition is demonstrated through its linguistic features and sly allusions to two fifteenth-century Scots poems, Henryson's "The Cock and the Fox" and Richard Holland's Buke of the Howlat. These, in combination with textual and codicological details explored here, suggest strongly that this ending was written in conscious defiance, rather than ignorance, of Chaucer's conclusion to the Parliament. [ABSTRACT FROM AUTHOR]

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]

Handwritten documents possess immense significance in domains such as law, history, and administration. However, they are vulnerable to forgery, which can undermine their credibility and reliability. This paper aims to establish a dependable technique for identifying altered text in handwritten document images, even in scenarios with high levels of noise and blur. Our study investigates 10 distinct categories of handwritten text that have been altered through various forgery operations. The suggested approach employs the deep neural architectures VGG16 and Resnet50 as feature extractors. The architecture comprises three parts: Feature extraction using individual models, a feature fusion layer, and a classification layer. Initially, we optimize the training process and feature extraction using VGG16 and ResNet50. The feature vectors obtained from both models are then fused together in the feature fusion layer and input into the classification layer for the classification task. Experiments are conducted on a custom-created dataset as well as benchmark datasets including ICPR FDC, IMEI Forged Number, and Kundu to demonstrate that the proposed method is superior to existing approaches. [ABSTRACT FROM AUTHOR]

Handwritten documents possess immense significance in domains such as law, history, and administration. However, they are vulnerable to forgery, which can undermine their credibility and reliability. This paper aims to establish a dependable technique for identifying altered text in handwritten document images, even in scenarios with high levels of noise and blur. Our study investigates 10 distinct categories of handwritten text that have been altered through various forgery operations. The suggested approach employs the deep neural architectures VGG16 and Resnet50 as feature extractors. The architecture comprises three parts: Feature extraction using individual models, a feature fusion layer, and a classification layer. Initially, we optimize the training process and feature extraction using VGG16 and ResNet50. The feature vectors obtained from both models are then fused together in the feature fusion layer and input into the classification layer for the classification task. Experiments are conducted on a custom-created dataset as well as benchmark datasets including ICPR FDC, IMEI Forged Number, and Kundu to demonstrate that the proposed method is superior to existing approaches. [ABSTRACT FROM AUTHOR]

Copy-move forgery (CMF) is a new challenge because it reduces the accuracy of image forgery detection. In CMFD, we have selected and pasted similar points. The proposed method based on the Equilibrium Optimization Algorithm (EOA), Discrete Wavelet Transform (DWT), and Discrete Cosine Transform (DCT) helps image forgery detection. The method includes feature detection, image segmentation, and detection of forgery areas using the EOA, DWT, and DCT. In the first step, the image converts to a grayscale. Then, with the help of a discrete cosine transform algorithm, it is taken to the signal domain. With the help of discrete wavelet transform, its appropriate properties are introduced. In the next step, the image is divided into blocks of equal size. Then the similarity search is performed with the help of an equilibrium optimization algorithm and a suitable proportion function. Copy-move forgery detection using the Equilibrium Optimization Algorithm can find areas of forgery with a precision of about 86.21% for the IMD data set and about 83.98% for the MICC-F600 data set. [ABSTRACT FROM AUTHOR]

Video forgery, a prevalent concern in today's digital age, involves the deliberate manipulation of video content, often carried out using sophisticated video editing software. In response to this challenge, the need for an automated approach to detect forged video footage has become increasingly pressing. Our proposed methodology addresses this need by employing a multi-faceted strategy. It begins with the classification of video frames as either originating from genuine sources or having undergone manipulation. To assess the authenticity, the Δ r ¯ s metric is applied to evaluate the coherence of frame sequences. Additionally, we've harnessed the power of machine learning, training a model on a diverse dataset, namely the VIFFD dataset. This robust machine learning approach, particularly the suggested Support Vector Machine (SVM) method, consistently achieves an impressive average accuracy of 94.4%, showcasing its potential as a dependable and effective solution for video forgery detection. In an era where the trustworthiness of video content is of paramount importance, our method emerges as a pivotal safeguard, contributing significantly to the preservation of the integrity and credibility of visual media. [ABSTRACT FROM AUTHOR]

• Comprehensive study of an alleged forgery of a tempera painting on tile with apparently Botticelli features. • Pigments identification – i.e., iron oxide pigments, cinnabar, carbon black, and calcite) - by combined FORS and Raman spectroscopy. • Pigments mapping by SCM algorithm application to the multispectral image cube. • Colorimetric analysis to quantitatively evaluate the cleaning method and provide support for an informed restoration process. In this work, a tempera painting of uncertain attribution from the Uffizi Galleries was studied. The painting, which portraits a man with apparently Botticellian features, was probably created by one of the most skilled forgers of the early 20th century, Umberto Giunti. He used a flat tile as a support, covered with preparatory layers emulating a wall painting. To shed light on the authorship of the painting, it was crucial to investigate its composing materials, as well as the execution technique and the state of conservation. To this end, non-invasive spectroscopic and imaging optical techniques were applied. Specifically, VIS-NIR multispectral reflectography was used to reveal the presence of possible preparatory drawings, pentimenti and overpaintings. Raman and Fibre Optics Reflectance Spectroscopies allowed identifying the pigments used by the artist. Finally, colour variations due to the cleaning procedure were documented in order to provide a support for an informed restoration process. [ABSTRACT FROM AUTHOR]

This article analyses several promising policies in the electronic parts industry for disrupting the flow of counterfeit electronic parts. A socio-technical electronic part supply-chain network model has been developed to facilitate policy analysis. The model is used to understand the technical and social dynamics associated with the insertion of counterfeit electronic components into critical systems (e.g., aerospace, transportation, defense, and infrastructure) and to analyze the impact of various anti-counterfeiting policies and practices. This network model is used to assess the effectiveness of mandatory original component manufacturer buyback programs and the debarment of distributors found to provide counterfeit components. In this agent-based model, each participant in the supply chain is modeled as an independent entity governed by its own motivations and constraints. The entities in the model include the original component manufacturers, distributors, system integrators, operators, and counterfeiters. Each of these entities has dynamic behaviors and connections to the other agents. Since time is an integral factor (lead times and inventory levels can be drivers behind the appearance of counterfeits), the simulation is dynamic. The model allows the prediction of the risk of counterfeits making it into an operator's system and the length of time between relevant supply-chain events/disruptions and the appearance of counterfeits. [ABSTRACT FROM AUTHOR]

To sum up, many countries all over the world tend to activate their soft power to support their status and enhance their image with what present them positively to other communities. Therefore, the Korean government started to utilize its cultural activities, especially its musical and dramatic works through mass media. These works managed to fulfill billions of views through new visual mass media channels, especially yutube. As a result, this massive increase of views enhanced the expansion of local Korean culture, especially in 2010 because of the amazing progress of "K pop" music. This music has the ability to exceed the local audience and spread among international audiences. Another factor that helps to achieve this exceptional spread of this type of music is the unique technique of marketing which depends on emotional intelligence that aims at reaching followers in different ways. Since this type of music has a remarkable acceptance among Saudi youth, this study tries to investigate the degree of exposure to this type of Korean soft power among Saudi university students. The study also aims at uncovering the motives and the needs they satisfy through following the works of these musical bands and their members' personal news. The study also aims at finding the relationship between the students' standard of living and the degree of their simulation and imitation of these band members . More specifically, this study assures its results depending on the quantitative approach in scanning surveying students, the sample of study. By using surveys, the study will try to find any kind of cultural influence by observing their behaviors in certain situations related to cultural aspects and the degree of their exposure to the Korean culture. The study also tries to find whether Korean soft power intersects with the students' local cultures,identities and manners, keeping in mind this Korean culture "K pop" has become one of the most important tools that has been used on purpose by its original country to enhance its soft power or as it sometimes called " diplomacy or cultural attractivity". After surveying a sample of 334 students at King Abdulaziz University in Jeddah, the researcher has come to some certain results. 1-52% of the research sample follow the Korean culture ( musical bands and drama) and the majority of them belong to scientific majors with a percentage of 56%. 2- 54.8% of the research sample have been following the Korean musical band for three years or more and 63.8% of the sample have been following drama for three years or more. In addition, there are varied cognitive and emotional reasons that motivate Saudi youth to follow Korean culture and being influenced by it. 1- Following the Korean culture participates in knowing how to deal with other cultures. It also adds to their knowledge of the Korean language. 2- They also assume that following the Korean culture makes them closer to Eastern Asian communities. 3- Following the Korean musical bans and drama makes them happier and more optimistic. 4-It also makes them more eager to learn more about the Korean culture and change their previous opinions and beliefs about it. [ABSTRACT FROM AUTHOR]

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

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

Previous research has shown that Artificial Intelligence is capable of distinguishing between authentic paintings by a given artist and human-made forgeries with remarkable accuracy, provided sufficient training. However, with the limited amount of existing known forgeries, augmentation methods for forgery detection are highly desirable. In this work, we examine the potential of incorporating synthetic artworks into training datasets to enhance the performance of forgery detection. Our investigation focuses on paintings by Vincent van Gogh, for which we release the first dataset specialized for forgery detection. To reinforce our results, we conduct the same analyses on the artists Amedeo Modigliani and Raphael. We train a classifier to distinguish original artworks from forgeries. For this, we use human-made forgeries and imitations in the style of well-known artists and augment our training sets with images in a similar style generated by Stable Diffusion and StyleGAN. We find that the additional synthetic forgeries consistently improve the detection of human-made forgeries. In addition, we find that, in line with previous research, the inclusion of synthetic forgeries in the training also enables the detection of AI-generated forgeries, especially if created using a similar generator. [ABSTRACT FROM AUTHOR]

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

Quantum secret sharing plays a crucial role in quantum cryptography. In this paper, we present a rational hierarchical (t,n)-threshold quantum secret sharing scheme based on Lagrange interpolation. In our scheme, participants possess rational and hierarchical properties, and the secret can be reconstructed when the number of rational participants satisfies the hierarchical (t,n)-threshold structure proposed in this paper. The reconstructed secret can encompass both classical information and quantum state information, enhancing the practicality and flexibility of our scheme compared to existing ones. Additionally, we redefine the utility of participants based on their roles in the secret recovery process. This newly defined utility allows for a more precise analysis of the correctness, fairness, and equilibrium of our scheme. Finally, our scheme not only resists a typical set of external attacks but also incorporates mechanisms to detect forgery and collusion among participants. [ABSTRACT FROM AUTHOR]

With the development of generative models, abused Deepfakes have aroused public concerns. As a defense mechanism, face forgery detection methods have been intensively studied. Remote photoplethysmography (rPPG) technology extract heartbeat signal from recorded videos by examining the subtle changes in skin color caused by cardiac activity. Since the face forgery process inevitably disrupts the periodic changes in facial color, rPPG signal proves to be a powerful biological indicator for Deepfake detection. Motivated by the key observation that rPPG signals produce unique rhythmic patterns in terms of different manipulation methods, we regard Deepfake detection also as a source detection task. The Multi-scale Spatial–Temporal PPG map is adopted to further exploit heartbeat signal from multiple facial regions. Moreover, to capture both spatial and temporal inconsistencies, we propose a two-stage network consisting of a Mask-Guided Local Attention module (MLA) to capture unique local patterns of PPG maps, and a Temporal Transformer to interact features of adjacent PPG maps in long distance. Abundant experiments on FaceForensics + + and Celeb-DF datasets prove the superiority of our method over all other rPPG-based approaches. Visualization also demonstrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Due to dynamic changes, applications designed for the Internet of Things (IoT) require new storage resources. Storing IoT data in a fog infrastructure is a common practice to guarantee the IoT application's high performance. Multiple fog nodes spread the data replica to accommodate multiple users and reduce access latency. However, such data replicas are subjected to intentional and accidental corruption. This paper proposes a data integrity verification approach in a distributed fog environment. The proposed work uses fuzzy clustering and consensus mechanism to verify the integrity of multiple replicas stored on distributed fog nodes. Security analysis shows that the proposed approach is highly resistant to replace, replay, and forgery attacks. Performance analysis demonstrates the practicability of the proposed approach in a fog environment. [ABSTRACT FROM AUTHOR]

As a decentralized network infrastructure, the data sent to the blockchain are public and temper-evident. The cover of massive normal transactions in a blockchain network is ideal for constructing a stable and reliable covert channel to achieve one-to-many group covert communication. Existing blockchain-based covert communication schemes face challenges in balancing concealment, embedding rate and filtering efficiency, making them unsuitable for direct extension to group scenarios. Adopting a key-leakage scheme can increase the channel capacity while maintaining high concealment from external adversaries. However, it will also expose more knowledge to the receiver. A malicious receiver has the ability to steal a sender's identity or replay historical transactions to control the entire channel. In this paper, we define the capabilities of malicious receivers in blockchain-based group covert communication scenarios and propose a group covert communication scheme resistant to transaction forgery attacks. Theoretical analysis and experiments prove that our covert transactions do not have any transaction correlativity, ensuring the unique authenticity of the sender's identity while maintaining supreme concealment compared with the existing schemes. The precision and recall of machine learning detection results can reach 0.57–0.62 (0.5 is the ideal value). [ABSTRACT FROM AUTHOR]

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

Using generative adversarial network (GAN) Goodfellow et al. (2014) for data enhancement of medical images is significantly helpful for many computer-aided diagnosis (CAD) tasks. A new GAN-based automated tampering attack, like CT-GAN Mirsky et al. (2019), has emerged. It can inject or remove lung cancer lesions to CT scans. Because the tampering region may even account for less than 1% of the original image, even state-of-the-art methods are challenging to detect the traces of such tampering. This paper proposes a two-stage cascade framework to detect GAN-based medical image small region forgery like CT-GAN. In the local detection stage, we train the detector network with small sub-images so that interference information in authentic regions will not affect the detector. We use depthwise separable convolution and residual networks to prevent the detector from over-fitting and enhance the ability to find forged regions through the attention mechanism. The detection results of all sub-images in the same image will be combined into a heatmap. In the global classification stage, using gray-level co-occurrence matrix (GLCM) can better extract features of the heatmap. Because the shape and size of the tampered region are uncertain, we use hyperplanes in an infinite-dimensional space for classification. Our method can classify whether a CT image has been tampered and locate the tampered position. Sufficient experiments show that our method can achieve excellent performance than the state-of-the-art detection methods. [ABSTRACT FROM AUTHOR]

Malicious individuals can modify speech recordings with advanced audio editing software to create forged audio. The most common forgery method, known as audio copy-move forgery, involves copying part of the audio to duplicate or delete a segment. Considering the fact that the speech recording is used as evidence in court, it is of great importance to detect whether the voice recordings are forged or not. To this end, we present an effective and robust method based on BRIEF and OPTICS to detect and locate audio copy-move forgeries. The proposed method uses super-resolution spectrogram images of the input audio to detect forged parts in suspicious audio recordings. For this purpose, key points and their feature descriptors are first extracted from the spectrogram image using the BRIEF method. The ordering points to identify the clustering structure method (OPTICS) is used by the approach to match the corresponding descriptors. The proposed approach to eliminate false matches evaluates the correctness of the matches. The method also marks the corresponding forged segments in the audio file based on the location of the keypoints in these clusters. The performance results show that the proposed method has significantly high robustness to post-processing attacks such as noise addition, filtering, and especially compression, as reported in the literature. [ABSTRACT FROM AUTHOR]

The evaluation and classification of chemical properties in different copypaper products could significantly help address document forgery. This study analyzes the feasibility of utilizing infrared spectroscopy in conjunction with machine learning algorithms for classifying copy-paper products. A dataset comprising 140 infrared spectra of copy-paper samples was collected. The classification models employed in this study include partial least squares-discriminant analysis, support vector machine, and K-nearest neighbors. The key findings indicate that a classification model based on the use of attenuated-total-reflection infrared spectroscopy demonstrated good performance, highlighting its potential as a valuable tool in accurately classifying paper products and ensuring assisting in solving criminal cases involving document forgery. [ABSTRACT FROM AUTHOR]

From 2018 to 2021, seizures of counterfeit oxycodone pills containing non-pharmaceutical fentanyl or other novel synthetic opioids increased significantly contributing to continuing increases in overdose mortality in Northern America. Evidence suggests that counterfeit pills are distributed through cryptomarkets. This article presents data regarding the availability and characteristics of oxycodone pills advertised on one major cryptomarket between January and March 2022. Collected data were processed using a dedicated Named Entity Recognition algorithm to identify oxycodone listings and categorized them as either counterfeit or pharmaceutical. Frequency of listings, average number of pills advertised, average prices per milligram, number of sales, and geographic indicators of shipment origin and destination were analyzed. In total, 2,665 listings were identified as oxycodone. 48.2% (1,285/2,665) of these listings were categorized as counterfeit oxycodone, advertising a total of 652,699 pills (93,242.7 pills per datapoint) offered at a lower price than pharmaceutical pills. Our data indicate the presence of a large volume of counterfeit oxycodone pills both in retail- and wholesale-level amounts mostly targeting US and Canadian customers. These exploratory findings call for more research to develop epidemiological surveillance systems to track counterfeit pill and other drug availability on the Dark web environment. [ABSTRACT FROM AUTHOR]

Secure and scalable data sharing is one of the main concerns of the Internet of Things (IoT) ecosystem. In this paper, we introduce a novel blockchain-based data-sharing construction designed to ensure full anonymity for both the users and the data. To share the encrypted IoT data stored on the cloud, users generate tokens, prove their ownership using zk-SNARKs, and target the destination address anonymously. To tackle the privacy concerns arising from uploading the data to the cloud, we use key-private re-encryption and share only the necessary information with the proxy. As the first time in the literature, we have integrated a token-based blockchain and a key private proxy re-encryption to achieve a fully anonymous data sharing scheme. Furthermore, we provide security proof of our proposed scheme is secure against existential forgery under chosen-plaintext attacks, under eDBDH assumption in the random oracle model. [ABSTRACT FROM AUTHOR]

Secure and scalable data sharing is one of the main concerns of the Internet of Things (IoT) ecosystem. In this paper, we introduce a novel blockchain-based data-sharing construction designed to ensure full anonymity for both the users and the data. To share the encrypted IoT data stored on the cloud, users generate tokens, prove their ownership using zk-SNARKs, and target the destination address anonymously. To tackle the privacy concerns arising from uploading the data to the cloud, we use key-private re-encryption and share only the necessary information with the proxy. As the first time in the literature, we have integrated a token-based blockchain and a key private proxy re-encryption to achieve a fully anonymous data sharing scheme. Furthermore, we provide security proof of our proposed scheme is secure against existential forgery under chosen-plaintext attacks, under eDBDH assumption in the random oracle model. [ABSTRACT FROM AUTHOR]

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

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

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

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

Due to their robustness against large-scale geometric transformations, keypoint-based detection methods play an important role in revealing copy–move evidence. However, where copy–move forgeries are only involved in low-contrast or small or smooth regions, these approaches do not yield high-performance results because the number of keypoints in these regions is very limited. We recommend a highly efficient copy–move forgery detection algorithm by ADaptive Scale-Invariant Feature Transform (ADSIFT). Initially, by adapting the gamma factor for contrast threshold and rescaling factor values for feature matching and forgery detection, we produce an adequate number of keypoints that occur even in low-contrast or small or smooth regions of noisy or noiseless images. The proposed approach delivers greater performance than the existing methods as compared to precision and efficiency. [ABSTRACT FROM AUTHOR]

This study examines substances identified during testing of counterfeit prescription pills seized by law enforcement in Rhode Island from 2017 to 2022. [ABSTRACT FROM AUTHOR]

This article analyses in detail a collection of seven counterfeit volumes of the Mercure galant—the main cultural periodical and principal propaganda machine of the late seventeenth-century—kept in the Austrian National Library in Vienna. These counterfeits, which have never been studied before, reveal the efforts and strategies undertaken by the Dutch bookseller Abraham Wolfgang to copy the text, engravings and music in each monthly issue. Analysing this set also reveals the financial and material constraints faced by the Parisian publishers of the original Mercure. Finally, the binding and bookplate reveal important clues regarding the international distribution and readership of Le Mercure galant, as well as its transformation from a periodical to an historical account. [ABSTRACT FROM AUTHOR]

Due to the drastic increase in the generation of high-quality fake images in social networking, it is essential to design effective recognition approaches. Image/video manipulation defines any set of actions which can be carried out on digital content by the use of software editing approaches or artificial intelligence. A major kind of image and video editing comprises replicating the regions of the image, named as copy-move technique. Conventional image processing methods physically search for the pattern relevant to the replicated contents, restricting the utilization in massive classification of data. Contrastingly, the recently developed deep learning (DL) models have exhibited promising performance over the traditional models. In this aspect, this paper presents a novel intelligent deep learning based copy move image forgery detection (IDL-CMIFD) technique. The proposed IDL-CMIFD technique intends to design a DL model to classify the candidate images into two classes: original and forged/tampered and then localized the copy moved regions. In addition, the proposed IDL-CMIFD technique involves the Adam optimizer with Efficient Net based feature extractor to derive a useful set of feature vectors. Moreover, chaotic monarch butterfly optimization (CMBO) with deep wavelet neural network (DWNN) model is applied for classification purposes. The CMBO algorithm is utilized to optimally tune the parameters involved in the DWNN model in such a way that the classification performance gets improved. The performance validation of the proposed model takes place on benchmark MICC-F220, MICC-F2000, MICC-F600 datasets. A wide range of comparative analyses is performed and the results ensured the better performance of the IDL-CMIFD technique in terms of different evaluation parameters. [ABSTRACT FROM AUTHOR]

With advent of digital devices, we are surrounded by many digital images. We usually believe on digital images in whatever form presented to us. Therefore, we need to be careful as the images may be forged. There exist several image forgeries through which original intent of the image may be hidden and some other meaning is reflected through forgery. Copy-move forgery is one such forgery technique, where the manipulator copies certain portion of the image and duplicates it in some other portion of the same image. In this paper, we propose a novel approach to detect the copy-move forgery in images using non-overlapping block level pixel comparisons and that can achieve better detection and classification accuracy. This approach divides image into 4, 5, 6 or more such blocks and compare each block by moving sliding window over the entire image which is not overlapping with current block. It was found that with different number of blocks the forged region of different sizes can be easily found. We have used SSIM (structure similarity index) parameter to classify the image as forged or original. Algorithm is simulated on various datasets including (MICC, CASIA, coverage, and COMOFOD, etc.) and achieved maximum accuracy of 98% and also compared our result on precision, recall, FPR and FNR including other parameters. [ABSTRACT FROM AUTHOR]

Žižek supposes that traumas can turn into death-drives and offer the subject surplus jouissance. He warns that ideological systems can profit from traumas to further subjugate their citizens. Notwithstanding, Badiou construes that traumas can appear as tremendous moments of Truth/Events that betrays the voids of the Symbolic Order and actualizes the universal truths that postmodernism has constantly denied or endeavored to suppress. In so thinking, Badiou hypothesizes that the Truth/Event will find/invent its own faithful subject that cooperate to actualize the suppressed or denied Real of their age. Badiou criticizes the Western Ethics for devising a secured mode of life that emasculates the subject of post capitalism age and deprives him of experiencing the sufferings that can confront him with the Real. Badiou states that the Western ethics deliberately ignores the positive effects of PTSD recoveries that can reveal a lot regarding the psychological and social weakness of the subject and the society as well. Defending the idea of traversing the fantasies and encountering the Real, Žižek, however, does not become convinced of the emancipatory force that Badiou attributes to traumatic Events. Instead, he announces that the subject's fidelity to the Badiouian Truth/Event approximates to the devoted insurgents' allegiance to the 'mythic violence' that enables them to disclaim responsibility for their deeds. Badiou himself is apprehensive of ever-present 'simulacra' that the sovereign ideology concocts to counterfeit the Truth/Event. What Žižek prescribes is an 'Act' that can be embodied in 'divine violence' that divulges the Symbolic Order's void. [ABSTRACT FROM AUTHOR]

The authenticity, privacy, and nonrepudiation of IoT applications are all guaranteed by the ID-Based Signature (IDBS) cryptographic technique. Digital communication presents challenges for information evaluation and removal-back transmission. Security was consequently needed in a cloud-based system to interact with the correct location or nodes. Furthermore, in a cloud-based environment, it is difficult to track a group's key escrow and secret key generations. We improve group signature security, an identity-based method, to address these problems. The strategy asks a member to sign documents on the group's behalf. The group manager (GM) membership is controlled by two protocols: join and revoke. The GM may utilize the Open protocol to determine who signed a communication. The classic ID-Based Group Signature (IDBGS) method uses a public-key generator (PKG). This method is founded on difficult mathematical ideas like the computational Diffie–Hellman assumptions. Based on the ID-Based GS system, this technique enhances group members' identity, legitimacy and visibility. The public key size and signature depend on the group members' autonomy. The improved method guarantees realistic confirmation of nodes and fortifies them against forgery attempts. This solution promises faster execution and increased dependability for secure nodes. [ABSTRACT FROM AUTHOR]

Face manipulation technology is rapidly evolving, making it impossible for human eyes to recognize fake faces in photos. Convolutional Neural Network (CNN) discriminators, on the other hand, can quickly achieve high accuracy in distinguishing fake from real face photos. In this paper, we investigate how CNN models distinguish between fake and real faces. According to our findings, face forgery detection heavily relies on the variation in the texture of the images. As a result of the aforementioned discovery, we propose a deep texture variation network, a new model for robust face fraud detection based on convolution and pyramid pooling. Convolution combines pixel intensity and pixel gradient information to create a stationary representation of composition difference information. Simultaneously, multi-scale information fusion based on the pyramid pooling can prevent the texture features from being destroyed. The proposed deep texture variation network outperforms previous techniques on a variety of datasets, including Faceforensics++, DeeperForensics-1.0, Celeb-DF, and DFDC. The proposed model is less susceptible to image distortion, such as JPEG compression and blur, which is important in this field. [ABSTRACT FROM AUTHOR]

Now-a-days biometric systems are useful in recognizing person’s identity but criminals change their appearance in behaviour and psychological to deceive recognition system. To overcome from this problem we are using new technique called Deep Texture Features extraction from images and then building train machine learning model using CNN (Convolution Neural Networks) algorithm. This technique refers as LBPNet or NLBPNet as this technique heavily dependent on features extraction using LBP (Local Binary Pattern) algorithm. In this project we are designing LBP Based machine learning Convolution Neural Network called LBPNET to detect fake face images. Here first we will extract LBP from images and then train LBP descriptor images with Convolution Neural Network to generate training model. Whenever we upload new test image then that test image will be applied on training model to detect whether test image contains fake image or non-fake image. Below we can see some details on LBP. Local binary patterns (LBP) is a type of visual descriptor used for classification in computer vision and is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. Due to its discriminative power and computational simplicity, LBP texture operator has become a popular approach in various applications. It can be seen as a unifying approach to the traditionally divergent statistical and structural models of texture analysis. Perhaps the most important property of the LBP operator in real-world applications is its robustness to monotonic gray-scale changes caused, for example, by illumination variations. Another important property is its computational simplicity, which makes it possible to analyze images in challenging real-time settings. [ABSTRACT FROM AUTHOR]

The application of biometrics has expanded the wings to many domains of application. However, various biometric features are being used in different security systems; the fingerprints have their own merits as it is more distinct. A different algorithm has been discussed earlier to improve the security and analysis of fingerprints to find forged ones, but it has a deficiency in expected performance. A multi-region minutiae depth value (MRMDV) based finger analysis algorithm has been presented to solve this issue. The image that is considered as input has been can be converted into noisy free with the help of median and Gabor filters. Further, the quality of the image is improved by sharpening the image. Second, the preprocessed image has been divided into many tiny images representing various regions. From the regional images, the features of ridge ends, ridge bifurcation, ridge enclosure, ridge dot, and ridge island. The multi-region minutiae depth value (MRMDV) has been computed based on the features which are extracted. The test image which has a similarity to the test image is estimated around MRMDV value towards forgery detection. The MRMDV approach produced noticeable results on forged fingerprint detection accuracy up to 98% with the least time complexity of 12 seconds. [ABSTRACT FROM AUTHOR]

With the gradual maturity of Smart Grid (SG), security challenges become the most important issues that need to be addressed urgently. In recent years, many schemes adopt mutual authentication and key agreement to ensure secure communication in SG. However, most existing methods have their own shortcomings in either security or efficiency which make them difficult to satisfy the security requirements of SG. In this paper, we propose a provable secure and lightweight authenticated key agreement scheme based on the Elliptic Curve Cryptosystem (ECC) for the cloud-edge-end collaboration SG communication network. The proposed scheme can guarantee the security of the communication and provide anonymity for both the edge server and the smart meters. The anonymity of the smart meters can be provided by a pseudonym mechanism. By rigorous security analysis, the proposed scheme can resist to the typical attacks including replay attacks and identity forgery attacks. The security properties are also evaluated by using the Canetti and Krawczyk (CK) adversary model. The performance simulation results denotes that the proposed scheme not only owns stronger security functions but also improves computation efficiency by 24.5% on average than other four schemes. By simulation results, the proposed scheme has been shown to hold high efficiency. [ABSTRACT FROM AUTHOR]

Deep learning advancements have resulted in breakthroughs in facial forgery techniques. Facial forgery videos are growing increasingly lifelike, making it impossible for people to tell the difference between the real and the fake. The proliferation of facial forgery techniques, as well as the slowness with which they may be detected, could jeopardize personal data security. As a result, it's critical to look at approaches that can be trained on both real and fake videos and then utilized to identify facial forgeries or not in videos. This study found that forged face videos undergo truncation between consecutive frames of optical flow imaging after dense optical flow processing. We present an approach for detecting video face forgery that extracts and analyzes characteristics from real and fake material, then use those features to train classification models on Celeb-DF and FaceForensics++. In addition, we employ a unique facial double-triangle region to assist in the extraction of video inter-frame feature data. Experiments results show that the facial motion features extracted from the double triangle region successfully assist in capturing the dense optical flow truncation. Extensive evaluation suggests that our proposed approach is effective for video face forgery detection. [ABSTRACT FROM AUTHOR]

Educational certificate counterfeiting poses a significant global challenge, demanding urgent attention and robust solutions. This paper addresses the problem of forgery and verification of educational certificates by leveraging blockchain technology as an anti-counterfeiting measure. A decentralized architecture for educational certificate verification systems is proposed and implemented using the Hyperledger Fabric as a blockchain platform and the Inter Planetary File System (IPFS). By utilizing the unique features of these technologies, we aim to establish a reliable and secure system for verifying authenticity of educational certificates without relying on third-party intermediaries. Our approach involves storing educational certificate files on a decentralized file system, namely IPFS, ensuring their integrity and accessibility. To enable efficient verification, each certificate is associated with a certificate ID, which retrieves the necessary validating information from the Hyperledger Fabric network. This integration ensures that educational certificates are securely stored on the blockchain, preventing any tampering attempts and enabling easy validation. The experimental results demonstrate that educational certificates are stored on the Hyperledger Fabric network in a manner that ensures their immutability, making it impossible to alter them. Furthermore, the system allows for verification of their authenticity without the need for any third-party intervention. By combining the power of blockchain technology with IPFS, our solution provides a robust approach to tackle the growing issue of educational certificate counterfeiting. This research contributes to the field by offering a decentralized architecture that enhances the security and trustworthiness of educational certificate verification systems. [ABSTRACT FROM AUTHOR]

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

In the article, we present, comment and analyse some elements of our cultural and evangelical history, which have been marked by numerous philosophers and theologians starting from the ancient Church to the present day. Closing into the present actually means a ban on living. Present life has its roots in the past and offers its promises, expectations and courage to the future. History exists, and man can dwell in history, without which there is no creative culture, only when three dimensions of time are lived organically. In this context, we also observe the contribution of an immense procession of thinkers like St Thomas Aquinas in successive historical periods. In the article, we explore the possible contribution of metaphysics to the questions posed to the modern man. The evangelical influence on culture, which is a complex phenomenon, should certainly be shed light on. Furthermore, it can be more or less deep. It can be hidden, more or less conscious. It is not always strongly present where it is most required, it is not real where it is explicitly ordered. In addition to authentic fruits, one should also be aware of counterfeits and hypocritical activities. In short, its paths are different and manifold. It seems important to point this out so that we could evaluate this influence, which is authenticated and credibly confirmed in the eyes of Christian orthodoxy, and which establishes piety. Certain errors, exaggerations and deviations are not imaginable outside the Christian horizon, which unmask it or wage a battle with it. [ABSTRACT FROM AUTHOR]

As manipulated faces become more realistic and indistinguishable, there is a high demand for efficiently and accurately detecting deepfakes. Existing CNN-based deepfake detection methods either learn a global feature representation of the whole face or learn multiple local features. However, these methods learn the global and local features independently, thus neglect the spatial correlations between the local features and global context, which are vital in identifying different forgery patterns. Therefore, in this paper, we propose Spatial Interaction Network (SI-Net), a deepfake detection method to mine potential complementary and co-occurrent features between local texture and global context concurrently. Specifically, we first utilize a region feature extractor that distills local features from the global features, to simplify the procedure of local feature extraction. We then propose spatial-aware transformer to learn the co-occurrence feature from local texture and global context, concurrently. We capture the attended feature from the local regions according to their importance. The final prediction is made through the composite considerations of the aforementioned modules. Experimental results on two public datasets, FaceForensics++ and WildDeepfake, demonstrate the superior performance of SI-Net compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]