Authentic Ones Research Articles

Detecting GNSS spoofing and Re-localization on UAV based on imagery matching

Abstract Due to rapid advancements in global navigation satellite system (GNSS), computer, and microelectronics technologies, there is a growing popularity and widespread promotion of high-performance, cost-effective intelligent UAVs across various applications. Recently, GNSS spoofing attacks have emerged as a significant obstacle hindering the long-term development of UAVs. UAVs rely heavily on unprotected GNSS signals for navigation, making them highly vulnerable to spoofing. This paper presents an algorithm that employs image matching for detecting GNSS spoofing and re-localization on UAVs using a deep learning methodology. This method functions autonomously solely reliance on camera and does not require alterations to the antenna configuration. Utilizing a camera-equipped UAV, we evaluate the likeness between real-time aerial photographs and satellite imagery by leveraging the position information provided by UAV. By identifying disparities between images taken by a spoofing affected drone and authentic ones, the spoofing can be identified using deep neural network models. Upon detecting spoofing, this paper presents a vision-based re-localization method for UAVs. Experimental results demonstrate an approximately 88.4% accuracy of our model in detecting GNSS spoofing attacks within 100 ms and 88% success rate of re-localization with the accuracy of less than 15 m. Our algorithm exclusively depends on publicly accessible satellite imagery, offering an intelligent and efficient approach for detecting UAV GNSS spoofing and re-localization in GNSS denied environment.

A Rapid, Non-Destructive Method to Detect Counterfeit Integrated Circuits Using a Resonant Cavity System

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

PETA KAJIAN ORIENTALIS DALAM STUDI HADIS NABI

This article tries to discuss the mapping of hadith studies carried out by orientalists. The study of hadith from time to time continues to develop so that the content of the transition process must be taken into account. This research uses a qualitative methodology which focuses on the use of descriptive-qualitative methodology in library research to understand hadith. So the complexity of hadith studies encourages scholars of hadith studies in both the classical and contemporary eras to come up with ideas regarding methodologies and theories that can differentiate between fake hadiths and authentic ones. This study is also practiced by Orientalists, so in practice it is not only carried out by Muslims. So this development has given rise to a new paradigm based on honesty and openness from both hadith circles and other circles.

Deepfake Detection System

Abstract: Because deep fake technology allows for the creation of incredibly realistically modified material that has the potential to mislead viewers and possibly cause instability across a range of businesses, it poses a severe threat to modern society. These days, detecting such modified content is crucial to maintaining the trustworthiness and integrity of digital media. In response, this research proposes a robust deep learning-based technique for detecting deep fakes in videos. Our method uses the Deep fake Detection Challenge dataset, which contains both real and deep fake films, to train and assess our deep learning model. We want to provide a dependable solution that can differentiate between artificially generated information and actual content using stateof-the-art neural networks. Artificial intelligence-generated phony videos are more likely to spread thanks to deep learning algorithms, which raises serious concerns about official blackmail, terrorist propaganda, revenge pornography, and political manipulation. To alleviate these concerns, our approach is designed to automatically identify several forms of deep forgery, including replacement and re-enactment techniques. By means of extensive testing and analysis, we demonstrate the effectiveness of our deep fake detection system in precisely distinguishing bogus films from authentic ones. Our system, which combines cutting-edge deep learning methods with a large dataset, offers a promising way to lessen the hazards related to the spread of deep fake technologies

Signing documents: Accountability politics and racialized suspicion in Africa's development audits

AbstractGood governance policies in international development require nongovernmental organizations (NGOs) to translate their programs into documents that render NGOs knowable and accountable to their donors. Drawing on multisited ethnographic fieldwork in Ghana and Uganda, we examine the signatures on these documents and the labor of NGO staff to obtain the signatures of aid recipients. We argue that signatures serve as mechanisms that NGO staff use to make their good governance practices traceable and to deter donor suspicion of funding misuse. Staff dedicate significant energy to imagining how signatures may or may not trigger donor suspicion. We posit that despite NGO staff's anxiety over getting signatures right—present, matching, and signed by the correct person—signatures can only ever defer donor suspicion. Such suspicion cannot be eliminated because it is deeply entrenched in racialized logics that position Global North donors as holding expertise and African NGOs as susceptible to corruption. Because NGO staff worry about donor suspicion rather than what aid recipients communicate with their (lack of) signatures, even fake signatures can circulate just as well as authentic ones. Tracing the social dynamics of collecting signatures sheds light on the racialized injustices inherent in Africa's development systems.

Fake Currency Detection Using Deep Learning

Fake currency is the money produced without the approval of the government, creation of it is considered as a great offence. The progression of shading printing innovation has expanded the rate of Fake currency copying notes on a large scale. Albeit electronic monetary exchanges are turning out to be more popular and the utilization of paper cash has been diminishing as of late, banknotes still remain in distribution attributable to their dependability and straight forwardness in use. Few years ago, the printing should be possible in a printing-houses, yet presently anybody can print a money paper with most extreme exactness utilizing a straightforward laser printer. As an outcome, the issue of duplicate currency rather than the authentic ones has been increases generally. India had reviled the problems like defilement and dark cash and fake of money notes is likewise a big issue to it. To handle this problem, a deep learning-based framework is proposed to identify the fake Indian currency. The MATLAB tool has been used to identify the fake currency. The outcome will classify whether the Indian currency note is Real or Fake

Tensor robust principal component analysis via dual lp quasi-norm sparse constraints

As a pioneering method, tensor robust principal component analysis (TRPCA) can separate an underlying low-rank component and a sparse component from the original data by minimizing a convex objective function composed of tensor nuclear norm and l1-norm. However, it has two limitations. One is that tensor nuclear norm, as a constraint on the low-rank component, treats all singular values uniformly, ignoring the differences among singular values. In essence, this constraint is a sparse constraint that is imposed on singular values of the low-rank component by l1-norm. The other is that l1-norm is used as a constraint for the sparse component. However, l1-norm is a loose constraint, leading to the solutions of TRPCA deviating from the authentic ones. To alleviate these issues, we propose a TPRCA model called p-TRPCA that utilizes the lp-norm to impose sparse constraints on both the singular values and the sparse component simultaneously. The lp quasi-norm (0<p<1) is a tighter constraint than l1-norm, which enhances the low-rankness and sparsity of the proposed model. To solve p-TRPCA, we present an effective algorithm based on the alternating direction method of multipliers and also analyze its convergence. Extensive experiments are performed on data simulation, image recovery, and background modeling. Experimental results show that our p-TRPCA outperforms TRPCA and its variants.

Comparative Analysis of Fake Product Identification System Using Blockchain Technology

Fictitious products have emerged as a substantial challenge in the manufacturing sector, inflicting adverse consequences on a company's financial health, reputation, and overall prosperity. Thankfully, blockchain technology provides an effective remedy for discerning fake items and verifying the legitimacy of authentic ones, all within a decentralized and widely distributed digital ledger. Quick Response (QR) codes serve as pivotal tools in the fight against fictitious goods, as each product is now furnished with a QR code that acts as a direct bridge to the blockchain system, essentially bestowing each item with a digital identification card. QR code scanners communicate with the blockchain to promptly validate whether a product is genuine or fictitious. Moreover, customers can harness this technology to track a product's journey through the supply chain and authenticate ownership details, akin to a digital breadcrumb trail that securely preserves product information and unique codes as database blocks. Considering the globalized business landscape and the perpetual advancements in technology, industrial manufacturers and distributors are wholeheartedly committed to optimizing their supply chain processes, ensuring they remain one step ahead of fictitious product proliferation and fortifying their operations against the dissemination of spurious items. In this paper we compare Different Fake product identification Methods like Barcodes, QR Codes, RFID Tags, Serial numbers with methods using Blockchain technology which is having high security, transferability &amp; traceability throughout Supply Chain.

Artificial intelligence for honey integrity in Ghana: A feasibility study on the use of smartphone images coupled with multivariate algorithms

Rapid, onsite, and simple detection of honey integrity in real time provides consumers with the assurance that the honey purchased is free from adulteration such as sugar syrup. In this study, smartphone based camera technique was used to differentiate authentic honey from adulterated ones (using authentic honey samples from different locations and adulterated samples made by spiking authentic ones with sugar syrup in the laboratory) by employing two supervised machine learning algorithms. Experimental results showed that the prediction model based on Random Forest (RF) was superior to K-nearest neighbour (KNN). The optimum results were assessed based on the prediction rate, specificity, and sensitivity. The performance of the RF model was 100 % accuracy while the specificity of 100 %, and sensitivity of 100 %. These findings could be exploited for reliable and rapid classification of honey integrity in Ghana and West Africa in general. This will further improve consumers’ confidence in the honey trade due to the ease and availability of smartphone technology.

Identification of Fake Currency Using Convolution Neural Network

The progression of shading printing innovation has expanded the rate of Fake currency copying notes on a large scale. Albeit electronic monetary exchanges a returning out to be more popular and the utilization of paper cash has been diminishing as of late, banknotes still remain in distribution attributable to their dependability and straight forwardness in use. Few years ago, the printing should be possible in a printing-house, yet presently anybody can print a money paper with most extreme exactness utilizing a straightforward laser printer. As an outcome, the issue of duplicate currency rather than the authentic ones has been increases generally. India had reviled the problems like defilement and dark cash and fake of money notes is likewise a big issue to it. To handle this problem, a deep learning- based framework is proposed to identify the fake Indian currency. The MATLAB tool has been used to identify the fake currency. The outcome will classify whether the Indian currency note is Real or Fake.

A method to build feature descriptor for GNSS spoofing detection by carrier phase double difference measurement

GNSS spoofing is a type of attack that aims to deceive GNSS receivers by transmitting fake signals that imitate the authentic ones. To detect such attacks, a possible solution is to calculate the double difference (DD) of carrier phase measurements between two antennas of two separated receivers. This DD measurement represents the angle of arrival (AoA) information of the signal. In the case of authentic signals, each signal comes from a different satellite, so the AoA values are different for each satellite. However, in the case of a spoofing attack, all the counterfeit signals from the same broadcaster come from the same direction, and thus the AoA values are the same for all DD measurements. Sum of Squares (SoS) detector and Dispersion of Double Difference (D3) detector are known as simple but effective methods for spoofing detection. However, these detectors are not robust in some special regions where cycle slips are large and vary rapidly. This paper proposes a new method for detecting GNSS spoofing by using carrier phase double difference measurements to generate a feature vector as a descriptor for each satellite in a set of observed satellites. The feature vectors are formed by statistical parameters of a series of consecutive double difference values computed for each reference satellite. Our proposed feature descriptor has been evaluated on a dataset containing both real and fake signals using an SVM classifier. The study shows that the proposed feature descriptor is highly effective in detecting spoofing satellite signals.

Learning Discriminative Noise Guidance for Image Forgery Detection and Localization

This study introduces a new method for detecting and localizing image forgery by focusing on manipulation traces within the noise domain. We posit that nearly invisible noise in RGB images carries tampering traces, useful for distinguishing and locating forgeries. However, the advancement of tampering technology complicates the direct application of noise for forgery detection, as the noise inconsistency between forged and authentic regions is not fully exploited. To tackle this, we develop a two-step discriminative noise-guided approach to explicitly enhance the representation and use of noise inconsistencies, thereby fully exploiting noise information to improve the accuracy and robustness of forgery detection. Specifically, we first enhance the noise discriminability of forged regions compared to authentic ones using a de-noising network and a statistics-based constraint. Then, we merge a model-driven guided filtering mechanism with a data-driven attention mechanism to create a learnable and differentiable noise-guided filter. This sophisticated filter allows us to maintain the edges of forged regions learned from the noise. Comprehensive experiments on multiple datasets demonstrate that our method can reliably detect and localize forgeries, surpassing existing state-of-the-art methods.

Language Testing of Pre-service Foreign language Teachers in their Professional Development

Introduction. Changes in educational goals in Ukraine, predicated by its accession to the pan-European space of higher education, have necessitated a review not only of teaching methods but also of teaching and learning materials These materials should aim to improve the language proficiency of pre-service students while equipping them with the tools necessary to address potential challenges in the classroom. Purpose. The article aims to scrutinize the role of language testing in the professional development of pre-service foreign language teachers. Methods. Reviewing studies on Methods for foreign language teaching and learning, Psychology, Pedagogy, Linguistics; analysis of curricula, syllabi, international tests, textbooks, study guides, dictionaries, including authentic ones; scientifically rigorous observation of the educational process; summarizing the positive work experience of university teachers. Results. Recognizing the lack of teaching materials, a group of university teachers from Ukrainian Institute of Arts and Sciences developed and published a manual “English Tests For University Students”. Subsequent experience with the book revealed the students’ positive attitudes towards language testing for assessing their foreign language communicative competence, citing its objectivity and validity. University teachers also highlighted the practicality, convenience, and cost-effectiveness of this form of assessment. Conclusion. Overall, despite obvious advantages of and progress in language testing, its wide-scale implementation requires university teachers to possess adequate knowledge and experience in this area, as well as awareness and readiness for its regular use in the classroom. Moreover, the development of tests remains challenging due to its time-consuming nature and the need for relevant theoretical knowledge, practical skills, and experience. This has resulted in a shortage of tests for pre-service foreign language teachers. Further research in this domain seems perspective for our future work.

Cyber Attack Against E-Albania and Its Social, Economic and Strategic Effects

Purpose: During last years, even because of pandemic situation caused by covid-19 virus, in Albania most of governmental public services for citizens, businesses and other customers were offered in an electronic way by creating a national database (e-Albania), offering more than 2200 services. As this electronic system was newly implemented, time after time it was attacked from hackers in different sectors of services, causing the interruption of service for hours, downloading all the confidential information and publishing them. After several partial attacks, in July 2022 came the general attack of the whole system, which black out the system and services for several days. Cyber actors - identifying as “HomeLand Justice” - launched a destructive cyber-attack against e-Albania which rendered websites and services unavailable. An investigation indicates cyber actors acquired initial access to the victim’s network approximately 14 months before launching the destructive cyber-attack, which included a ransomware-style file encryptor and disk wiping malware. The actors maintained continuous network access for approximately a year, periodically accessing and exfiltrating e-mail content. From late July to mid-August 2022, social media accounts associated with HomeLand Justice demonstrated a repeated pattern of advertising Albanian Government information for release, posting a poll asking respondents to select the government information to be released by HomeLand Justice, and then releasing that information - either in a .zip file or a video of a screen recording with the documents shown. This cyber-attack creates social problems, economical loss and influenced negatively in the reputation of e-Albania and damage as well strategically the country and development of this sector in the future. Methodology: We have monitored the system and the attack, and we continue to do this. We analyze and synthesis the data collected, to come to conclusions and recommendations needed for the future. All the data which we have used are open for public, and mostly are primary data. The research method combines both quantitative and qualitative methods, but it is closer with qualitative method, as far as there in not enough data for using e pure quantitative analysis. We have used mostly the descriptive method. Results/Findings: Improving essentially the cyber infrastructure to avoid in the future such attacks with high social, economic and strategical cost. Conclusions: In the institution there was not a team for Cyber Security Monitoring the system, so called SOC (Security Operation Center), who controls in the real time all the logins. It was missing as well so called “Identifying Behavior”. There was not e separation of active directory, in physic machines and virtual machines, they were altogether. As the administrator had Full Right Privilege, the hacker doesn’t need to create a Privilege Escalation Vertical, so he easily took all the right of Admin. Originality and Practical Implications: The paper is original; it has not been previously published and it is not under consideration by any other publisher. The originality of the method stands in the fact that it is the first case in the world in information age, that a country (a whole electronic system, e-Albania), face a such complex, well organized and hard cyber-attack, which collapse the system for several days. All the data are authentic ones.

Discourse Structure Analysis of Making Request in Japanese Conversation

Making requests (irai) is a genre of spoken interaction that is taught from the basic level of learning Japanese as a foreign language. A request is one of the speech acts that may raise face-threatening potentials. Understanding the stages of request appropriate to Japanese culture is thus essential for Japanese learners to achieve successful conversation. Therefore, conversation pedagogy by using a discourse approach is essential. This study investigates a potential structure gap in Japanese making-requests conversations realized in actual settings and textbook conversational models. By employing genre theory and interpersonal discourse of “Negotiation” as a qualitative discourse analytic method from the Systemic Functional Linguistics (SFL) perspective, this paper describes the gaps and some factors that potentially influence the structure of Japanese making-request conversation. Data were obtained from conversational texts in the Japanese language corpus named Japanese Natural Conversation Corpus and Japanese textbooks for elementary and middle adult learners. Regarding the structure, the results show no difference between conversations in textbooks and authentic ones at the stage level, but both differ at the phase level. There is no introduction to the problem, additional explanation, and confirmation phases in textbook conversational models. In addition, the absence of the phases, the differences in pre-condition content between textbook and authentic conversations, also the length of the reasoning phase, are assumed to be influenced by relational status between participants (tenor) as well as the imposition degree of the requested object.

Foreign Language Teaching in the Conditions of Import Substitution of Information Technologies

The study is aimed at analyzing a role of domestic software products in education productivity. They can be applied in the process of teaching foreign language. The paper considers import substitution of foreign teaching application. Software products are proposed that can be used in teaching English in order to be applied instead of teaching foreign means for foreign language training. The software product approbation results in the conditions of English learning are presented. The diagnostics of software products proposed in the course of foreign language education was conducted. The stages of experiments on the basis of computer class are analyzed. In conclusion, the main prospects for the educational process improvement in the context of sanctions are defined. The authors emphasized that domestic electronic means of teaching English were more effective than authentic ones. They correspond to the level of knowledge of students of Black Sea Higher Naval School named after P.S. Nakhimov and the presentation of materials is accessible and visual, since it includes audio and video fragments of a professional orientation.

Phishing Website Detection using Machine Learning: A Comprehensive Study

Phishing attacks continue to be a serious danger to consumer privacy and internet safety. Attackers use false websites that look like authentic ones to steal sensitive data, including login credentials and financial information. In the study, different machine learning techniques for categorizing phishing websites are evaluated, including decision trees,logistic regression,Multinomial NB.This study also explores the potential integration of the generated models into online browsers and security systems, which would offer real-time defense against phishing assaults.The results of this study help to improve internet security, protect user data, and lessen the effect of phishing assaults on the digital world.

THE SPEECH RATE AND VOCABULARY PROFILE OF TED-ED VIDEOS AS EXTENSIVE LISTENING MATERIALS FOR EFL LEARNERS

Listening proficiency is essential in language acquisition, yet both its classroom time and research are overshadowed by other skills, such as speaking and reading. To tackle the issue of limited classroom time, extensive listening is one of the most convenient and effective solutions. Selecting the materials, however, needs an equally extensive effort; especially for the authentic ones. Therefore, this study aims to investigate the speech rate and vocabulary level of TED-Ed videos as one of the sources of authentic material for extensive listening to determine their suitability for EFL learners. The research used a quantitative descriptive design with purposive sampling. The data collection of Speech Rate is conducted by transcribing the sample videos and dividing the number of words by the length of the video, and the result is measured in terms of WPM (Word Per Minute). To get the vocabulary level, the video transcripts are put into the Vocabkitchen website to analyze the vocabulary CEFR level for each word. From the analysis, the average speech rate of TED-Ed videos is 147.4 WPM and it is suitable for all levels of EFL learners, including beginner level. Meanwhile, the CEFR level of the vocabulary is more suitable for intermediate EFL learners and beyond.

Oil Palm USB (Unstripped Bunch) Detector Trained on Synthetic Images Generated by PGGAN

Identifying Unstriped Bunches (USB) is a pivotal challenge in palm oil production, contributing to reduced mill efficiency. Existing manual detection methods are proven time-consuming and prone to inaccuracies. Therefore, we propose an innovative solution harnessing computer vision technology. Specifically, we leverage the Faster R-CNN (Region-based Convolution Neural Network), a robust object detection algorithm, and complement it with Progressive Growing Generative Adversarial Networks (PGGAN) for synthetic image generation. Nevertheless, a scarcity of authentic USB images may hinder the application of Faster R-CNN. Herein, PGGAN is assumed to be pivotal in generating synthetic images of Empty Fruit Bunches (EFB) and USB. Our approach pairs synthetic images with authentic ones to train the Faster R-CNN. The VGG16 feature generator serves as the architectural backbone, fostering enhanced learning. According to our experimental results, USB detectors that were trained solely with authentic images resulted in an accuracy of 77.1%, which highlights the potential of this methodology. However, employing solely synthetic images leads to a slightly reduced accuracy of 75.3%. Strikingly, the fusion of authentic and synthetic images in a balanced ratio of 1:1 fuels a remarkable accuracy surge to 87.9%, signifying a 10.1% improvement. This innovative amalgamation underscores the potential of synthetic data augmentation in refining detection systems. By amalgamating authentic and synthetic data, we unlock a novel dimension of accuracy in USB detection, which was previously unattainable. This contribution holds significant implications for the industry, ensuring further exploration into advanced data synthesis techniques and refining detection models.

RHGNN: Fake reviewer detection based on reinforced heterogeneous graph neural networks

In e-commerce, fake reviewers frequently post fake reviews to mislead consumers into making unwise shopping decisions, seriously affecting customers’ benefits. Graph neural networks (GNNs) have been widely cultivated in fake reviewer detection in recent years, but they exhibit two main limitations. Firstly, most approaches model reviewer detection with a homogeneous graph, ignoring the interdependent clues among reviewers, reviews, and products, making them ineffective against stealthy fake reviewers. Secondly, few works noticed the camouflage behavior of fake reviewers, i.e., fake reviewers disguise themselves as authentic ones by establishing links with genuine reviewers, which extremely weakens the suspiciousness of fake reviewers and undermines detection performance. This paper proposes RHGNN, a robust fake reviewers detection framework based on Reinforced Heterogeneous Graph Neural Networks. Particularly, we first model fake reviewer detection with a heterogeneous graph consisting of reviews, reviewers, and products, to capture stealthy fraud clues by investigating their heterogeneous interactive relations. Subsequently, we present a reinforced neighbor selector (RNS) to sample and aggregate top−k informative neighbors while filtering out the camouflaged relationships. Finally, we propose a self-supervised heterogeneous graph embedding method based on mutual information to learn the discriminative features of fake reviewers. Experimental results show that RHGNN demonstrates promising and robust performance, especially in detecting well-disguised fake reviewers.