• Journal of Intelligence and Information Systems
• /
• v.28 no.2
• /
• pp.57-74
• /
• 2022

Recently, artificial intelligence parking control systems have increased the recognition rate of vehicle license plates using deep learning, but there is a problem that they cannot determine vehicles with fake license plates. Despite these security problems, several institutions have been using the existing system so far. For example, in an experiment using a counterfeit license plate, there are cases of successful entry into major government agencies. This paper proposes an improved system over the existing artificial intelligence parking control system to prevent vehicles with such fake license plates from entering. The proposed method is to use the degree of matching of the front feature points of the vehicle as a passing criterion using the ORB algorithm that extracts information on feature points characterized by an image, just as the existing system uses the matching of vehicle license plates as a passing criterion. In addition, a procedure for checking whether a vehicle exists inside was included in the proposed system to prevent the entry of the same type of vehicle with a fake license plate. As a result of the experiment, it showed the improved performance in identifying vehicles with fake license plates compared to the existing system. These results confirmed that the methods proposed in this paper could be applied to the existing parking control system while taking the flow of the original artificial intelligence parking control system to prevent vehicles with fake license plates from entering.

• Journal of the Korean Data and Information Science Society
• /
• v.20 no.5
• /
• pp.933-939
• /
• 2009

The bivariate data in clinical research fields often has two types of failure times, which are mark variable for the first failure time and the final failure time. This paper showed how to generate bootstrap data to get Bayesian estimation for the joint distribution of bivariate survival times. The observed data was generated by Frank's family and the fake date is simulated with the Gamma prior of survival time. The bootstrap data was obtained by combining the mimic data with the observed data and the simulated fake data from the observed data.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2022.01a
• /
• pp.411-412
• /
• 2022

본 논문에서는 코로나19 뉴스와 코로나19 가짜뉴스의 데이터셋을 활용하여 입력 받은 뉴스가 가짜뉴스일 확률을 예측한다. 가짜 뉴스 본문에는 코로나19, 대통령, 정부, 가짜, 언론 등의 키워드의 빈도가 높았다. 위의 키워드를 토대로 나이브 베이즈 모델링을 하여 이를 적용해 가짜 뉴스를 가려내는 웹페이지를 개발하였다.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.27 no.5
• /
• pp.1069-1076
• /
• 2017

Recently $Pok\acute{e}mon$ GO implements an online game with location-based real time augmented reality on mobile. The correct play of this game should be based on collecting the $Pok\acute{e}mon$ that appears as the user moves around by foot, but as the popularity increases, it appears an abuse to play easily. Many people have used an application that provides a mock location service such as Fake GPS, and these applications can be judged to be cheating in online games because they can play games in the house without moving. Detection of such cheating from a client point of view (mobile device) can consume a large amount of resources, which can reduce the speed of the game. It is difficult for developers to apply detection methods that negatively affect game usage and user's satisfaction. Therefore, in this paper, we propose a method to detect users abusing mock location service in online game by route analysis using GPS location record from the server point of view.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.9
• /
• pp.3231-3249
• /
• 2014

Deniable encryption, introduced in 1997 by Canetti, Dwork, Naor, and Ostrovsky, guarantees that the sender or the receiver of a secret message is able to "fake" the message encrypted in a specific ciphertext in the presence of a coercing adversary, without the adversary detecting that he was not given the real message. Sender - side deniable encryption scheme is considered to be one of the classification of deniable encryption technique which defined as resilient against coercing the sender. M. H. Ibrahim presented a sender - side deniable encryption scheme which based on public key and uncertainty of Jacobi Symbol [6]. This scheme has several problems; (1) it can't be able to derive the fake message $M_f$ that belongs to a valid message set, (2) it is not secure against Quadratic Residue Problem (QRP), and (3) the decryption process is very slow because it is based dramatically on square root computation until reach the message as a Quadratic Non Residue (QNR). The first problem is solved by J. Howlader and S. Basu's scheme [7]; they presented a sender side encryption scheme that allows the sender to present a fake message $M_f$ from a valid message set, but it still suffers from the last two mentioned problems. In this paper we present a new sender-side deniable public-key encryption scheme with fast decryption by which the sender is able to lie about the encrypted message to a coercer and hence escape coercion. While the receiver is able to decrypt for the true message, the sender has the ability to open a fake message of his choice to the coercer which, when verified, gives the same ciphertext as the true message. Compared with both Ibrahim's scheme and J. Howlader and S. Basu's scheme, our scheme enjoys nice two features which solved the mentioned problems: (1) It is semantically secure against Quadratic Residue Problem; (2) It is as fast, in the decryption process, as other schemes. Finally, applying the proposed deniable encryption, we originally give a coercion resistant internet voting model without physical assumptions.

• Journal of Contemporary Eastern Asia
• /
• v.20 no.2
• /
• pp.40-62
• /
• 2021

Are intentions important in deciding the outcome of an action such as sharing misinformation among social media users during the pandemic? What is their role and how far they are important for the very act of fake sharing news? The social media users' actions on the social platform are determined by what they plan to do themselves; however, their motivation has an immense role to play in the dissemination of fake news on social media. The study proposes a conceptual model for understanding how select factors affect fake news sharing motivation and intentions of social media users. The study scrutinizes the relationship between content and context, fear of missing out (FoMO), news verification and news sharing gratification on the motivation and intention of social media users of networked Asian society. Empirical Data were drawn from social media users (N = 243) from India, using an online questionnaire based on prior studies and structural equation modeling (SEM) approach was used to analyze the data collected. Results indicate that news content, news verification, and news sharing gratification have a direct and positive relationship with sharing motivation. On the other hand, news context and content, FoMO and news sharing gratification have a positive significant relationship with sharing intention. Likewise, it was discovered that news verification will decrease sharing intention of the social media users. However, news context, that is the pandemic in the case of the present study and FoMO were not identified as determinant variables for sharing motivation among social media users. The research limitations and further scope were discussed.

• Smart Media Journal
• /
• v.11 no.1
• /
• pp.58-66
• /
• 2022

As Internet technology develops, SNS users are increasing. As SNS becomes popular, SNS-type crimes using the influence and anonymity of social networks are increasing day by day. In this paper, we propose a fake account classification method that applies machine learning and deep learning to statistical and image data for fake accounts classification. SNS account data used for training was collected by itself, and the collected data is based on statistical data and image data. In the case of statistical data, machine learning and multi-layer perceptron were employed to train. Furthermore in the case of image data, a convolutional neural network (CNN) was utilized. Accordingly, it was confirmed that the overall performance of account classification was significantly meaningful.

• The Research Journal of the Costume Culture
• /
• v.6 no.2
• /
• pp.163-180
• /
• 1998

The purpose of this study is to investigate the main characteristics of the environment-friendly design expressed in Koran fashion. Environment-friendly design can be categorized into choice of material, extension of products life cycle, and recycling design. In this study, Korean fashion designers can be found having the conciousness of environment for fashion design since 1990. First, in choice of material, the designers used Natural fibers which are cotton, linen, wool, and etc, and used natural dyes. Some of the designers have moved from using real fur to using fake fur for animal welfare. But fake furs produced from synthetic or regenerated fibers have the environmental problems during textile production processes. Some of the designers used fake leather made from the skins of an edible fish which are otherwise going to waste. Secondly, Design for extension of products life cycle can economize the resources and energy. Design for extension of products life cycle are classified into reversible clothing, many function clothing, modular style, patina clothing, simple style, and layered look. Finally, recycling design are classified into recycling of daily necessaries and expression techniques of recycling design which are designer's works used patchwork, mash techniques, and handmade of knits or buttonhole stitch.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.11
• /
• pp.5354-5369
• /
• 2019

Sensor networks are deployed in unheeded environment to monitor the situation. In view of the unheeded environment and by the nature of their communication channel sensor nodes are vulnerable to various attacks most commonly malicious packet dropping attacks namely blackhole, grayhole attack and sinkhole attack. In each of these attacks, the attackers capture the sensor nodes to inject fake details, to deceive other sensor nodes and to interrupt the network traffic by packet dropping. In all such attacks, the compromised node advertises itself with fake routing facts to draw its neighbor traffic and to plunge the data packets. False routing advertisement play vital role in deceiving genuine node in network. In this paper, behavior based routing misbehavior detection (BRMD) is designed in wireless sensor networks to detect false advertiser node in the network. Herein the sensor nodes are monitored by its neighbor. The node which attracts more neighbor traffic by fake routing advertisement and involves the malicious activities such as packet dropping, selective packet dropping and tampering data are detected by its various behaviors and isolated from the network. To estimate the effectiveness of the proposed technique, Network Simulator 2.34 is used. In addition packet delivery ratio, throughput and end-to-end delay of BRMD are compared with other existing routing protocols and as a consequence it is shown that BRMD performs better. The outcome also demonstrates that BRMD yields lesser false positive (less than 6%) and false negative (less than 4%) encountered in various attack detection.

• International Journal of Internet, Broadcasting and Communication
• /
• v.11 no.2
• /
• pp.59-66
• /
• 2019

The emergence of new IT technologies and convergence industries, such as artificial intelligence, bigdata and the Internet of Things, is another chance for South Korea, which has established itself as one of the world's top IT powerhouses. On the other hand, however, privacy concerns that may arise in the process of using such technologies raise the task of harmonizing the development of new industries and the protection of personal information at the same time. In response, the government clearly presented the criteria for deidentifiable measures of personal information and the scope of use of deidentifiable information needed to ensure that bigdata can be safely utilized within the framework of the current Personal Information Protection Act. It strives to promote corporate investment and industrial development by removing them and to ensure that the protection of the people's personal information and human rights is not neglected. This study discusses the strategy of deidentifying personal information protection based on the analysis of fake news. Using the strategies derived from this study, it is assumed that deidentification information that is appropriate for deidentification measures is not personal information and can therefore be used for analysis of big data. By doing so, deidentification information can be safely utilized and managed through administrative and technical safeguards to prevent re-identification, considering the possibility of re-identification due to technology development and data growth.