• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.267-283
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• 2023

The COVID-19 pandemic, which began in December 2019 and continues to this day, has left the public needing information to help them cope with the pandemic. However, COVID-19-related fake news on social media seriously threatens the public's health. In particular, if fake news related to COVID-19 is massively spread with similar content, the time required for verification to determine whether it is genuine or fake will be prolonged, posing a severe threat to our society. In response, academics have been actively researching intelligent models that can quickly detect COVID-19-related fake news. Still, the data used in most of the existing studies are in English, and studies on Korean fake news detection are scarce. In this study, we collect data on COVID-19-related fake news written in Korean that is spread on social media and propose an intelligent fake news detection model using it. The proposed model utilizes the frequency information of parts of speech, one of the linguistic characteristics, to improve the prediction performance of the fake news detection model based on Doc2Vec, a document embedding technique mainly used in prior studies. The empirical analysis shows that the proposed model can more accurately identify Korean COVID-19-related fake news by increasing the recall and F1 score compared to the comparison model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.51-73
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• 2023

Social media play a significant role in communicating information across the globe, connecting with loved ones, getting the news, communicating ideas, etc. However, a group of people uses social media to spread fake information, which has a bad impact on society. Therefore, minimizing fake news and its detection are the two primary challenges that need to be addressed. This paper presents a multi-modal deep learning technique to address the above challenges. The proposed modal can use and process visual and textual features. Therefore, it has the ability to detect fake information from visual and textual data. We used EfficientNetB0 and a sentence transformer, respectively, for detecting counterfeit images and for textural learning. Feature embedding is performed at individual channels, whilst fusion is done at the last classification layer. The late fusion is applied intentionally to mitigate the noisy data that are generated by multi-modalities. Extensive experiments are conducted, and performance is evaluated against state-of-the-art methods. Three real-world benchmark datasets, such as MediaEval (Twitter), Weibo, and Fakeddit, are used for experimentation. Result reveals that the proposed modal outperformed the state-of-the-art methods and achieved an accuracy of 86.48%, 82.50%, and 88.80%, respectively, for MediaEval (Twitter), Weibo, and Fakeddit datasets.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1447-1456
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• 2019

As artificial intelligence technology is actively used in image processing, it is possible to generate high-quality fake images based on deep learning. Fake images generated using GAN(Generative Adversarial Network), one of unsupervised learning algorithms, have reached levels that are hard to discriminate from the naked eye. Detecting these fake images is required as they can be abused for crimes such as illegal content production, identity fraud and defamation. In this paper, we develop a deep-learning model based on CNN(Convolutional Neural Network) for the detection of StyleGAN fake images. StyleGAN is one of GAN algorithms and has an excellent performance in generating face images. We experiment with 48 number of experimental scenarios developed by combining parameters of the proposed model. We train and test each scenario with 300,000 number of real and fake face images in order to present a model parameter that improves performance in the detection of fake faces.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.337-351
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• 2022

With the full spread and rapid development of social media, the trend of decentralization of social media information propagation is becoming clearer day by day, and the segmentation of time by audiences using social media information is clearly progressing. Therefore, this study aims to study the influence relationship between social media attitudes toward fake news, social capital, risk perception, and discriminant intentions based on existing studies. Accordingly, the research model presented related research questions and organized a questionnaire to collect a total of 500 valid surveys. The SPSS 26.0 program and the AMOS 24.0 program were used to analyze the data. The research results are as follows. First, the more positive the user's attitude towards the fake news identification intention of social media, the more they want to use various methods or tools to identify the authenticity of online information. Second, the more positive the user's attitude towards social media fake news, the more aware of the potential threats social media fake news poses to their own physical, psychological, financial and so on. At the same time, by raising one's own awareness of the dangers, counterintelligence intentions against fake news on social media will also increase. Third, the richer the social capital the user has, the stronger the information literacy, and therefore the stronger the identification intention of social media fake news. Fourth, the higher the value of social capital Chinese users have, the greater the damage they have suffered from fake news, and the higher the risk awareness of fake news to protect their interests. Fifth, it means that Chinese users recognized information suspected of social media and took corresponding measures.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.351-354
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• 2005

In this paper, we propose a new fake-iris detection method. In order to detect the fake-iris, we measure the change of the reflectance ratio between iris and sclera. In a live iris, the amount of change is relatively large compared to that in a fake-iris. From our experimental results, it is clear that our fake-iris detection method achieves high performance when distinguishing between a live-iris and a fake-iris.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.

• International journal of advanced smart convergence
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• v.10 no.3
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• pp.122-130
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• 2021

This study is about an Artificial Intelligence-based fake news identification system and its methods to determine the authenticity of content distributed over the Internet. Among the news we encounter is news that an individual or organization intentionally writes something that is not true to achieve a particular purpose, so-called fake news. In this study, we intend to design a system that uses Artificial Intelligence techniques to identify fake content that exists within the news. The proposed identification model will propose a method of extracting multiple unit factors from the target content. Through this, attempts will be made to classify unit factors into different types. In addition, the design of the preprocessing process will be carried out to parse only the necessary information by analyzing the unit factor. Based on these results, we will design the part where the unit fact is analyzed using the deep learning prediction model as a predetermined unit. The model will also include a design for a database that determines the degree of fake news in the target content and stores the information in the identified unit factor through the analyzed unit factor.

• Journal of Service Research and Studies
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• v.12 no.3
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• pp.144-157
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• 2022

This study began by pointing out the problem of domestic media reporting on 'fake news' regulations that frequently appear through the French 'Les proposals de loi control de l'information'case, while still approaching with different standards and perspectives on where to see fake news. In the age of 'social network services', the answer to what the media is, what the news is, and who the reporter is increasingly difficult. While reviewing the long history and background of the spread of fake news examined in this study, it was confirmed that could not determine the concept and scope of fake news, punished, regulated, controlled, or judged simply by one standard. From the perspective of 'freedom of expression' set by the law, we have the authority to express our opinions freely. In addition, 'online' space is a place where fake news is generated and spread, but at the same time, there is plenty of room to act as an antidote. In the end, the only alternative to the damage of long-term fake news will be to create a media environment that allows more high-quality "real news" to pour out, allowing us to develop our ability to judge reliable information through balanced competition among various news in the free market of ideas.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.1
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• pp.155-163
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• 2014

To enhance network efficiency, content-centric networking (CCN) proposes that intermediated network nodes on a content-delivery path temporally cache transmitted contents. Then if an intermediated node receives a content request message (Interest) for previously cached content, the node directly transmits the cached content as a response message (Data) to requestors and finishes the transmission of the received Interest. Since Interest is performed by intermediated network nodes, it is possible to efficiently transmit contents and to effectively solve a network congestion problem caused around contents sources. For that, CCN utilizes both content store to temporarily cache content and pending Interest table (PIT) to record Interest incoming Face. However, it has mentioned the possibility of denial service attack using both the limitation of PIT resource and fake Interests. In this paper, we briefly describe the presented PIT flooding attack utilizing fake Interest. Then we introduce new attack possibility using fake Data and propose a countermeasure for the proposed attack. Also we evaluate the performance of our proposal.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.1
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• pp.85-106
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• 2021

The purpose of this study is to examine the concept and definition of fake news focusing on misinformation/false information and is to examine the ways in which our society can respond to the distortion of social reality and damage to democracy caused by information distortion such as fake news. To do this, the concept of fake news was examined based on the level of facticity and intention to device, and our social environment in which fake news was created and spread was examined from the perspective of datafication. In this environment, the library community, which plays a pivotal role in human access to and use of information, argued that it should strive to establish and provide universal literacy education in order to realize the Sustainable Development Goals of the UN 2030 agenda. The core of universal literacy education is to understand the society by investigating and analyzing data communication types according to the degree of datafication and the political, economic, social, and cultural background of society. For this reason, it was concluded that universal literacy should be implemented flexibly according to the degree of datafiation and users of each society.