• Journal of Digital Convergence
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• v.17 no.5
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• pp.137-143
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• 2019

Fake news is a form of misinformation that has the advantage of rapid spreading of information on media platforms that users interact with, such as social media. There has been a lot of social problems due to the recent increase in fake news. In this paper, we propose a method to detect such false news. Previous research on fake news detection mainly focused on text analysis. This research focuses on a network where social media news spreads, generates qualities with DeepWalk, a network embedding method, and classifies fake news using logistic regression analysis. We conducted an experiment on fake news detection using 211 news on the Internet and 1.2 million news diffusion network data. The results show that the accuracy of false network detection using network embedding is 10.6% higher than that of text analysis. In addition, fake news detection, which combines text analysis and network embedding, does not show an increase in accuracy over network embedding. The results of this study can be effectively applied to the detection of fake news that organizations spread online.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.201-220
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• 2019

Fake news has emerged as a significant issue over the last few years, igniting discussions and research on how to solve this problem. In particular, studies on automated fact-checking and fake news detection using artificial intelligence and text analysis techniques have drawn attention. Fake news detection research entails a form of document classification; thus, document classification techniques have been widely used in this type of research. However, document summarization techniques have been inconspicuous in this field. At the same time, automatic news summarization services have become popular, and a recent study found that the use of news summarized through abstractive summarization has strengthened the predictive performance of fake news detection models. Therefore, the need to study the integration of document summarization technology in the domestic news data environment has become evident. In order to examine the effect of extractive summarization on the fake news detection model, we first summarized news articles through extractive summarization. Second, we created a summarized news-based detection model. Finally, we compared our model with the full-text-based detection model. The study found that BPN(Back Propagation Neural Network) and SVM(Support Vector Machine) did not exhibit a large difference in performance; however, for DT(Decision Tree), the full-text-based model demonstrated a somewhat better performance. In the case of LR(Logistic Regression), our model exhibited the superior performance. Nonetheless, the results did not show a statistically significant difference between our model and the full-text-based model. Therefore, when the summary is applied, at least the core information of the fake news is preserved, and the LR-based model can confirm the possibility of performance improvement. This study features an experimental application of extractive summarization in fake news detection research by employing various machine-learning algorithms. The study's limitations are, essentially, the relatively small amount of data and the lack of comparison between various summarization technologies. Therefore, an in-depth analysis that applies various analytical techniques to a larger data volume would be helpful in the future.

• Information Systems Review
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• v.23 no.4
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• pp.127-146
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• 2021

Fake news isexpanded and reproduced rapidly regardless of their authenticity by the characteristics of modern society, called the information age. Assuming that 1% of all news are fake news, the amount of economic costs is reported to about 30 trillion Korean won. This shows that the fake news isvery important social and economic issue. Therefore, this study aims to develop an automated detection model to quickly and accurately verify the authenticity of the news. To this end, this study crawled the news data whose authenticity is verified, and developed fake news prediction models using word embedding (Word2Vec, Fasttext) and deep learning algorithms (LSTM, BiLSTM). Experimental results show that the prediction model using BiLSTM with Word2Vec achieved the best accuracy of 84%.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10e
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• pp.331-333
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• 2002

신뢰성 높은 이동 에이전트 시스템을 구성하는데 있어서 지속적인 이동 에이전트 연산을 보장하는 결함 포용기법은 중요한 고려사항이다. 이를 위해 많은 연구들이 단계군 구성에 기반한 이동 에이전트 수행에 대한 결함 포용 기법들을 제안하고 있다. 그러나 제안된 기법들은 단계군을 구성함으로써 에이전트 연산 실행에 대한 봉쇄 가능성을 감소시켰으나, 에이전트를 이주시키는 통신비용과 단계군 작업들에 대한 부하를 증가시켰다. 본 논문에서는 단계군내에 지역(region)적으로 다르게 분포한 실행장소(place)에 대해서 가짜 참여자(quasi-participant)를 두어 지역적으로 같은 곳에 모이게 하는 새로운 단계군 구성기법을 제안한다. 또한, 가짜 참여자와 실제 실행장소를 하나의 단계군으로 구성하기 위해 단계군내에 하위단계군(substage)을 두어 단계군을 구성하는 기법을 제안한다. 하위단계군은 가짜 참여자와 실제 실행장소의 작업을 분리하여 가짜 참여자로 인해 발생할 수 있는 추가 통신비용을 감소시킨다. 본 논문에서 제안하는 가짜 참여자와 하위단계군을 사용한 단계군 구성기법은 고장 자유(failure-free) 상태에서 단계군 작업들에 대한 수행시간을 단축시켜 단계군을 이용하여 결함을 포용하는 이동 에이전트의 전체 수행시간을 단축시킨다.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.5
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• pp.9-17
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• 2018

Recently, the increasing interest of biometric systems has led to the creation of many researches of biometrics forgery. In order to solve this forgery problem, this paper proposes a method of determining whether a synthesized face made of artificaial intelligence is real face or fake face. The proposed algorithm consists of two steps. Firstly, we create the fake face images using various GAN (Generative Adversarial Networks) algorithms. After that, deep learning algorithm can classify the real face image and the generated face image. The experimental results shows that the proposed algorithm can detect the fake face image which looks like the real face. Also, we obtained the classification accuracy of 88.7%.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.569-578
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• 2021

Thanks to web development today, we can easily access online news via various media. As much as it is easy to access online news, we often face fake news pretending to be true. As fake news items have become a global problem, fact-checking services are provided domestically, too. However, these are based on expert-based manual detection, and research to provide technologies that automate the detection of fake news is being actively conducted. As for the existing research, detection is made available based on contextual characteristics of an article and the comparison of a title and the main article. However, there is a limit to such an attempt making detection difficult when manipulation precision has become high. Therefore, this study suggests using a verifying article to decide whether a news item is genuine or not to be affected by article manipulation. Also, to improve the precision of fake news detection, the study added a process to summarize a subject article and a verifying article through the summarization model. In order to verify the suggested algorithm, this study conducted verification for summarization method of documents, verification for search method of verification articles, and verification for the precision of fake news detection in the finally suggested algorithm. The algorithm suggested in this study can be helpful to identify the truth of an article before it is applied to media sources and made available online via various media sources.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.687-689
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• 2021

정맥을 이용한 생체 인식 방법은 신체의 노화에 영향을 받지 않고 높은 사용 편의성과 변조의 위험이 적어 인증 수단으로 폭넓게 활용되고 있다. 그러나 가짜 정맥 영상을 통한 스푸핑 공격 위험이 존재한다. 이러한 문제를 해결하기 위해 실제 정맥 영상과 가짜 정맥 영상을 구분하는 기술이 필요하다. 본 연구에서는 실제 정맥 데이터의 마디와 뼈의 밝기 차이를 이용해 진짜 정맥 영상과 가짜 정맥 영상을 구분하는 기술을 연구했다.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.15-21
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• 2019

Research in detecting fake information gained a lot of interest after the US presidential election in 2016. Information from unknown sources are produced in the shape of news, and its rapid spread is fueled by the interest of public drawn to stimulating and interesting issues. In addition, the wide use of mass communication platforms such as social network services makes this phenomenon worse. Poynter Institute created the International Fact Checking Network (IFCN) to provide guidelines for judging the facts of skilled professionals and releasing "Code of Ethics" for fact check agencies. However, this type of approach is costly because of the large number of experts required to test authenticity of each article. Therefore, research in automated fake news detection technology that can efficiently identify it is gaining more attention. In this paper, we investigate fake news detection systems and researches that are rapidly developing, mainly thanks to recent advances in deep learning technology. In addition, we also organize shared tasks and training corpus that are released in various forms, so that researchers can easily participate in this field, which deserves a lot of research effort.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.01a
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• pp.51-54
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• 2012

전 세계적으로 유통되고 있는 가짜의약품으로 인하여 제약사들은 판매수익과 기업브랜드 이미지에 큰 피해를 입고 있으며 가짜 의약품을 복용한 소비자들은 생명에 위협을 받고 있는 실정이다. 이러한 가짜의약품의 유통을 근절시키기 위하여 세계 각국에서는 의약품의 생산에서부터 소비자에게 판매될 때까지, 즉 의약품 유통과정을 추적하여 가시성있는 이력관리를 할 수 있도록 RFID 및 barcode 등 IT기반의 e-Pedigree(electronic pedigree)를 법제화시키고 있지만 구체적이고 실질적인 구축방법이 제시되지 못하고 있다. 따라서 본 연구는 국내 제약산업의 유통합리화와 소비자 안전성을 제고시키기 위하여 RFID 기반의e-Pedigree 적용방안을 제시하는데 그 목적이 있고 더 나아가 세계 각 국가의 e-Pedigree 구축을 위한 참조모델 개발에 일조하고자 한다.