• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.2
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• pp.305-314
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• 2017

Recently, user authentication through biometric traits such as fingerprint and iris raise more and more attention especially in mobile commerce and fin-tech fields. In particular, commercialized authentication methods using fingerprint recognition are widely utilized mainly because customers are more adopted and used to fingerprint recognition applications. In the meantime, the security issues caused by fingerprint falsification bring lots of attention. In this paper, we propose a new method to improve the performance of fake fingerprint detection using CNN(Convolutional Neural Network). It is common practice to increase the amount of learning data by using affine transformation or horizontal reflection to improve the detection rate in CNN characteristics that are influenced by learning data. However, in this paper we propose an effective data augmentation method based on the database difficulty level. The experimental results confirm the validity of proposed method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.89-93
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• 2014

Reviews are very important for potential consumer' making choices. They are also used by manufacturers to find problems of their products and to collect competitors' business information. But someone write fake reviews to mislead readers to make wrong choices. Therefore detecting fake reviews is an important problem for the E-commerce sites. Support Vector Machines (SVMs) are very important text classification algorithms with excellent performance. In this paper, we propose a new incremental algorithm based on weight and the extension of Karush-Kuhn-Tucker(KKT) conditions and Convex Hull for online Review Spam Detection. Finally, we analyze its performance in theory.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.213-214
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• 2023

스마트폰은 앱을 통하여 사람들에게 다양하고 유용한 기능을 제공하며, 새로운 앱들이 계속해서 개발되어 출시되고 있다. 그러나 이러한 긍정적인 측면에서 불구하고, 사람들의 편리한 사용에 대한 욕구를 이용하여, 신종 앱 사기와 같은 범죄가 발생하고 있으며, 이를 악용하여 금전적으로 피해를 주거나 개인정보를 탈취하는 범죄로가 증가되는 추세이다. 이와 같은 앱으로 인한 범죄를 대응하기 위하여, 신종 앱 사기 범죄를 분석하고 해결하는 방안이 요구되는 실정이다. 따라서 본 논문에서는 신종 앱 사기 범죄에 악용되는 가짜 앱을 탐지하고, 공식 기관에서 제공하는 정보를 기반으로 가짜 앱과 공식 앱에 대한 대량의 정보를 공유하는 프레임워크를 개발한다. 개발한 프레임워크를 통하여, 정보를 공유한 사람들에게 가짜 앱에 대한 정보를 알려주고, 공식 기관의 앱을 확인하는 안전한 모바일 환경을 제공할 것으로 사료된다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.37-39
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• 2020

인터넷 상에서의 허위정보 생산과 유통은 주로 가짜 뉴스를 통하여 이루어진다. 과거에는 신문이나 공중파 TV등 뉴스 기사의 생산과 유통이 매우 제한적이었지만 지금은 인터넷의 발달로 누구나 쉽게 뉴스를 생산하고 유통할 수 있다. 뉴스 생산의 용이성은 정보 공유의 즉각성과 수월성이라는 장점을 제공하지만 반대로 불확실한 뉴스 남발로 인한 정보의 신뢰성 하락과 선량한 피해자를 양산하는 단점 또한 존재한다. 이는 가짜 뉴스가 사회적 문제로 대두되고 있는 이유이다. 에이전트나 스파이더 등의 소프트웨어를 통해 인터넷으로 급속도로 전파되는 가짜 뉴스를 전통 방식인 소수의 전문가가 수동으로 잡아내는 것은 불가능하다. 이에 기술발달로 잡아내기 힘들어진 가짜뉴스에 대해, 역으로 발달된 기술을 활용하여 잡아내려는 시도가 늘어나고 있다. 본 논문에서는 가짜뉴스를 판별하는 다양한 기법들을 탐색하고 해결방안을 제시하고자 한다.

• Journal of KIISE
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• v.44 no.4
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• pp.339-343
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• 2017

As the number of customers who write fake reviews is increasing, online shopping sites have difficulty in providing reliable reviews. Fake reviews are called review spam, and they are written to promote or defame the product. They directly affect sales volume of the product; therefore, it is important to detect review spam. Review spam detection methods suggested in prior researches were only based on an international site even though review spam is a widespread problem in domestic shopping sites. In this paper, we have presented new review features of the domestic shopping site NAVER, and we have applied the formerly introduced method to this site for performing an evaluation.

• Journal of information and communication convergence engineering
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• v.18 no.2
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• pp.132-146
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• 2020

Biometric technologies have become widely available in many different fields. However, biometric technologies using existing physical features such as fingerprints, facial features, irises, and veins must consider forgery and alterations targeting them through fraudulent physical characteristics such as fake fingerprints. Thus, a trend toward next-generation biometric technologies using behavioral biometrics of a living person, such as bio-signals and walking characteristics, has emerged. Accordingly, in this study, we developed a bio-signal authentication algorithm using electrocardiogram (ECG) signals, which are the most uniquely identifiable form of bio-signal available. When using ECG signals with our system, the personal identification and authentication accuracy are approximately 90% during a state of rest. When using fingerprints alone, the equal error rate (EER) is 0.243%; however, when fusing the scores of both the ECG signal and fingerprints, the EER decreases to 0.113% on average. In addition, as a function of detecting a presentation attack on a mobile phone, a method for rejecting a transaction when a fake fingerprint is applied was successfully implemented.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.199-202
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• 2020

본 논문에서는 가짜뉴스 탐지 모델에 워드 임베딩 기법을 접목하여 성능을 향상시키는 방법을 제안한다. 기존의 한국어 가짜뉴스 탐지 연구는 희소 표현인 빈도-역문서 빈도(TF-IDF)를 활용한 탐지 모델들이 주를 이루었다. 하지만 이는 가짜뉴스 탐지의 관점에서 뉴스의 언어적 특성을 파악하는 데 한계가 존재하는데, 특히 문맥에서 드러나는 언어적 특성을 구조적으로 반영하지 못한다. 이에 밀집 표현 기반의 워드 임베딩 기법인 Word2vec을 활용한 텍스트 전처리를 통해 문맥 정보까지 반영한 가짜뉴스 탐지 모델을 본 연구의 제안 모델로 생성한 후 TF-IDF 기반의 가짜뉴스 탐지 모델을 비교 모델로 생성하여 두 모델 간의 비교를 통한 성능 검증을 수행하였다. 그 결과 Word2vec 기반의 제안모형이 더욱 우수하였음을 확인하였다.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.2
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• pp.71-75
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• 2017

In this paper, a smart optical fingerprint sensor technology that is robust against faked fingerprints. A new lens and prism accurately detect fingerprint ridges and valleys that are needed to express a fingerprint's intrinsic characteristics well. The proposed technology includes light path configuration and an optical fingerprint sensor that can effectively identify faked fingerprint features. Results of simulation show the smart optical fingerprint sensor classifies the characteristics of faked fingerprints made from silicone, gelatin, paper, and rubber, and show that the proposed technology has superior detection performance with faked fingerprints, compared to the existing infrared discrimination method.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.57-74
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• 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 Information Technology Applications and Management
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• v.28 no.5
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• pp.41-50
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• 2021

As COVID-19 spread widely, and rapidly, the number of misinformation is also increasing, which WHO has referred to this phenomenon as "Infodemic". The purpose of this research is to develop detection and rectification of COVID-19 misinformation based on Open-domain QA system and text similarity. 9 testing conditions were used in this model. For open-domain QA system, 6 conditions were applied using three different types of dataset types, scientific, social media, and news, both datasets, and two different methods of choosing the answer, choosing the top answer generated from the QA system and voting from the top three answers generated from QA system. The other 3 conditions were the Closed-Domain QA system with different dataset types. The best results from the testing model were 76% using all datasets with voting from the top 3 answers outperforming by 16% from the closed-domain model.