• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5120-5142
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• 2018

Social media enables customers to share their views, opinions and experiences as product reviews. These product reviews facilitate customers in buying quality products. Due to the significance of online reviews, fake reviews, commonly known as spam reviews are generated to mislead the potential customers in decision-making. To cater this issue, review spam detection has become an active research area. Existing studies carried out for review spam detection have exploited feature engineering approach; however limited number of features are considered. This paper proposes a Feature-Centric Model for Review Spam Detection (FMRSD) to detect spam reviews. The proposed model examines a wide range of feature sets including ratings, sentiments, content, and users. The experimentation reveals that the proposed technique outperforms the baseline and provides better results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.892-911
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• 2019

Biometric recognition systems have been widely used for information security. Among the most popular biometric traits, there are fingerprint and face due to their high recognition accuracies. However, the security system that uses face recognition as the login method are vulnerable to face-spoofing attacks, from using printed photo or video of the valid user. In this study, we propose a fast and robust method to detect face-spoofing attacks based on the analysis of spatial frequency differences between the real and fake videos. We found that the effect of a spoofing attack stands out more prominently in certain regions of the 2D Fourier spectra and, therefore, it is adequate to use the information about those regions to classify the input video or image as real or fake. We adopt a divide-conquer-aggregate approach, where we first divide the frequency domain image into local blocks, classify each local block independently, and then aggregate all the classification results by the weighted-sum approach. The effectiveness of the methodology is demonstrated using two different publicly available databases, namely: 1) Replay Attack Database and 2) CASIA-Face Anti-Spoofing Database. Experimental results show that the proposed method provides state-of-the-art performance by processing fewer frames of each video.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.241-245
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• 2006

Recent studies show that fingerprint recognition technology is confronted with spoofing of artificial fingers. In order to overcome this problem, the fingerprint recognition system needs to distinguish a fake finger from a live finger. This paper examines existing software-based approaches for fingerprint liveness detection through experiments. Implemented and tested in this paper are the approaches based on deformation, wavelet, and perspiration. These approaches will be analyzed and compared based on experimental results.

• Electronics and Telecommunications Trends
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• v.35 no.7
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• pp.67-76
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• 2020

Because of the development and spread of artificial intelligence (AI) technology, new security threats and adverse AI functions have emerged as a real problem in the process of diversifying areas of use and introducing AI-based products and services to users. In response, it is necessary to develop new AI-based technologies in the field of information protection and security. This paper reviews topics such as domestic and international trends on false information detection technology, cyber security technology, and trust distribution platform technology, and it establishes the direction of the promotion of technology development. In addition, the development of international trends in ethical AI guidelines to ensure the human-centered ethical validity of AI development processes and final systems in parallel with technology development are analyzed and discussed. ETRI has developed AI policing technology, information protection, and security technologies as well as derived tasks and implementation strategies to prepare ethical AI development guidelines to ensure the reliability of AI based on its capabilities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1825-1844
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• 2019

Political campaigns circulate manipulative opinions in online communities to implant false beliefs and eventually win elections. Not only is this type of manipulation unfair, it also has long-lasting negative impacts on people's lives. Existing tools detect political manipulation based on a supervised classifier, which is accurate when trained with large labeled data. However, preparing this data becomes an excessive burden and must be repeated often to reflect changing manipulation tactics. We propose a practical detection system that requires moderate groundwork to achieve a sufficient level of accuracy. The proposed system groups opinions with similar properties into clusters, and then labels a few opinions from each cluster to build a classifier. It also models each opinion with features deduced from raw data with no additional processing. To validate the system, we collected over a million opinions during three nation-wide campaigns in South Korea. The system reduced groundwork from 200K to nearly 200 labeling tasks, and correctly identified over 90% of manipulative opinions. The system also effectively identified transitions in manipulative tactics over time. We suggest that online communities perform periodic audits using the proposed system to highlight manipulative opinions and emerging tactics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1671-1686
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• 2022

Face manipulation tools represented by Deepfake have threatened the security of people's biological identity information. Particularly, manipulation tools with deep learning technology have brought great challenges to Deepfake detection. There are many solutions for Deepfake detection based on traditional machine learning and advanced deep learning. However, those solutions of detectors almost have problems of poor performance when evaluated on different quality datasets. In this paper, for the sake of making high-quality Deepfake datasets, we provide a preprocessing method based on the image pixel matrix feature to eliminate similar images and the residual channel attention network (RCAN) to resize the scale of images. Significantly, we also describe a Deepfake detector named Cascaded-Hop which is based on the PixelHop++ system and the successive subspace learning (SSL) model. By feeding the preprocessed datasets, Cascaded-Hop achieves a good classification result on different manipulation types and multiple quality datasets. According to the experiment on FaceForensics++ and Celeb-DF, the AUC (area under curve) results of our proposed methods are comparable to the state-of-the-art models.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.540-542
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• 2023

Due to the serious issues posed by facial manipulation technologies, many researchers are becoming increasingly interested in the identification of face forgeries. The majority of existing face forgery detection methods leverage powerful data adaptation ability of neural network to derive distinguishing traits. These deep learning-based detection methods frequently treat the detection of fake faces as a binary classification problem and employ softmax loss to track CNN network training. However, acquired traits observed by softmax loss are insufficient for discriminating. To get over these limitations, in this study, we introduce a novel discriminative feature learning based on Vision Transformer architecture. Additionally, a separation-center loss is created to simply compress intra-class variation of original faces while enhancing inter-class differences in the embedding space.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.649-651
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• 2021

In recent years, O2O services for real estate sales are widely distributed in web platforms and apps. This allows sellers, buyers, and real estate brokers to quickly and conveniently conduct real estate sales and charter contracts. However, in the O2O-based real estate sales information system, it wastes time and money for real estate buyers due to the posting of fake information, partial correction of the sales information, and intentional non-posting of the sales information. Therefore, we propose a method of detecting the false or not of real estate property information that can occur on the web platform, and design and implement a proposal system for this. To this end, we propose a method of detecting personal identity and property information based on DID, a distributed identity authentication protocol. The false real estate sales information detection system proposed by us can determine the existence of real estate sales information, partially correct the false sales information, or prove whether or not intentionally unpublished in three steps.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.165-166
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• 2007

특수 분장을 이용하여 매우 정교하게 제작된 가면을 쓴 얼굴 위변조의 경우 일반적인 밝기 영상으로는 검출이 용이하지 않다. 최근의 획기적인 특수 분장 기술 발전을 고려할 때 성공적인 얼굴 인식시스템 개발을 위해 가면을 쓴 얼굴 위변조 검출 연구는 매우 중요하다. 본 연구에서는 물질의 재질에 따른 반사율의 차이를 기반으로 가면을 착용하는 얼굴 위변조를 검출하는 방법을 제안한다. 우선 알비도(albedo)에 착안하여 여러 파장대의 조명에 대해 실험하였다. 실제 얼굴 인식 시스템의 적용 환경을 고려할 때 알비도를 단순히 빛의 반사량으로 간략화 할 수 있음을 보였고, 실험결과 850nm 적외선 조명이 적합하다는 결론을 얻었다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.5
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• pp.781-793
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• 2014

A Public Key Infrastructure (PKI) is security standards to manage and use public key cryptosystem. A PKI is used to provide digital signature, authentication, public key encryption functionality on insecure channel, such as E-banking and E-commerce on Internet. A soft-token private key in PKI is leaked easily because it is stored in a file at standardized location. Also it is vulnerable to a brute-force password attack as is protected by password-based encryption. In this paper, we proposed a new method that detects private key compromise and is probabilistically secure against a brute-force password attack though soft-token private key is leaked. The main idea of the proposed method is to use a genuine signature key pair and (n-1) fake signature key pairs to make an attacker difficult to generate a valid signature with probability 1/n even if the attacker found the correct password. The proposed method provides detection and notification functionality when an attacker make an attempt at authentication, and enhances the security of soft-token private key without the additional cost of construction of infrastructure thereby extending the function of the existing PKI and SSL/TLS.