• Journal of Ubiquitous Convergence Technology
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• v.2 no.1
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• pp.12-17
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• 2008

In this paper, the 128bit AES block cipher algorithm OCB (Offset Code Book) mode for privacy and authenticity of high speed packet data was efficiently designed in C language level and was optimized to support the required capacity of contents server using high performance DSP. It is known that OCB mode is about two times faster than CBC-MAC mode. As an experimental result, the encryption / decryption speed of the implemented block cipher was 308Mbps, 311 Mbps respectively at 1GHz clock speed, which is 50% faster than a general design with 3.5% more memory usage.

• ETRI Journal
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• v.29 no.6
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• pp.755-768
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• 2007

A very promising application of active RFID systems is the electronic seal, an electronic device to guarantee the authenticity and integrity of freight containers. To provide freight containers with a high level of tamper resistance, the security of electronic seals must be ensured. In this paper, we present the design and implementation of an electronic seal protection system. First, we propose the eSeal Protection Protocol (ePP). Next, we implement and evaluate various cryptographic primitives as building blocks for our protocol. Our experimental results show that AES-CBC-MAC achieves the best performance among various schemes for message authentication and session key derivation. Finally, we implement a new electronic seal system equipped with ePP, and evaluate its performance using a real-world platform. Our evaluation shows that ePP guarantees a sufficient performance over an ARM9-based interrogator.

• Korean Journal of Agricultural Science
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• v.46 no.1
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• pp.183-194
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• 2019

Adulteration of argan oil with some other cheaper oils with similar chemical compositions has resulted in increasing demands for authenticity assurance and quality control. Fast and simple analytical techniques are thus needed for authenticity analysis of high-priced argan oil. Raman spectroscopy is a potent technique and has been extensively used for quality control and safety determination for food products In this study, Raman spectroscopy in combination with a net analyte signal (NAS)-based methodology, i.e., hybrid linear analysis method developed by Goicoechea and Olivieri in 1999 (HLA/GO), was used to predict the different concentrations of olive oil (0 - 20%) added to argan oil. Raman spectra of 90 samples were collected in a spectral range of $400-400cm^{-1}$, and calibration and validation sets were designed to evaluate the performance of the multivariate method. The results revealed a high coefficient of determination ($R^2$) value of 0.98 and a low root-mean-square error (RMSE) value of 0.41% for the calibration set, and an $R^2$ of 0.97 and RMSE of 0.36% for the validation set. Additionally, the figures of merit such as sensitivity, selectivity, limit of detection, and limit of quantification were used for further validation. The high $R^2$ and low RMSE values validate the detection ability and accuracy of the developed method and demonstrate its potential for quantitative determination of oil adulteration.

• Management & Information Systems Review
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• v.39 no.2
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• pp.113-126
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• 2020

This study suggested implications by dividing the concept of brand origin into national image and manufacturing capacity image, verifying the direct impact of these variables on brand trust and brand loyalty, and verifying the difference between products with the effect on brand trust of brand origin, which is the result of verification. The specific implications of this paper are as follows. First, brand origin does not directly affect brand loyalty, but it can have a direct impact on the preceding variable, brand loyalty. This study may conclude that it is desirable to define the factors that affect the purchase selection indirectly through the assessment of product properties or positive effects on brand image rather than having a direct impact on product purchase or selection. Second, the difference in brand origin influence by product characteristics was very evident. Past studies were limited to a few products, so pan-product testing was not conducted, and the empirical power was judged to be limited, so this study included a variety of products and tried to detect differences between products through actual empirical research. Involvement and self-congruity have been presented with results that can be judged as important variables for brand origin to affect brand performance and variables. Looking at the role of the brand origin for each product characteristic by distinguishing between product characteristics and whether or not products related to quality, history, authenticity, etc., the product recognized as high quality and the product recognized as having high integrity showed higher effect of the brand origin, but history was a product characteristic that did not show the effect of the brand origin.

• Journal of Korea Multimedia Society
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• v.18 no.3
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• pp.323-329
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• 2015

In this paper we applied pattern recognition approach to detect audio forgery. Classification of the microphone types and models can help determining the authenticity of the recordings. Canonical correlation analysis was applied to extract feature for microphone classification. We utilized the linear dependence between two near-silence regions. To utilize the advantage of multi-feature based canonical correlation analysis, we selected three commonly used features to capture the temporal and spectral characteristics. Using three different microphones, we tested the usefulness of multi-feature based characteristics of canonical correlation analysis and compared the results with single feature based method. The performance of classification rate was carried out using the backpropagation neural network. Experimental results show the promise of canonical correlation features for microphone classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4183-4204
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• 2018

The existing en-route filtering schemes only consider some simple false data injection attacks, which results in lower safety performance. In this paper, we propose an efficient geographical information-based en-route filtering scheme (EGEFS), in which each forwarding node verifies not only the message authentication codes (MACs), but also the report identifier and the legitimacy and authenticity of locations carried in a data report. Thus, EGEFS can defend against not only the simple false data injection attacks and the replay attack, but also the collusion attack with forged locations proposed in this paper. In addition, we propose a new method for electing the center-of-stimulus (CoS) node, which can ensure that only one detecting node will be elected as the CoS node to generate one data report for an event. The simulation results show that, compared to the existing en-route filtering schemes, EGEFS has higher safety performance, because it can resist more types of false data injection attacks, and it also has higher filtering efficiency and lower energy expenditure.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.6-31
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• 2018

With the rapid development of the science and technology, it has been becoming more and more convenient to obtain abundant information via the diverse multimedia medium. However, the contents of the multimedia are easily altered with different editing software, and the authenticity and the integrity of multimedia content are under threat. Forensics technology is developed to solve this problem. We focus on reviewing the blind image forensics technologies for copy-move forgery in this survey. Copy-move forgery is one of the most common manners to manipulate images that usually obscure the objects by flat regions or append the objects within the same image. In this paper, two classical models of copy-move forgery are reviewed, and two frameworks of copy-move forgery detection (CMFD) methods are summarized. Then, massive CMFD methods are mainly divided into two types to retrospect the development process of CMFD technologies, including block-based and keypoint-based. Besides, the performance evaluation criterions and the datasets created for evaluating the performance of CMFD methods are also collected in this review. At last, future research directions and conclusions are given to provide beneficial advice for researchers in this field.

• The Korea Journal of Herbology
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• v.27 no.6
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• pp.15-21
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• 2012

Objective : Lonicera japonica THUNB. a traditional herbal medicine, has been commonly used anti-inflammatory disease. It has been very complicated with respect to its sources on the market. The significant selection of medicine depends on its origin. However, it is difficult to discrimination criteria for confirming L. japonica authenticity using the senses. This study was performed to determine the discriminant analysis of L. japonica and L. confusa. Methods : The identification of L. japonica and L. confusa were performed by the classification and identification committee of the national center for standardization of herbal medicines. And we examined its differences using HPLC and genetic marker analysis. Results : The analytical pattern of High Performance Liquid Chromatography was determined from the corresponding peak curves ((E)-aldosecologanin, chlorogenic acid, luteolin 7-O-glucoside, sweroside). For L. japonica, additional unknown peaks were detected at 13.8 min, 20.6 min, and 36.9 min. And, we developed genetic marker using the the tRNA-Leu gene, trnL-trnF intergenic spacer and tRNA-Phe region of chloroplast DNA. By the method, 164 bp PCR product amplified from L. confusa was distinguished into L. japonica and L. confusa efficiently. Conclusion : Base on these results, two techniques provide effective approaches to distinguish L. japonica from L. confusa.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1450-1470
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• 2023

In a Vehicular Cloud (VC) network, an announcement protocol plays a critical role in promoting safety and efficiency by enabling vehicles to disseminate safety-related messages. The reliability of message exchange is essential for improving traffic safety and road conditions. However, verifying the message authenticity could lead to the potential compromise of vehicle privacy, presenting a significant security challenge in the VC network. In contrast, if any misbehavior occurs, the accountable vehicle must be identifiable and removed from the network to ensure public safety. Addressing this conflict between message reliability and privacy requires a secure protocol that satisfies accountability properties while preserving user privacy. This paper presents a novel announcement protocol for secure communication in VC networks that utilizes group signature to achieve seemingly contradictory goals of reliability, privacy, and accountability. We have developed the first comprehensive announcement protocol for VC using group signature, which has been shown to improve the performance efficiency and feasibility of the VC network through performance analysis and simulation results.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.127-135
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• 2021

We analyze the information asymmetry in the capital market by examining the long-term performance by the insider's trading behavior in the companies that made investment announcements for the construction of the new office building. The results are summarized as follows. On average, the long-term abnormal returns on share prices of sample firms represent a significant positive value. The regression analysis confirmed that there is a statistically significant positive correlation between the factor of the change in equity of large shareholders and the long-term performance. On the other hand, negative correlation was observed between change in equity of small individual investors and long-term performance. These results mean that an insider can determine the authenticity of a manager's private intention. In other words, it supports that the insider is in a position of information superiority. In addition, it is expected to provide practical usefulness to investors in that the change in equity can be used as a predictor of long-term performance.