• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2006.11a
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• pp.313-316
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• 2006

디지털 핑거프린팅(Digital Fingerprinting)은 기 밀 정보를 디지털 콘텐츠에 삽입하는 측면에서는 디지털 워터마킹과 동일 하다고 볼 수 있으나 저작권자나 판매자의 정보가 아닌 콘텐츠를 구매한 사용자의 정보를 삽입함으로써 콘텐츠 불법 배포자를 추적할 수 있도록 한다는 점에서 워터마킹과 차별화된 기술이다. 이러한 핑거프린팅 기술은 소유권에 대한 인증뿐만 아니라 개인 식별 기능까지 제공해야 하므로 기존의 워터마킹이 갖추어야 할 요구사항인 비가시성, 견고성, 유일성과 더불어 공모허용, 비대칭성, 익명성, 조건부 추적성 등이 부가적으로 필요하다. 본 논문에서는 행렬의 한 열을 선택 후 쉬프팅 기법을 사용 하서 사용자 정보로 조합하여 핑거프린트를 생성하였다. 이렇게 생성된 핑거프린트 정보를 2레벨 웨이블릿 변환 영역 중 LH2, HL2, HH2 부대역에 삽입하였다. 쉬프팅 정보와 도메인 개념을 사용하여 보다 많은 사용자에게 핑거프린트 정보를 삽입할 수 있으며, 공모공격과 JPEG 압축에서도 최소한 1명 이상의 공모자를 검출할 수 있는 핑거프린팅의 기본 조건을 만족하였다.

• Review of KIISC
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• v.16 no.1
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• pp.49-58
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• 2006

디지털 핑거프린팅(Digital Fingerprinting) 기술은 온라인상에서 멀티미디어 콘텐츠의 저작권을 보호하기 위한 기술의 하나로 워터마킹(Watermarking) 기술과 같이 콘텐츠에 저작권을 증명하기 위한 부가정보를 비인지적으로 삽입하고 추출하는 기술이다. 핑거프린팅 기술에서는 주로 구매자의 정보를 삽입하기 때문에 콘텐츠를 처음 유포한 구매자를 역추적 할 수 있는 기능(trace traitor)을 제공한다. 본 고에서는 핑거프린팅 된 콘텐츠에서 악의적인 사용자가 핑거프린트를 제거하기 위하여 같은 콘텐츠를 구매한 다른 구매자와 공모하는 기술과, 이런 공모 후에도 핑거프린트를 추출할 수 있도록 삽입 코드를 공모 허용하도록 설계하는 공모보안코드에 대해 고찰한다.

• Journal of KIISE:Databases
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• v.41 no.4
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• pp.199-208
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• 2014

In this paper, we represent a system of identification and feature search for scanned comics in consideration of their content characteristics. For creating the feature of the scanned comics, we utilize a method of hierarchical symmetry fingerprinting. Proposed identification and search system is designed to give online service provider, such as Webhard, an immediate identification result under conditions of huge volume of the scanned comics. In simulation part, we analyze the robustness of the identification of the fingerprint to image modification such as rotation and translation. Also, we represent a structure of database for fast matching in feature point database, and compare search performance between other existing searching methods such as full-search and most significant feature search.

• The Journal of the Korea Contents Association
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• v.12 no.5
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• pp.1-9
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• 2012

The popularity of digital music and smart phones led to develope noise-robust real-time audio fingerprinting system in various ways. In particular, The Multiple Hashing(MLH) of fingerprint algorithms is robust to noise and has an elaborate structure. In this paper, we propose a filter engine based on MLH to achieve better performance. In this approach, we compose a energy-intensive filter to improve the accuracy of Q/R from music database and a statistic filter to remove continuity and redundancy. The energy-intensive filter uses the Discrite Cosine Transform(DCT)'s feature gathering energy to low-order bits and the statistic filters use the correlation between searched fingerprint's information. Experimental results show that the superiority of proposed algorithm consists of the energy and statistical filtering in noise environment. It is found that the proposed filter engine achieves more robust to noise than Philips Robust Hash(PRH), and a more compact way than MLH.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1463-1471
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• 2016

The indoor localization algorithm using the Difference Means based on Fingerprint (DMFPA) to improve the performance of indoor localization in moving Wi-Fi environment is proposed in this paper. In addition to this, the performance of the proposed algorithm is also compared with the Original Fingerprint Algorithm (OFPA) and the Gaussian Distribution Fingerprint Algorithm (GDFPA) by our developed indoor localization simulator. The performance metrics are defined as the accuracy of the average localization accuracy; the average/maximum cumulative distance of the occurred errors and the average measurement time in each reference point.

• Journal of Broadcast Engineering
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• v.20 no.3
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• pp.379-387
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• 2015

In recent years, copyright violations from illegal copying and distribution of e-comic contents have become an important issue. Fingerprinting techniques have been emerged to provide a fast and reliable identification method of identifying e-comic contents. When illegally scanned or camera captured comic contents are distributed, they suffer from distortions. So the fingerprint differs from the original version. This paper presents a restoration framework for correcting geometric distortions in distorted comics to improve the comic content identification.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.458-459
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• 2021

Recently, researches using deep learning technology based on Wi-Fi fingerprints have been conducted for accurate services in indoor location-based services. Among the deep learning models, an RNN model that can store information from the past can store continuous movements in indoor positioning, thereby reducing positioning errors. At this time, continuous sequential data is required as training data. However, since Wi-Fi fingerprint data is generally managed only with signals for a specific location, it is inappropriate to use it as training data for an RNN model. This paper proposes a path generation method through prediction of a moving path based on Wi-Fi fingerprint data extended to region data through clustering to generate sequential input data of the RNN model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.456-457
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• 2021

Recently, in order to more accurately provide indoor location-based services, technologies using Wi-Fi fingerprints and deep learning are being studied. Among the deep learning models, an RNN model that can store information from the past can store continuous movements in indoor positioning, thereby reducing positioning errors. When using an RNN model for indoor positioning, the collected training data must be continuous sequential data. However, the Wi-Fi fingerprint data collected to determine specific location information cannot be used as training data for an RNN model because only RSSI for a specific location is recorded. This paper proposes a region clustering technique for sequential input data generation of RNN models based on Wi-Fi fingerprint data.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.1
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• pp.92-98
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• 2014

Recently, Fingerprint approach using RSSI based on WLAN has been many studied in order to construct low-cost indoor localization systems. Because this technique is relatively evaluated non-precise positioning technique compared with the positioning of Ultra-Wide-Band(UWB), the performance of the Fingerprint based on WLAN should be continuously improved to implement various indoor location. Therefore, this paper presents a Fingerprint approach which can improve the performance of localization by using RSSI and LQI contained IEEE 802.15.4 standard. The advantages of these techniques are that the characteristics of each location is created more clearly by utilizing RSSI and LQI and Fingerprint technique is improved by using the modified Euclidean distance method. The experimental results which are applied in NLOS indoor environment with various obstacles show that the accuracy of localization is improved to 22% compared to conventional Fingerprint.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.12
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• pp.1021-1029
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• 2013

In this paper, we propose a new indoor location estimation method which combines the fingerprint technique with the propagation prediction model. The wireless LAN (WLAN) access points (APs) deployed indoors are divided into public APs and private APs. While the fingerprint method can be easily used to public APs usually installed in fixed location, it is difficult to apply the fingerprint scheme to private APs whose location can be freely changed. In the proposed approach, the accuracy of user location estimation is improved by simultaneously utilizing public and private APs. Specifically, the fingerprint method is used to the received signals from public APs and the propagation prediction model is employed to the signals from private APs. The performance of the proposed method is compared with that of conventional indoor location estimation schemes through measurements and numerical simulations in WLAN environments.