• Korean Journal of Forensic Psychology
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• v.11 no.2
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• pp.173-190
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• 2020

The purpose of this study is to verify whether it is possible for participants to discriminate between innocent and guilty suspects when they are exposed to criminal information utilizing an autobiographical implicit association test (aIAT). A total of 49 college students were randomly assigned to guilty group, innocent-aware group, or innocent-unaware group. Participants performed an aIAT to detect suspects after performing either mock crime or control task. It was verified that innocent suspect and guilty suspect exposed with crime information could be distinguished through D-score and reaction time, converted to symbolize strength of the association between guilty sentences, innocent sentences, and truth sentences. As a result of the analysis, guilty group showed significantly higher D-score than both innocent-aware group and innocent-unaware group. guilty group also showed faster response time in true-guilty condition than true-innocent condition. This shows that the association of true-guilty conditions is stronger than that of true-innocent conditions. On the other hand, the innocent-aware group showed a faster response time in the true-innocent condition than the true-guilty condition, and innocent-unaware group showed no significant difference between the two conditions. Through this, it was confirmed that innocent suspects exposed to criminal information can be discriminated according to the aIAT pattern, which has a faster reaction rate to the truth and innocence union than the guilty group. This study confirmed that suspects exposed to criminal information can be effectively discriminated using aIAT, and further suggests the usefulness and potential of aIAT in the field of lie detection.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.397-402
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• 2022

익명 네트워크 기술에 기반한 다크웹은 일반 표면웹보다 더 강화된 익명성을 제공한다. 최근 이 익명성을 악용하여 다수의 다크웹 사용자들이 다크웹 내에서 범죄 행위를 모의하는 행위가 꾸준히 발생하고 있다. 특히, 국내 다크웹 사용자들은 마약 유포를 위한 방법을 공유하거나 성착취물 유포 행위 등에 직간접적으로 가담하고 있다. 이와 같은 범죄 행위들은 수사 기관의 눈을 피해 현재까지도 계속해서 발생하고 있어 국내 다크웹 범죄 동향 파악의 필요성이 증대되고 있다. 그러나 다크웹 특성상 범죄 행위를 논의하는 게시글을 수집하기가 어렵고, 다크웹 내에서의 언어 사용 특성에 대한 이해 부족으로 그동안 다크웹 사용자들이 어떤 내용의 범죄를 모의하는지 파악하기가 어려웠다. 본 논문에서는 국내 사용자들이 활동하는 다크웹 포럼들을 중심으로 사용자들의 언어 사용 특성을 연구하고, 이를 통해 다크웹에서 다뤄지는 범죄 유형들을 분석한다. 이를 위해, 자연어처리 기반의 분석 방법론을 적용하여 다크웹에서 공유되는 게시글을 수집하고 다크웹 사용자들의 은어와 특정 범죄군에서 선호되는 언어 특성을 파악한다. 특히 현재 다크웹 내에서 사용자들 사이에 관측되는 어휘들에 대한 기술통계 분석과 유의어 관계 분석을 수행하였고, 실제 다크웹 내에서 사용자들이 어떠한 범죄에 관심이 많은지를 분석하였으며, 더 나아가 수사의 효율성을 증대시키기 위한 소셜미디어, URL 인용 빈도에 대한 연구를 진행하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2010.11a
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• pp.127-128
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• 2010

본 논문에서는 CCTV 감시 영상에서 취득한 얼굴 이미지를 이용하여, 범죄자 감시목록에 등록된 범죄 용의자를 탐지 식별하는 시스템을 설계 및 구현하였다. 특히 본 논문에서 제안한 SVDD와 SRC를 혼합한 계층적 구조의 범죄 용의자 식별 모듈은 다음과 같은 특성을 갖는다: 1) 먼저 SVDD를 이용하여 범죄 용의자만을 빠르게 인식함으로써, 일반인에 대한 불필요한 범죄자 식별 연산을 수행하지 않는다; 2) 다양한 식별 성능을 저해하는 환경에서도 이미 강인한 성능이 검증된 SRC를 범죄 용의자 식별과정에 적용함으로써 안정적이고 정확한 식별 시스템을 보장한다; 3) 동일 생체 특정의 반복적 사용을 통한 다수결 투표전략을 취함으로써 시스템의 신뢰도를 보장한다; 4) 점증적 갱신의 학습 능력으로 인하여 범죄 용의자 감시목록 데이터베이스의 변화에도 능동적으로 적응한다 실제 KUFD(Korea University Face Database)를 자체 제작하고 캠퍼스 내에서 CCTV 환경의 얼굴 인식 기반 범죄 용의자 탐지 및 식별 시스템 환경을 모의 구축하여 실험적으로 제안된 시스템의 성능을 검증한다.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.06a
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• pp.477-484
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• 2008

연쇄 살인과 같은 강력 범죄의 심각성이 사회적 이슈가 되면서 이에 대한 효과적인 과학 수사의 필요성이 증가되고 있다. 특히, 연쇄 범죄 데이타에 대한 공간 분석을 통해 범죄자의 거점 위치를 예측하는 지리적 프로파일링과 미래에 발생될 범행 장소의 위치, 즉 기존 범행에 이어 일어날 다음 범행 위치 예측에 관한 연구가 활발하다. 그러나, 이와 관련된 기존 연구는 물리적인 거리에 대한 통계적 기법을 적용하거나 단순한 공간적 분석만을 적용하므로 낮은 예측 정확도를 보이는 문제점이 있다. 본 논문에서는 이러한 문제를 해결하고 보다 효과적인 연쇄 범죄 수사를 지원하는 방법으로써 연쇄 범죄 발생에 대한 공간적 시간적 분포 특성에 따른 시공간 패턴을 기반으로 다양한 시공간 분석을 적용하는 거점 위치 예측 기법과 다음 범행 위치 예측 기법을 제안한다. 제안 기법은 중심축을 따라 나타나는 선형 분포의 연쇄 범죄에서도 정확도 높은 예측이 가능하고, 다수의 서로 다른 군집들에 대해 각 군집내 범행에 대한 지역적 예측과 대상 영역의 모든 범행에 대한 전역적 예측이 가능하다. 또한 방향 패턴을 활용하여 다음 범행 위치 예측 정확도도 개선하였다.

• Science of Emotion and Sensibility
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• v.24 no.2
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• pp.3-12
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• 2021

Most researchers have experimentally evaluated the detection efficiency of reaction time-based concealed information tests (RT CIT). In experimental studies, two experimental paradigms have been mainly used to create a lying situation, mock-crime paradigm and personal-item paradigm. This study is aimed at testing the detection efficiency of RT CIT for the one that could be estimated as the same as the other, regardless of the experimental paradigms. In study 1, it was tested whether the effect size of RT CIT was different in the two experimental paradigms through meta-analysis. As a result of the meta-analysis of 39 studies, the effect size (Hedges'g = 1.330) of the mock-crime paradigm was slightly larger than that (Hedges'g = 1.145) of the personal-item paradigm, but no statistically significant difference was found. Study 2 was an experimental study using both the mock-crime and personal-item paradigms, it was conducted to determine whether the detection efficiency of RT CIT differs in the two experimental paradigms. As a result of ANOVA, it was found that the RT differences between the probe and irrelevant stimuli were not significant in the two experimental paradigms. In the experimental study, the effect size (Cohen's d) of the mock-crime and personal-item paradigms were 1.638 and 1.535, respectively. In the discussion section, the reason of the detection efficiency of RT CIT not affected by the experimental paradigms was discussed.

• Convergence Security Journal
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• v.15 no.3_2
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• pp.43-51
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• 2015

Many of crimes are solved by the report of people. Therefore, countries pay compensation to crime reporter. However, the current system of compensation is focused on the report of criminal fact and criminals arrest, so that there is no compensation on the providing evidence. On the other hand, since the current judicial system adopted the principle of trial by evidence, all the facts are made by the evidence. But it is impossible to obtain all the evidence only by law enforcement agencies. Therefore, it is necessary for people to report the evidence positively. So it is necessary to positively take advantages of smart phones, vehicle black boxes and cctvs. Various incentives such as compensation would be needed to require the evidence of smartphone or black box, cctv. In order to strengthen evidence report, it will be needed the legislation of crime report compensation, smartphone apps development including the provision of various incentives.

• Journal of the Korean Regional Science Association
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• v.31 no.4
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• pp.3-24
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• 2015

Crimes must be reduced not only because of the financial, physical, and emotional damages they bring to the victims but also because crimes increase social costs by elevating distrust in society and instilling fear. With the increasing number of crimes in Korea, finding other factors that affect the occurrence of crimes is needed beyond the current viewpoint for crime analysis. Social support factors can be candidates for studies on the social support effect on crime occurrence in their initial stage. In this study, we identified the effect of social support factors on crime occurrence or deterrence, none of which has been considered important until now, given the emergence of spatial econometrics. The resulting Moran's I values revealed the existence of a spatial autocorrelation in all three crimes: heinous crimes, theft, and violence. As shown in the analysis using spatial econometrics and ordinary least squares, social support from families is significant in reducing all crimes especially violence. Social support from the local government is significant in preventing only theft. The spatial econometrics model is only valid in heinous crimes. These different effects of social support factors and spatial factors on crime occurrences are caused by the different characteristics of crimes. Hence, policymakers should consider the social support effect when they establish policies related to social housing or welfare.

• Science of Emotion and Sensibility
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• v.21 no.3
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• pp.61-72
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• 2018

Items related to a crime that are known only to criminals and investigators can be used in the concealed information test (CIT) to assess whether the suspect is guilty of the offense. However, in many cases wherein the suspect is exposed to information about the crime, the CIT cannot be used. Although the perpetrator's memories about the details of the crime are episodic, the memories of a suspect who has inadvertently discovered the details of the crime are more likely to be semantic. The retrieval of episodic memories is associated with theta wave activity, whereas that of semantic memories is associated with alpha wave activity. Therefore, these aspects of memory retrieval can be useful in identifying the perpetrator of the crime. In this study, P300-based CITs were conducted in a guilty participant in a mock crime and an innocent participant who has been given information about the simulated offense. The results demonstrate that the difference in P300 amplitudes between the probe and the irrelevant stimulus did not differ between the guilty and innocent conditions. As expected, the lower theta band power (4-6 Hz) was higher in the probe than in the irrelevant stimulus in the guilty condition, but there was no difference in the innocent condition. Conversely, the upper alpha band power (8-10 Hz) was lower in the probe than in the irrelevant stimulus in the innocent condition, but there was no difference in the guilty condition. The possibility of using theta and alpha band powers in lie detection is discussed.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1638-1641
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• 2015

최근 다양한 공공 데이터가 속속 개방되고 있지만, 지역 내의 범죄 데이터는 통계 데이터 이외에는 공개되지 않고 있다. 이처럼 공공 데이터의 확보가 어려운 경우에는 해당 지역 내의 범죄 관련 모든 온라인 뉴스 기사를 주기적으로 수집하고 범죄 현황에 관한 정보를 자동으로 추출하여 맵(map)에 시각화 하여 보여주는 프레임워크의 개발이 필요하다. 본 논문에서는 프레임워크 개발에 필요한 주요 알고리즘들과 효과적인 시각화 방안을 제안한다. 또한 이미 공개된 군산시의 범죄 발생 통계 자료를 비교하여 제안 시스템의 효용성을 평가한다.

• Journal of the Korea Society of Computer and Information
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• v.17 no.4
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• pp.163-172
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• 2012

Modern society, various great strength crimes are producing. After all crimes happen, it is most important that prevent crime beforehand than that cope. So, many research studied to prevent various crime. However, existing method of studies are to analyze and prevent by society and psychological factors. Therefore we wishes to achieve research to forecast crime by time using Markov chain method. We embody modelling for crime occurrence estimate by crime type time using crime occurrence number of item data that is collected about 5 great strength offender strength, murder, rape, moderation, violence. And examined propriety of crime occurrence estimate modelling by time that propose in treatise that compare crime occurrence type crime occurrence estimate price and actuality occurrence value. Our proposed crime occurrence estimate techniques studied to apply maximum value by critcal value about great strength crime such as strength, murder, rape etc. actually, and heighten crime occurrence estimate probability by using way to apply mean value about remainder crime in this paper. So, we wish to more study about wide crime case and as the crime occurrence estimate rate and actuality value by time are different in crime type hereafter applied examples investigating.