• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.49-55
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• 2016

In digital forensic images, there is a serious problem that is distributed with various image types. For the problem solution, this paper proposes a classification algorithm of the forensic image types. The proposed algorithm extracts the 21-dim. feature vector with the contrast and energy from GLCM (Gray Level Co-occurrence Matrix), and the entropy of each image type. The classification test of the forensic images is performed with an exhaustive combination of the image types. Through the experiments, TP (True Positive) and FN (False Negative) is detected respectively. While it is confirmed that performed class evaluation of the proposed algorithm is rated as 'Excellent(A)' because of the AUROC (Area Under Receiver Operating Characteristic Curve) is 0.9980 by the sensitivity and the 1-specificity. Also, the minimum average decision error is 0.1349. Also, at the minimum average decision error is 0.0179, the whole forensic image types which are involved then, our classification effectiveness is high.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.111-114
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• 2012

본 논문에서는 국내 디지털 저작권 보호 업체 혹은 대학 및 연구소에서 개발하고 생산하는 포렌식마크 기술의 품질을 객관적으로 평가할 수 있는 평가절차와 평가지표를 정량적으로 제시하였다. 포렌식마크 기술을 객관적으로 평가하기 위해서 본 논문에서는 우선, 구매자 정보(포렌식마크라)가 삽입된 테스트 영상의 공격 항목과 수준을 정하고, 포렌식마크 정보의 추출 성능을 평가하기 위한 평가절차로서 평가항목, 평가기준, 평가절차를, 그리고 인증을 위해서는 포렌식마크 기술의 신뢰성에 대한 통계정보를 포함하는 인증서를 생성하기 위한 인증절차를 포함하였다. 이러한 포렌식마크 기술의 평가 및 인증 지침은 기술 개발자에게는 자신들이 개발한 포렌식마크 기술에 대한 객관적인 평가결과를 미리 알아볼 수 있어서 기술의 상품성을 점검할 수 있고 소비자 입장에서는 객관적이고 보편타당성 있는 평가 결과에 대한 신뢰를 가질 수 있다. 평가자 입장에서는 표준 평가를 통해 객관적이고 정량적인 평가 결과를 얻을 수 있어서 상대적인 우열을 가리기가 용이해지는 편리성을 얻을 수 있다. 이를 통하여 포렌식마크 기술의 발전과 디지털 저작권 보호 시장의 활성화에 일조를 할 수 있을 것으로 사료된다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.4
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• pp.647-659
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• 2023

As digital evidence becomes more important in criminal investigations, disputes are increasing in court. As media diversifies and the scope of analysis expands, the level of expertise in digital forensics is also increasing. However, no competency model has been developed to define the capabilities of digital evidence examiners or to judge their expertise. There have been some studies that have derived the capabilities necessary for digital evidence examiner, but they are still insufficient. Therefore, in this study, 25 competency evaluation factors in a total of 9 competency groups were defined using methodologies such as expert FGI and Delphi survey. Specifically, it was defined as Digital Forensics Theory, Digital Evidence Collection&Management, Disk Forensics, Mobile Forensics, Video Forensics, infringement forensics, DB Forensics, Embedded(IoT) Forensics, and Cloud Forensics. The digital evidence examiner competency model is expected to be used in various fields such as recruitment, education and training, and performance evaluation in the future.

• Journal of Internet Computing and Services
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• v.9 no.6
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• pp.81-87
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• 2008

In this paper, we propose a forensic watermarking scheme using temporal differences of video chrominance values. Proposed method has low complexity and good visual quality, which is suitable for traitor tracing of digital cinema. Experimental results show that our forensic watermarking method has good detection performance and is robust to several attacks, such as camcorder recording and video compression.

• Review of KIISC
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• v.21 no.6
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• pp.57-66
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• 2011

국내 스마트폰의 사용자는 2천만 명에 도달하고 있고, Wi-Fi Zone과 3G망 뿐만 아니라, 4G WiBro와 LTE로 속도까지 향상 되어 스마트폰의 활용은 더욱 높아지고 있다. 또한 일부 스마트폰 사용자들은 본인의 스마트폰 단말기를 해킹과 루팅을 통하여 멀티미디어 콘텐츠를 불법으로 이용하고 있다. 이는 한 미 FTA체결에 저작권의 법적 문제제기가 있다. 또한 기존의 이동통신 2G, 3G 휴대폰들과 스마트폰은 휴대용 이용기기로서, 범죄에 사용되는 직간접 증거들과 관련성이 높아서, 휴대폰과 스마트폰에서 생성 저장된 디지털 증거에 대한 스마트폰 포렌식 적용에 대한 연구가 필요하다. 스마트폰은 휴대폰 보다 다양한 기능을 지원해주기 때문에 스마트폰의 사용정보에는 연락처, 통화기록, 인터넷, 메세지, 사진, 동영상 등의 많은 정보를 가지고 있으며 이러한 정보는 포렌식 수사 과정에서 사용자의 행위를 유추하는데 도움이 될 수 있다. 본 디지털 증거 수집과 분석을 위한 스마트폰 포렌식 적용에서는 법정에서 사용될 포렌식 증거의 수집과 분석을 위한 압수 수색 방법과 주의할 점을 연구하였다.

• Korea Information Processing Society Review
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• v.21 no.6
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• pp.11-16
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• 2014

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.11
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• pp.427-438
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• 2016

Car blackboxes record the information and status of driving. Since blackboxes are commonly used in daily life, the usage of video data recorded from blackboxes is increasing for investigating. Investigators use a own analysis tool suitable for their blackbox provided by the manufacturer in order to check the data. But the tools are not enough to use in the digital forensic analysis because they are dependent on a specific model of blackbox and provides ungeneralized functions. Moreover, if the manufacturer is bankrupt, then their own tools can not be obtained also. Therefore, the way data are stored in the blackboxes which are now in the market are investigated and the features and limitations which have blackbox's own analysis tools are checked. And a comprehensive tool for the analysis of blackboxes is designed and implemented as in this paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.323-331
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• 2013

There are three ways how to extract forensic evidence from mobile phone, such as SYN, JTAG, Revolving. However, it should be a different way to extract forensic evidence due to the differences of their usage and technology between them(mobile phone and smart phone). Therefore, in this paper, I will come up with extraction method that forensics evidence by search and seizure of a smart phone. This study aims to analyze specifications and O.S., backup analysis, evidence in smart to analyze for search and seizure of a smart phone commonly used google android and windows mobile smart phone. This study also aim to extract forensics evidence related to google android and phone book, SMS, photos, video of window mobile smart phone to make legal evidence and forensics report. It is expected that this study on smart phone forensics technology will contribute to developing mobile forensics technology.

• Journal of Broadcast Engineering
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• v.18 no.2
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• pp.196-203
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• 2013

The importance of Digital Rights Protection technologies, especially the forensic marking, is getting larger and larger as the demand for the digital contents is increased. But the technologies for the evaluation of forensic marking is not set up properly due to the conflicts between interested parties and technical difficulties. Therefore to evaluate the performance of forensic marking objectively, image and video modification items/levels and evaluation criteria are essential. This paper suggests the quantitative system for evaluating the performance of forensic marking objectively. By providing the opportunity to evaluate the Digital Rights Protection product in objective and quantitative manner, forensic marking industry can expedite its technology development and consumer can get objective and universally validated performance result. It is expected this technology could help revitalizing the related industries and help expediting the development of forensic mark technologies.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.5
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• pp.42-47
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• 2017

In the distribution of digital image, the median filtering is used for a forgery. This paper proposed the algorithm of a image forensics detection for the classification of median filtering. For the solution of this grave problem, the feature vector is composed of 42-Dim. The detected quantity 32, 64 and 128 of forgery image edges, respectively, which are processed by the Hough transform, then it extracted from the start-end point coordinates of the Hough Lines. Also, the Hough Peaks of the Angle-Distance plane are extracted. Subsequently, both of the feature vectors are composed of the proposed scheme. The defined 42-Dim. feature vector is trained in SVM (Support Vector Machine) classifier for the MF classification of the forged images. The experimental results of the proposed MF detection algorithm is compared between the 10-Dim. MFR and the 686-Dim. SPAM. It confirmed that the MF forensic classification ratio of the evaluated performance is 99% above with the whole test image types: the unaltered, the average filtering ($3{\times}3$), the JPEG (QF=90 and 70)) compression, the Gaussian filtered ($3{\times}3$ and $5{\times}5$) images, respectively.