• Journal of radiological science and technology
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• v.46 no.4
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• pp.315-323
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• 2023

Post-mortem computed tomography (PMCT) is commonly employed in the field of forensic pathology. PMCT was mainly performed using a whole-body scan with a wide field of view (FOV), which lead to a decrease in spatial resolution due to the increased pixel size. This study aims to evaluate the potential for developing a super-resolution model based on conditional generative adversarial networks (CGAN) to enhance the image quality of CT. 1761 low-resolution images were obtained using a whole-body scan with a wide FOV of the head phantom, and 341 high-resolution images were obtained using the appropriate FOV for the head phantom. Of the 150 paired images in the total dataset, which were divided into training set (96 paired images) and validation set (54 paired images). Data augmentation was perform to improve the effectiveness of training by implementing rotations and flips. To evaluate the performance of the proposed model, we used the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Deep Image Structure and Texture Similarity (DISTS). Obtained the PSNR, SSIM, and DISTS values of the entire image and the Medial orbital wall, the zygomatic arch, and the temporal bone, where fractures often occur during head trauma. The proposed method demonstrated improvements in values of PSNR by 13.14%, SSIM by 13.10% and DISTS by 45.45% when compared to low-resolution images. The image quality of the three areas where fractures commonly occur during head trauma has also improved compared to low-resolution images.

• Proceedings of the Korea Concrete Institute Conference
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• 2003.05a
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• pp.181-186
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• 2003

There are many uncertainties in structural failures or structures, so probabilistic failure cause assessment should be performed in order to consider the uncertainties. However, in many cases of forensic engineering, the failure cause assessments are performed by deterministic approach though number of uncertainties are existed in the failures or structures. Thus, deterministic approach may have possibility for leading to unreasonable and unrealistic failure cause assessment due to ignorance of the uncertainties. Therefore, probabilistic approach is needed to complement the shortcoming of deterministic approach and to perform the more reasonable and realistic failure cause assessment. In this study, reliability-based failure cause assessment (reliability based forensic engineering) is performed, which can incorporate uncertainties in failures and structures. For more practical application, the modified ETA technique is proposed, which automatically generates the defected structural model, performs structural analysis and reliability analysis, and calculates the failure probabilities of the failure events and the occurrence probabilities of the failure scenarios. Also, for more precise reliability analysis, uncertainties are estimated more reasonably by using bayesian approach based on the experimental laboratory testing data in forensic report.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1171-1179
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• 2021

Image forensic is performed to check image limpidness. In this paper, a robust scheme is discussed to detect median filtering in low quality images. Detection of median filtering assists in overall image forensic. Improved spatial statistical features are extracted from the image to classify pristine and median filtered images. Image array data is rescaled to enhance the spatial statistical information. Features are extracted using Markov model on enhanced spatial statistics. Multiple difference arrays are considered in different directions for robust feature set. Further, texture operator features are combined to increase the detection accuracy and SVM binary classifier is applied to train the classification model. Experimental results are promising for images of low quality JPEG compression.

• 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 Society of Computer and Information
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• v.19 no.12
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• pp.161-169
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• 2014

Digital forensics is the process of proving criminal charges by collecting and analyzing digital evidence which is related to the crime in question. Most digital forensic research is focused on digital forensic techniques themselves or cyber crime. In this paper, we designed a digital forensics-criminal investigation linked model in order to effectively apply digital forensics to various types of criminal investigations. Digital forensic ontology was developed based on this model. For more effective application of digital forensics to criminal investigation we derived specific application fields. The ontology has legality rules and adequacy rules, so it can support investigative decision-making. The ontology can be developed into an intelligent criminal investigation system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.4
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• pp.609-619
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• 2023

Recently, crimes using drones, one of the IoT industries have been continuously reported. In particular, drones are characterized by easy access and free movement, so they are used for various crimes such as transporting explosives, transporting drugs, and illegal recording. In order to analyze and investigate these criminal acts, drone forensic research is highly emphasized. Media data, PII, and flight records are digital forensic artifacts that can be acquired from drones, in particluar flight records are important artifacts since they can be used to trace drone activities. Therefore, in this paper, the characteristics of the deleted flight record files of DJI drones are presented and verified using the Phantom3, Phantom4 andMini2 models, two drones with differences in characteristics. Additionally, the recovery level is analyzed using the flight record file characteristics, and lastly, drones with the capacity to recover flight records for each drone model and drone models without it are classified.

• Anatomy and Cell Biology
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• v.56 no.2
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• pp.259-267
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• 2023

Stature is an essential component of biological profile analysis since it determines an individual's physical identity. Long bone dimensions are generally used to estimate the stature of skeletal remains; however, non-long bones such as the sternum, cranium, and sacrum may be necessary for some forensic situations. This study aimed to generate a regression equation for stature estimation of the skeletal remains in the Thai population. Ten measurements of the sacrum were measured from 200 dry sacra. The results revealed that the maximum anterior breadth (MAB) provided the most accurate stature prediction model among males (correlation coefficient [r]=0.53), standard error of estimation (SEE=5.94 cm), and females (r=0.48, SEE=6.34 cm). For the multiple regression model, the best multiple regression models were stature equals 41.2+0.374 (right auricular surface height [RASH])+1.072 (anterior-posterior outer diameter of S₁ vertebra corpus [APOD])+0.256 (dorsal height [DH])+0.417 (transverse inner diameter of S₁ vertebra corpus [TranID])+0.2 (MAB) with a SEE of 6.42 cm for combined sex. For males, stature equals 63.639+0.478 (MAB)+0.299 (DH)+0.508 (APOD) with a SEE of 5.35, and stature equals 75.181+0.362 (MAB)+0.441 (RASH)+0.132 (maximum anterior height [MAH]) with a SEE of 5.88 cm for females. This study suggests that regression equations derived from the sacrum can be used to estimate the stature of the Thai population, especially when a long bone is unavailable.

• The Journal of Korean Institute of Information Technology
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• v.17 no.8
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• pp.1-9
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• 2019

With the development of imaging information communication technology in modern society, imaging acquisition and mass production technology have developed rapidly. However, crime rates using these technology are increased and forensic studies are conducted to prevent it. Identification techniques for image acquisition devices are studied a lot, but the field is limited to images. In this paper, camera model identification technique for video, not image is proposed. We analyzed video frames using the trained model with images. Through training and analysis by considering the frame characteristics of video, we showed the superiority of the model using the P frame. Then, we presented a video camera model identification system by applying a majority-based decision algorithm. In the experiment using 5 video camera models, we obtained maximum 96.18% accuracy for each frame identification and the proposed video camera model identification system achieved 100% identification rate for each camera model.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06a
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• pp.29-30
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• 2008

• Communications for Statistical Applications and Methods
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• v.31 no.3
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• pp.279-289
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• 2024

Familial searching is a useful technique in a forensic investigation. Using genetic information, it is possible to identify individuals, determine familial relationships, and obtain racial/ethnic information. The total number of shared alleles (TNSA) and likelihood ratio (LR) methods have traditionally been used, and novel data-mining classification methods have recently been applied here as well. However, it is difficult to apply these methods to identify familial relationships above the third degree (e.g., uncle-nephew and first cousins). Therefore, we propose to apply a stacking ensemble machine learning algorithm to improve the accuracy of familial relationship identification. Using real data analysis, we obtain superior relationship identification results when applying meta-classifiers with a stacking algorithm rather than applying traditional TNSA or LR methods and data mining techniques.