• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.67-73
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• 2015

In a distribution of digital image, there is a serious problem that is the image alteration by a forger. For the problem solution, this paper proposes the forensic decision algorithm of a median filtering (MF) image using the feature vector based on a coefficient of variation (c.v.) of Fourier transform. In the proposed algorithm, we compute Fourier transform (FT) coefficients of row and column line respectively of an image first, then c.v. between neighboring lines is computed. Subsquently, 10 Dim. feature vector is defined for the MF detection. On the experiment of MF detection, the proposed scheme is compared to MFR (Median Filter Residual) and Rhee's MF detection schemes that have the same 10 Dim. feature vector both. As a result, the performance is excellent at Unaltered, JPEG (QF=90), Down scaling (0.9) and Up scaling (1.1) images, and it showed good performance at Gaussian filtering ($3{\times}3$) image. However, in the performance evaluation of all measured items of the proposed scheme, AUC (Area Under ROC (Receiver Operating Characteristic) Curve) by the sensitivity and 1-specificity approached to 1 thus, it is confirmed that the grade of the performance evaluation is rated as 'Excellent (A)'.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.58-60
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• 2019

Double JPEG compression detection is one of the most important ways of exposing the integrity of the JPEG image in image forensics. Several methods have been proposed for discriminating against the double JPEG image. In this paper, we propose a new method for restoring the JPEG compressed image and making the detector confused by introducing a Generative Adversarial Network (GAN). First, a generator network is designed for restoring the JPEG compressed image and analyzed the quality. Then, the restored image is tested with the double compression detector for evaluating the robustness of the proposed GAN model. The detection accuracy reduces from 98% to 58%.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.2
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• pp.75-86
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• 2017

Docker, which is one of the various virtualization technology in server systems, is getting popular as it provides more lightweight environment for service operation than existing virtualization technology. It supports easy way of establishment, update, and migration of server environment with the help of image and container concept. As the adoption of docker technology increases, the attack motive for the server for the distribution of docker images and the incident case of attacking docker-based hosts would also increase. Therefore, the method and procedure of digital forensic investigation of docker-based host including the way to extract the filesystem of containers when docker daemon is inactive are presented in this paper.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.245-247
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• 2012

We know that JPEG image format is one of the most popular image formats in the digital area and distribution of digital photographic drawing it is interested frequently in certain types of forensic investigation. In most case, corrupted images are shown gaudiness with the boundary of the corrupted parts. In the paper, we propose a technique to carve correct JPEG images using transformation method and the approach can be used for JPEG image file carving tool development.

• Imaging Science in Dentistry
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• v.51 no.2
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• pp.187-193
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• 2021

Purpose: This study aimed to assess the usefulness of machine learning and automation techniques to match pairs of panoramic radiographs for personal identification. Materials and Methods: Two hundred panoramic radiographs from 100 patients (50 males and 50 females) were randomly selected from a private radiological service database. Initially, 14 linear and angular measurements of the radiographs were made by an expert. Eight ratio indices derived from the original measurements were applied to a statistical algorithm to match radiographs from the same patients, simulating a semi-automated personal identification process. Subsequently, measurements were automatically generated using a deep neural network for image recognition, simulating a fully automated personal identification process. Results: Approximately 85% of the radiographs were correctly matched by the automated personal identification process. In a limited number of cases, the image recognition algorithm identified 2 potential matches for the same individual. No statistically significant differences were found between measurements performed by the expert on panoramic radiographs from the same patients. Conclusion: Personal identification might be performed with the aid of image recognition algorithms and machine learning techniques. This approach will likely facilitate the complex task of personal identification by performing an initial screening of radiographs and matching ante-mortem and post-mortem images from the same individuals.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1886-1898
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• 2015

In today’s era of advanced technological developments, the threats to the authenticity and integrity of digital images, in a nutshell, the threats to the Image Forensics Research communities have also increased proportionately. This happened as even for the ‘non-expert’ forgers, the availability of image processing tools has become a cakewalk. This image forgery poses a great problem for judicial authorities in any context of trade and commerce. Block matching based image cloning detection system is widely researched over the last 2-3 decades but this was discouraged by higher computational complexity and more time requirement at the algorithm level. Thus, for reducing time need, various dimension reduction techniques have been employed. Since a single technique cannot cope up with all the transformations like addition of noise, blurring, intensity variation, etc. we employ multiple techniques to a single image. In this paper, we have used Fuzzy logic approach for decision making and getting a global response of all the techniques, since their individual outputs depend on various parameters. Experimental results have given enthusiastic elicitations as regards various transformations to the digital image. Hence this paper proposes Fuzzy based cloning detection and classification system. Experimental results have shown that our detection system achieves classification accuracy of 94.12%. Detection accuracy (DAR) while in case of 81×81 sized copied portion the maximum accuracy achieved is 99.17% as regards subjection to transformations like Blurring, Intensity Variation and Gaussian Noise Addition.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1624-1631
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• 2021

With the continued development of image processing technology, we live in a time when it is difficult to visually discriminate processed (or tampered) images from real images. However, as the risk of fake images being misused for crime increases, the importance of image forensic science for identifying fake images is emerging. Currently, various deep learning-based identifiers have been studied, but there are still many problems to be used in real situations. Due to the inherent characteristics of deep learning that strongly relies on given training data, it is very vulnerable to evaluating data that has never been viewed. Therefore, we try to find a way to improve generalization ability of deep learning-based fake image identifiers. First, images with various contents were added to the training dataset to resolve the over-fitting problem that the identifier can only classify real and fake images with specific contents but fails for those with other contents. Next, color spaces other than RGB were exploited. That is, fake image identification was attempted on color spaces not considered when creating fake images, such as HSV and YCbCr. Finally, dropout, which is commonly used for generalization of neural networks, was used. Through experimental results, it has been confirmed that the color space conversion to HSV is the best solution and its combination with the approach of increasing the training dataset significantly can greatly improve the accuracy and generalization ability of deep learning-based identifiers in identifying fake images that have never been seen before.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.7
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• pp.307-314
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• 2017

Stick-PC is small and portable, So it can be used like a desktop if you connect it to a display device such as a monitor or TV anytime and anywhere. Accordingly, Stick-PC can related to various crimes, and various evidence may remain. Stick-PC uses the same Windows version of the operating system as the regular Desktop, the artifacts to be analyzed are the same. However, unlike the Desktop, it can be used as a meaningful information for forensic investigation if it is possible to identify the actual user and trace the usage by finding the traces of peripheral devices before analyzing the system due to the mobility. In this paper, We presents a method of collecting images using Bootable OS, which is one of the image collection methods of Stick-PC. In addition, we show how to analyze the trace of peripheral connection and network connection trace such as Display, Bluetooth through the registry and event log, and suggest the application method from the forensic point of view through experimental scenario.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.287-290
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• 2003

Over the last few years computer based image processing has become more prominent in forensic science. The image quality from many CCTV systems is too poor for facial recognition. but there are other human characteristics which allow us to recognize individuals from a distance. one of these parameters is a human's height. In this paper, we propose useful height measurement method by auto Position, size adjustment which uses image superimposition and edge detection regardless of lens distortion and not uses conventional photogrammetry calibration methods.

• Bulletin of the Korean Chemical Society
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• v.32 no.4
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• pp.1387-1390
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• 2011