• Korean Security Journal
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• no.24
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• pp.67-90
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• 2010

Today, the police alone can not prevent a crime. And the police is limited to meet for people's the increased needs on public safety. So the police and the community needs the cooperation of a variety of resources. Police in cooperation with community resources to respond to the crime's most professional and the private sector is a private security. However, the role of private security for cooperation policing is insufficient in South Korea. So for this study to build a cooperative policing in South Korea as private security for the following four kinds of directions are presented. First, as a private security of the United States and Japan, specializes in diversified business sectors. Simple human-oriented private security of the building security get out. Instead, take the high-tech crime prevention or industry complex security should be changed to a professional organization. Second, the interaction between police and private security should be increased. Police and private security through regular meetings between the need for mutual interests and build consensus is needed. The role of private security companies to be represented on the Security Association of South Korea's active role in the matter. Third, efforts to improve the image of private security activities and the publiciy activity of private security is needed. Some of the private security in an effort to escape a negative image to the people and actively promote a positive image is necessary. Finally, for South Korea to the level the cooperation between the police and private security are required to develop system models. Front-line policing priority in the field and the mutual understanding between the police and private security in an effort to have a positive perception is needed. Equal partners, especially the police and private security to private security companies to have recognized experts in their own recruitment and training should be improved by strengthening the expertise.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12C
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• pp.995-1003
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• 2010

The content-based hashing for authentication and copy protection of image, video and 3D model has to satisfy the robustness and the security. For the security analysis of the hash value, the modelling method based on differential entropy had been presented. But this modelling can be only applied to the image hashing. This paper presents the modelling for the security analysis of the hash feature value in 3D model hashing based on differential entropy. The proposed security analysis modeling design the feature extracting methods of two types and then analyze the security of two feature values by using differential entropy modelling. In our experiment, we evaluated the security of feature extracting methods of two types and discussed about the trade-off relation of the security and the robustness of hash value.

• Convergence Security Journal
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• v.18 no.2
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• pp.33-40
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• 2018

ICT technology has been applied to various educational fields, but applying to educational test field is limited. Computer-based test (CBT) can overcome temporal and spatial constraints of conventional paper-based test, but is vulnerable to fraud by test parties. In this paper, we propose real-time monitoring and process management methods to enhance the security of CBT. In the proposed methods, the test screens of students are periodically captured and transferred to the professor screen to enable real-time monitoring, and the possible processes used for cheating can be blocked before testing. In order to monitor the screen of many students in real time, effective compression of the captured original image is important. We applied three-step compression methods: initial image compression, resolution reduction, and re-compression. Through this, the original image of about 6MB was converted into the storage image of about 3.8KB. We use the process extraction and management functions of Windows API to block the processes that may be used for cheating. The CBT system of this paper with the new security enhancement methods shows the superiority through comparison of the security related functions with the existing CBT systems.

• Journal of the Optical Society of Korea
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• v.4 no.1
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• pp.19-22
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• 2000

A new image encoding and identification scheme is proposed for security verification by us-ing a CGH(computer generated hologram), random phase mask, and a correlation technique. The encrypted image, which is attached to the security product, is made by multiplying a QP- CGH(quadratic phase CGI) with a random phase function. The random phase function plays a key role when the encrypted image is decrypted. The encrypted image can be optically recovered by a 2-f imaging system and automatically verified for personal identification by a 4-f correlation system. Simulation results show the proposed method can be used for both the reconstruction of an original image and the recognition of an encrypted image.

• Current Optics and Photonics
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• v.2 no.4
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• pp.315-323
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• 2018

A new multiple-image encryption scheme that is based on a compressive ghost imaging concept along with a Fourier transform sampling principle has been proposed. This further improves the security of the scheme. The scheme adopts a Fourier transform to sample the original multiple-image information respectively, utilizing the centrosymmetric conjugation property of the spatial spectrum of the images to obtain each Fourier coefficient in the most abundant spatial frequency band. Based on this sampling principle, the multiple images to be encrypted are grouped into a combined image, and then the compressive ghost imaging algorithm is used to improve the security, which reduces the amount of information transmission and improves the information transmission rate. Due to the presence of the compressive sensing algorithm, the scheme improves the accuracy of image reconstruction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.2
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• pp.457-476
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• 2011

This paper presents an improved XOR-based Data Hiding Scheme (XDHS) to hide a halftone image in more than two halftone stego images. The hamming weight and hamming distance is a very important parameter affecting the quality of a halftone image. For this reason, we proposed a method that involves minimizing the hamming weights and hamming distances between the stego image and cover image in $2{\times}2$-pixel grids. Moreover, our XDHS adopts a block-wise operation to improve the quality of a halftone image and stego images. Furthermore, our scheme improves security by using a block-wise operation with A-patterns and B-patterns. Our XDHS method achieves a high quality with good security compared to the prior arts. An experiment verified the superiority of our XDHS compared with previous methods.

• Convergence Security Journal
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• v.22 no.4
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• pp.129-133
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• 2022

Deep learning frameworks are still evolving, and there are various frameworks. Typical deep learning frameworks include TensorFlow, PyTorch, and Keras. The Deepram framework utilizes optimization models in image classification through image learning. In this paper, we use the TensorFlow and PyTorch frameworks, which are most widely used in the deep learning image recognition field, to proceed with image learning, and compare and analyze the results derived in this process to know the optimized framework. was made.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.756-766
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• 2023

To effectively track the illegal use of digital images and maintain the security of digital image communication on the Internet, this paper proposes a reversible multipurpose image watermarking algorithm based on a deep residual network (ResNet) and perceptual hashing (also called MWR). The algorithm first combines perceptual image hashing to generate a digital fingerprint that depends on the user's identity information and image characteristics. Then it embeds the removable visible watermark and digital fingerprint in two different regions of the orthogonal separation of the image. The embedding strength of the digital fingerprint is computed using ResNet. Because of the embedding of the removable visible watermark, the conflict between the copyright notice and the user's browsing is balanced. Moreover, image authentication and traitor tracking are realized through digital fingerprint insertion. The experiments show that the scheme has good visual transparency and watermark visibility. The use of chaotic mapping in the visible watermark insertion process enhances the security of the multipurpose watermark scheme, and unauthorized users without correct keys cannot effectively remove the visible watermark.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.450-453
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• 2002

In this paper a Phase-based virtual image encryption and decryption techniques based on a joint transform correlator (JTC) are proposed. In this method, an encrypted image is obtained by multiplying a phase-encoded virtual image that contains no information from the decrypted image with a random phase. Even if this encryption process converts a virtual image into a white-noise-like image, the unauthorized users can permit a counterfeiting of the encrypted image by analyzing the random phase mask using some phase-contrast technique. However, they cannot reconstruct the required image because the virtual image protects the original image from counterfeiting and unauthorized access. The proposed encryption technique does not suffer from strong auto-correlation terms appearing in the output plane. In addition, the reconstructed data can be directly transmitted to a digital system for real-time processing. Based on computer simulations, the proposed encryption technique and decoding system were demonstrated as adequate for optical security applications.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.252-259
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• 2022

Artificial intelligence is used in fusion with image processing techniques using cameras. Image processing technology is a technology that processes objects in an image received from a camera in real time, and is used in various fields such as security monitoring and medical image analysis. If such image processing reduces the accuracy of recognition, providing incorrect information to medical image analysis, security monitoring, etc. may cause serious problems. Therefore, this paper uses a mixture of YOLOv4-tiny model and image processing algorithm and uses the COCO dataset for learning. The image processing algorithm performs five image processing methods such as normalization, Gaussian distribution, Otsu algorithm, equalization, and gradient operation. For RGB images, three image processing methods are performed: equalization, Gaussian blur, and gamma correction proceed. Among the nine algorithms applied in this paper, the Equalization and Gaussian Blur model showed the highest object detection accuracy of 96%, and the gamma correction (RGB environment) model showed the highest object detection rate of 89% outdoors (daytime). The image binarization model showed the highest object detection rate at 89% outdoors (night).