• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.413-414
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• 1998

This paper presents a real-time Spatial Decision Support System(SDSS) for security camera image monitoring. Other SDSSs are not real-time systems, i.e., they show the images that are already transformed into data format such as virtual reality. In our system, the image is broadcasted in real-time since the purpose of the security camera needs to do it in real-time. With these real-time images, other systems do not add up anything more; the screen just shows the images from the camera. However in our system, we created a motion detection system so that the supervisor(Judge) of a sec.urity monitoring system does not have to pay attention to it constantly. In other words, we created a judge advising system for the supervisor of the security monitoring system. Most of small objects do not need the supervisor's attention since they could be birds, cats, dogs, etc. if they show up in the screen image. In this new system the system only report the unusual change to the supervisor by calculating the motion and size of objects in the screen. Thus the supervisor can be liberated from the 24-hour concentration duty; instead he/she can be only alerted when the real security threat such as a big moving object like an human intruder appears. Thus this system can be called a real-time Spatial DSS. The utility of this system is proved mathematically by using the concept of entropy. In other words, big objects like human intruders increase the entropy of the screen images significantly therefore the supervisor must be alerted. Thus by proving its utility of the system theoretically, we can claim that our new real-time SDSS is superior to others which do not use our technique.hnique.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.2
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• pp.311-318
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• 2014

As modern computer graphics technology has been developed, it is hard to discriminate computer graphics from photographic images with the naked eye. Advances in graphics technology has brought a lot of convenience to human, it has side effects such as image forgery, malicious edit and fraudulent means. In order to cope with such problems, studies of various algorithms using a feature that represents a characteristic of an image has been processed. In this paper, we verify directly the existing algorithm, and provide new features based a noise that represents the characteristics of the computer graphics well. And this paper introduces the method of using SVM(Support Vector Machine) with features proposed in previous research to improve the discrimination accuracy.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.65-74
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• 2021

Digital image processing and retrieving have increasingly become very popular on the Internet and getting more attention from various multimedia fields. That results in additional privacy requirements placed on efficient image matching techniques in various applications. Hence, several searching methods have been developed when confidential images are used in image matching between pairs of security agencies, most of these search methods either limited by its cost or precision. This study proposes a secure and efficient method that preserves image privacy and confidentially between two communicating parties. To retrieve an image, feature vector is extracted from the given query image, and then the similarities with the stored database images features vector are calculated to retrieve the matched images based on an indexing scheme and matching strategy. We used a secure content-based image retrieval features detector algorithm called Speeded-Up Robust Features (SURF) algorithm over public cloud to extract the features and the Honey Encryption algorithm. The purpose of using the encrypted images database is to provide an accurate searching through encrypted documents without needing decryption. Progress in this area helps protect the privacy of sensitive data stored on the cloud. The experimental results (conducted on a well-known image-set) show that the performance of the proposed methodology achieved a noticeable enhancement level in terms of precision, recall, F-Measure, and execution time.

• Current Optics and Photonics
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• v.5 no.6
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• pp.686-698
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• 2021

A camouflaged encryption scheme based on Hadamard matrix and ghost imaging is proposed. In the process of the encryption, an orthogonal matrix is used as the projection pattern of ghost imaging to improve the definition of the reconstructed images. The ciphertext of the secret image is constrained to the camouflaged image. The key of the camouflaged image is obtained by the method of sparse decomposition by principal component orthogonal basis and the constrained ciphertext. The information of the secret image is hidden into the information of the camouflaged image which can improve the security of the system. In the decryption process, the authorized user needs to extract the key of the secret image according to the obtained random sequences. The real encrypted information can be obtained. Otherwise, the obtained image is the camouflaged image. In order to verify the feasibility, security and robustness of the encryption system, binary images and gray-scale images are selected for simulation and experiment. The results show that the proposed encryption system simplifies the calculation process, and also improves the definition of the reconstructed images and the security of the encryption system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5990-6008
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• 2019

Recognizing license plate from its traffic camera captured images is one of the most important aspects in many traffic management systems. Despite many sophisticated license plate recognition related algorithms available online, license plate recognition is still a hot research issue because license plates in each country all round the world lack of uniform format and their camera captured images are often affected by multiple adverse factors, such as low resolution, poor illumination effects, installation problem etc. A novel method is proposed in this paper to solve the inclination problem of license plates in their camera captured images through four parts: Firstly, special edge pixels of license plate are chosen to represent main information of license plates. Secondly, least square methods are used to compute the inclined angle of license plates. Then, coordinate rotation methods are used to rotate the license plate. At last, bilinear interpolation methods are used to improve the performance of license plate rotation. Several experimental results demonstrated that our proposed method can solve the inclination problem about license plate in visual aspect and can improve the recognition rate when used as the image preprocessing method.

• ETRI Journal
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• v.34 no.3
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• pp.410-420
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• 2012

A series of reversible watermarking technologies have been proposed to increase embedding capacity and the quality of the watermarked image simultaneously. The major skills include difference expansion, histogram shifting, and optimizing embedding order. In this paper, an accurate predictor is proposed to enhance the difference expansion. An efficient sorter is also suggested to find a more desirable embedding order. The payload is differently distributed into two sub-images, split like a chessboard pattern, for better watermarked image quality. Simulation results of the accurate prediction and sorter based on the payload balancing method yield generally better performance over previous methods. The gap is wide, in particular, in low payload for natural images. The peak signal-to-noise ratio improvement is around 2 dB in low payload ranges.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.93-96
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• 2024

The segmentation, detection, and extraction of infected tumour area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated many algorithm methods are available in medical imaging amongst them the Threshold technique brain tumour segmentation process gives an accurate result than other methods for MR images. The proposed method compare with the K-means clustering methods, it gives a cluster of images. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, process time and similarity of the segmented part. The experimental results achieved more accuracy, less running time and high resolution.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1447-1456
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• 2019

As artificial intelligence technology is actively used in image processing, it is possible to generate high-quality fake images based on deep learning. Fake images generated using GAN(Generative Adversarial Network), one of unsupervised learning algorithms, have reached levels that are hard to discriminate from the naked eye. Detecting these fake images is required as they can be abused for crimes such as illegal content production, identity fraud and defamation. In this paper, we develop a deep-learning model based on CNN(Convolutional Neural Network) for the detection of StyleGAN fake images. StyleGAN is one of GAN algorithms and has an excellent performance in generating face images. We experiment with 48 number of experimental scenarios developed by combining parameters of the proposed model. We train and test each scenario with 300,000 number of real and fake face images in order to present a model parameter that improves performance in the detection of fake faces.

• Convergence Security Journal
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• v.16 no.1
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• pp.81-87
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• 2016

In this paper, we propose a forgery/falsification detection technique of web site using the images. The proposed system captures images of the web site when a user accesses to the forgery/falsification web site that has the financial information deodorizing purpose. The captured images are compared with those of normal web site images to detect forgery/falsification. The proposed system calculates similarity factor of normal site image with captured one to detect whether the site is normal or not. If it is determined as normal, analysis procedure is finished. But if it is determined as abnormal, a message informs the user to prevent additional financial information spill and further accidents from the forgery web site.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.179-184
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• 2021

In our study we performed a sentiments analysis from the images. For this purpose, we used 153 images that contain: people, animals, buildings, landscapes, cakes and objects that we divided into two categories: images that suggesting a positive or a negative emotion. In order to classify the images using the two categories, we created two models. The SAI-G model was created with Google's AutoML Vision service. The SAI-C model was created on the Clarifai platform. The data were labeled in a preprocessing stage, and for the SAI-C model we created the concepts POSITIVE (POZITIV) AND NEGATIVE (NEGATIV). In order to evaluate the performances of the two models, we used a series of evaluation metrics such as: Precision, Recall, ROC (Receiver Operating Characteristic) curve, Precision-Recall curve, Confusion Matrix, Accuracy Score and Average precision. Precision and Recall for the SAI-G model is 0.875, at a confidence threshold of 0.5, while for the SAI-C model we obtained much lower scores, respectively Precision = 0.727 and Recall = 0.571 for the same confidence threshold. The results indicate a lower classification performance of the SAI-C model compared to the SAI-G model. The exception is the value of Precision for the POSITIVE concept, which is 1,000.