• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1491-1500
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• 2008

This paper proposes an intelligent surveillance system to recognize suspicious patterns of the human behavior by using the Hidden Markov Model. First, the method finds foot area of the human by motion detection algorithm from image sequence of the surveillance camera. Then, these foot locus form observation series of features to learn the HMM. The feature that is position of the human foot is changed to each code that corresponds to a specific label among 16 local partitions of image region. Therefore, specific moving patterns formed by the foot locus are the series of the label numbers. The Baum-Welch algorithm of the HMM learns each suspicious and specific pattern to classify the human behaviors. To recognize the inputted human behavior pattern in a test image, the probabilistic comparison between the learned pattern of the HMM and foot series to be tested decides the categorization of the test pattern. The experimental results show that the method can be applied to detect a suspicious person prowling in corridor.

• Journal of the Korea Computer Graphics Society
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• v.24 no.1
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• pp.27-33
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• 2018

This paper presents a new adaptive LSB steganography for high capacity in spatial color images. The number of least signi ficant bit (LSB) of each RGB component in a color image pixel, to replace with the data bits to be hidden, was determine d through analysis of the worst case peak signal noise ratio (PSNR). In addition, the combination of the number of bits is determined adaptively according to image content. That is, 70% of the data to be hidden is proposed to be replaced with 3 bit LSB of two components, 2 bit LSB of the rest component, and 30% be replaced with 4 bit LSB of each RGB compon ent. To find edge areas in an image, delta sorting in local area is also suggested. Using the proposed method, the data cap acity is 9.2 bits per pixel (bpp). The average PSNR value of the tested images with concealed data of up to 60Kbyte was 43.9 db and also natural histograms were generated.

• Structural Engineering and Mechanics
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• v.77 no.1
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• pp.47-56
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• 2021

This paper deals with damage detection using a Gapped Smoothing Method (GSM) combined with deep learning. Convolutional Neural Network (CNN) is a model of deep learning. CNN has an input layer, an output layer, and a number of hidden layers that consist of convolutional layers. The input layer is a tensor with shape (number of images) × (image width) × (image height) × (image depth). An activation function is applied each time to this tensor passing through a hidden layer and the last layer is the fully connected layer. After the fully connected layer, the output layer, which is the final layer, is predicted by CNN. In this paper, a complete machine learning system is introduced. The training data was taken from a Finite Element (FE) model. The input images are the contour plots of curvature gapped smooth damage index. A free-free beam is used as a case study. In the first step, the FE model of the beam was used to generate data. The collected data were then divided into two parts, i.e. 70% for training and 30% for validation. In the second step, the proposed CNN was trained using training data and then validated using available data. Furthermore, a vibration experiment on steel damaged beam in free-free support condition was carried out in the laboratory to test the method. A total number of 15 accelerometers were set up to measure the mode shapes and calculate the curvature gapped smooth of the damaged beam. Two scenarios were introduced with different severities of the damage. The results showed that the trained CNN was successful in detecting the location as well as the severity of the damage in the experimental damaged beam.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.1
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• pp.45-50
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• 2023

Roblox, the world's No. 1 metaverse platform, has more than 3 billion subscription accounts and more than 150 millionmonthly active users (MAU). Despite such high interest in metaverse, existing studies on analyzing the risk of cyberattacks and security in the metaverse environment is insufficient. Therefore, in this paper, we propose a new steganography-basedcovert communication method in Roblox. In our proposed method, a secret message is hidden into an image by using a function provided in the Roblox Experience environment and then the image is automatically stored in the RobloxExperience participants' devices (PC or Smartphone) so that a malicious software can extract the hidden message fromthe image. By our experiments in the Roblox metaverse environment, we validated our proposed method works and thus want to inform our proposed method can be used in various cyberattacks and crimes such as the spread of secret commands, the establishment of a steganography botnet, and the mass distribution of malicious malware in metaverse platforms.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.835-838
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• 2003

Computer vision and natural-language dialogue play an important role in friendly human-machine interfaces for service robots. In this paper we describe an integrated face detection and face recognition system for a welfare robot, which has also been combined with the robot's speech interface. Our approach to face detection is to combine neural network (NN) and genetic algorithm (GA): ANN serves as a face filter while GA is used to search the image efficiently. When the face is detected, embedded Hidden Markov Model (EMM) is used to determine its identity. A real-time system has been created by combining the face detection and recognition techniques. When motivated by the speaker's voice commands, it takes an image from the camera, finds the face inside the image and recognizes it. Experiments on an indoor environment with complex backgrounds showed that a recognition rate of more than 88% can be achieved.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.222-225
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• 2000

Human frequently communicate non-linguistic information with gesture. So, we must develop efficient and fast gesture recognition algorithms for more natural human-computer interaction. However, it is difficult to recognize gesture automatically because human's body is three dimensional object with very complex structure. In this paper, we suggest a method which is able to detect key frames and frame changes, and to classify image sequence into some gesture groups. Gesture is classifiable according to moving part of body. First, we detect some frames that motion areas are changed abruptly and save those frames as key frames, and then use the frames to classify sequences. We symbolize each image of classified sequence using Principal Component Analysis(PCA) and clustering algorithm since it is better to use fewer components for representation of gestures. Symbols are used as the input symbols for the Hidden Markov Model(HMM) and recognized as a gesture with probability calculation.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.3
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• pp.29-36
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• 2015

Steganography is the technique of hiding the existence of a secret message that is communicated hiddenly. Generally, the main objectives of this paper is to develop newer and more sophisticated steganographic techniques based on perceptual transparency, robustness and capacity of the hidden data. This paper analyzes the advantages and disadvantages of image steganography techniques and proposes an effective method. As a result, the images steganography technique based on good ELSB and DCT which applies the rearranged key is secure and effective.

• Journal of Broadcast Engineering
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• v.7 no.2
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• pp.192-201
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• 2002

This paper develops a novel robust information hiding technique that uses channel codes derived from the error-correcting coder. The message encoded by the cover encoder is hidden in DCT transform domain of the cover image. The method exploits the sensitivity of human eyes to adaptively embed a visually recognizable message in an image without affecting the perceptual quality of the underlying cover image. Experimental results show that the proposed data hiding technique is robust to cropping operations, lossy JPEG compression, noise interference and secure against known stego attacks. The performance of the proposed scheme with turbo coder is superior to that without turbo coder.

• Convergence Security Journal
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• v.10 no.2
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• pp.19-26
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• 2010

In this study, we try to make up for the vulnerability in steganography that it is easily revealed the hidden logo image in cover image by bit-plane extraction. For this, we apply some methods, the permutation which shift the scattered pieces of logo image to one side, bit-plane dispersion insertion method and pack-type compressor.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.221-224
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• 2001

In this paper we propose a shift-invariant uHMT estimation for wavelet-based image denoising. The proposed estimation have just nine meta-parameter (independent of the size of the image and the number of wavelet scales) and requires no kinds of training. Also it solve visual artifacts resulted in the lack of shift-invariance in the DWT. The experimental results show that the proposed estimation is more effective than the other wavelet-based denoising by 0.5-ldB (PSNR) and allows an Ο(nlog n) in terms of performance speed.