• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.466-469
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• 2009

This paper presents a modified QIM-JPEG2000 steganography which improves the previous JPEG2000 steganography using quantization index modulation (QIM). Post-embedding changes in file size and PSNR by the modified QIM-JPEG2000 are smaller than those by the previous QIM-JPEG2000. Steganalysis experiments to determine whether messages are embedded in given JPEG2000 images show that the modified QIM-JPEG2000 is more secure than the previous QIMJPEG2000.

• The Journal of the Acoustical Society of Korea
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• v.32 no.1
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• pp.71-78
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• 2013

This paper describes a multiple audio watermarking using Quantization Index Modulation (QIM) on frequency phase and magnitude response. Proposed embedding procedure is composed of two stage. At the first stage, the watermark is embedded on the frequency phase response using QIM. In the second stage, the watermark is embedded using adaptive QIM with the step-size that is adaptively determined using the maximum value of the frequency magnitude response of every frame. The watermark is extracted by calculating the Euclidean distance as the blind detection. The proposed method is robust against most of attacks of audio watermark benchmarking. For the Fourier attacks, the proposed method shows over 95% recovery rate.

• The Journal of the Acoustical Society of Korea
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• v.30 no.6
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• pp.303-307
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• 2011

This paper describes an audio watermarking using Quantization Index Modulation (QIM) on significant peaks in frequency domain. The audio signal is broken up into L samples length frames with non-overlapping and rectangular window. The zero-crossing rate of each frame is calculated for decision whether it is proper to be watermarked or not. If the frame is legitimate, frequency magnitude response is computed by discrete Fourier transform. For the QIM, we set the quantization step size based on maximum value of frequency magnitude response and select n significant peaks with w samples around them in frequency domain, totally $n{\times}(w+1)$ samples. Finally, watermark embedding is performed. Decoder extract watermarks based on Euclidean distance, that is a blind detection. The proposed method is robust against many attacks of watermark benchmark.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.2
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• pp.63-68
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• 2015

This paper proposes a robust multiple audio watermarking algorithm using improved QIM(quantization index modulation) with adaptive stepsize for different signal power and SVM(support vector machine) decoding model. The proposed algorithm embeds watermarks into both frequency magnitude response and frequency phase response using QIM. This multiple embedding method can achieve a complementary robustness. The SVM decoding model can improve detection rate when it is not sure whether the extracted data are the watermarks or not. To evaluate robustness, 11 attacks are employed. Consequently, the proposed algorithm outperforms previous multiple watermarking algorithm, which is identical to the proposed one but without SVM decoding model, in PSNR and BER. It is noticeable that the proposed algorithm achieves improvements of maximum PSNR 7dB and BER 10%.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1431-1437
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• 2011

This paper proposes a novel watermarking algorithm that protects digital copyrights and is robust to attacks. Watermarks are embedded in the subband including the significant part of the signal such as a pitch. Generally, the subband containing the pitch has the biggest energy. In order to find this subband, wavelet packet transform is used to decompose the subbands and their energy are calculated. The signal of the selected subbands is transformed in frequency domain using FFT. The watermarks are embedded using QIM for samples higher than a certain threshold. The blind detection uses the Euclidean distance. The proposed method shows less than 5% BER in the audio watermark benchmarking.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.1
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• pp.45-53
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• 2006

In this paper, we propose a method for enlarging quantization steps of a QIM watermarking scheme which determines the perceptual quality and robustness of the watermarked images. In general, increasing the quantization steps leads to good robustness but poor perceptual quality of watermarked images and vice versa. However, if we choose the quantization steps considering the expected quantization results as well as the original images, we can increase both robustness and perceptual quality of the watermarked images.

• Journal of Korea Multimedia Society
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• v.9 no.5
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• pp.649-657
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• 2006

Reinforcement Learning is a computational approach to learning whereby an agent take an action which maximize the total amount of reward it receives among possible actions within current state when interacting with a uncertain environment. Q-learning, one of the most active algorithm in Reinforcement Learning, is consist of rewards which is obtained when an agent take an action. But it has the problem with mapping real world to discrete states. When state spaces are very large, Q-learning suffers from time for learning. In constant, when the state space is reduced, many state spaces map to single state space. Because an agent only learns single action within many states, an agent takes an action monotonously. In this paper, to reduce time for learning and complement simple action, we propose the Q-learning using influence map(QIM). By using influence map and adjacent state space's learning result, an agent could choose proper action within uncertain state where an agent does not learn. When this paper compares simulation results of QIM and Q-learning, we show that QIM effects as same as Q-learning even thought QIM uses 4.6% of the Q-learning's state spaces. This is because QIM learns faster than Q-learning about 2.77 times and the state spaces which is needed to learn is reduced, so the occurred problem is complemented by the influence map.

• Journal of Power Electronics
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• v.10 no.6
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• pp.694-701
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• 2010

In this paper an effective direct torque control (DTC) and stator flux control is developed for a quasi six-phase induction motor (QIM) drive with sinusoidally distributed windings. Combining sliding-mode (SM) control and adaptive input-output feedback linearization, a nonlinear controller is designed in the stationary reference frame, which is capable of tracking control of the stator flux and torque independently. The motor controllers are designed in order to track a desired second order linear reference model in spite of motor resistances mismatching. The effectiveness and capability of the proposed method is shown by practical results obtained for a QIM supplied from a voltage source inverter (VSI).

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.476-484
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• 2018

The high capacity audio watermarking algorithms are facing a main challenge in satisfying the robustness against attacks especially on de-synchronization attacks. In this paper, a robust and a high capacity algorithm is proposed using segment selection, Stationary Wavelet Transform (SWT) and the Quantization Index Modulation (QIM) techniques along with new synchronization mechanism. The proposed algorithm provides enhanced trade-off between robustness, imperceptibility, and capacity. The achieved watermarking improves the reliability of the available watermarking methods and shows high robustness towards signal processing (manipulating) attacks especially the de-synchronization attacks such as cropping, jittering, and zero inserting attacks. For imperceptibility evaluation, high signal to noise ratio values of above 22 dB has been achieved. Also subjective test with volunteer listeners shows that the proposed method has high imperceptibility with Subjective Difference Grade (SDG) of 4.76. Meanwhile, high rational capacity up to 176.4 bps is also achieved.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.561-563
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• 2019

This paper proposes a watermarking method for copyright protection of images used in VR. The Embedding method is that finds the point through the SIFT feature points, inserts the watermark by using DWT and QIM on the surrounding area. The objective image to extract the embedded watermark is the projected image and its method finds the SIFT feature points and extracts watermark data from its surrounding areas after correction by using inverse process of matching and projection in the VR image production process. By comparing the NCC and BER between the extracted watermark and the inserted watermark, the watermark is determined by accumulating the watermark having a threshold value or more. This is confirmed by comparing with a conventional method.