• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4285-4299
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• 2019

Adversarial attack is a technique that causes a malfunction of classification models by adding noise that cannot be distinguished by humans, which poses a threat to a deep learning model. In this paper, we propose an efficient method to detect adversarial images using Gaussian process regression. Existing deep learning-based adversarial detection methods require numerous adversarial images for their training. The proposed method overcomes this problem by performing classification based on the statistical features of adversarial images and clean images that are extracted by Gaussian process regression with a small number of images. This technique can determine whether the input image is an adversarial image by applying Gaussian process regression based on the intermediate output value of the classification model. Experimental results show that the proposed method achieves higher detection performance than the other deep learning-based adversarial detection methods for powerful attacks. In particular, the Gaussian process regression-based detector shows better detection performance than the baseline models for most attacks in the case with fewer adversarial examples.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.8
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• pp.1136-1145
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• 1993

The channel capacities of various lading channels are calculated and compared with that of Gaussian noise channel to find out the decrements of channel capacity according to each fading environment. As a result, it is confirmed that the channel capacities in Rician and m-distribution fading channels approach to that of Gaussian noise channel as direct-to-indirect power ratio in Rician fading channel and fading index m in m-distribution fading channel increases respectively. And the difference between two channel capacities of Gaussian noise channel and each fading channel which is dependent on carrier-to-noise power ratio (CNR) is found. Also the improvement of channel capacity of Rayleigh fading channel by introducing two-branch diversities is obtained. For diversity reception, predetection maximal-ratio and postdetection selective combining techniques are adopted. The results show that the improvement of channel capacity by predetection maximal-ratio combining diversity is superior to the postdetection selective combining diversitiy regardless of correlation coefficient between two diversity branches in Raylelgh fading channel. The best improvement is achieved when two branches are noncorrelative in both two diversify techniques and as correlation coefficient of two diversity branches is smaller, the improvement of channel capacity is greater.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.4
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• pp.649-659
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• 1994

The channel capacity of fadingchannel becomes less than that of Gaussian noise channel. Moreover, the channel capacity of system with modulated signal is expected to be much less than that of system with nonmodulated signal. It is very difficult to evaluate the capacity in fading channel with modulated signal. The channel capacity for M-ary PSK signal has already been derived in Gaussian noise channel but it is very complicate form composed of complex integral formula. In this paper, we have derived the simple form of channel capacity with M-ary PSK signal by using the average symbol error rate. And we also evaluate the capacity in Rician and m-distribution fading channels as well as Gaussian noise channel. We have verified that the channel capacity obtained are almost the same as in other literature. As a results, we show that the channel capacity of M-art PSK signal increases to converge to the number of bits per symbol (log M) as signal to-noise power ratio increases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1687-1693
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• 2015

Recently, demand for digital image processing devices increases rapidly, more clear images have been required. But, in the process of digital image acquisition, processing and transmission, image degradation occurs due to various external reasons and researches about noise reduction are on the rise. Therefore, this study suggested the algorithm to process AWGN(additive white Gaussian noise) by separately processing as three levels according to the pixel distribution in the mask in order to remove AWGN(additive white Gaussian noise) which is added in the image. Regarding the processed results by applying Barbara images which were damaged by AWGN(σ = 15), suggested algorithm showed the improvement by 2.87[dB], 2.95[dB], 2.88[dB], 1.52[dB], 1.49[dB], 1.58[dB] and 1.25[dB] respectively compared with the existing MF(5 × 5), A-TMF(5 × 5), AWMF(5 × 5), MF(3 × 3), A-TMF(3 × 3), AWMF(3 × 3), GF(5 × 5).

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.273-285
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• 2007

Most stereo matching methods use intensity values in small image patches to measure the correspondence between two points. If the noisy pixels are used in computing the corresponding point, the matching performance becomes low. For this reason, the noise plays a critical role in determining the matching performance. In this paper, we propose a method for combining intensity and edge filters robust to the noise in order to improve the performance of stereo matching using high resolution satellite imagery. We used intensity filters such as Mean, Median, Midpoint and Gaussian filter and edge filters such as Gradient, Roberts, Prewitt, Sobel and Laplacian filter. To evaluate the performance of intensity and edge filters, experiments were carried out on both synthetic images and satellite images with uniform or gaussian noise. Then each filter was ranked based on its performance. Among the intensity and edge filters, Median and Sobel filter showed best performance while Midpoint and Laplacian filter showed worst result. We used Ikonos satellite stereo imagery in the experiments and the matching method using Median and Sobel filter showed better matching results than other filter combinations.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.398-401
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• 1997

The EMG signal of spinal cord injured patient is very feeble because that the information from central nervous system is not sufficiently transmitted to molter neuron or muscle fiber. Therefore the observer can not observe contraction and relaxation movement of muscle from the raw EMG signal. In this paper, we propose the muscle contraction and relaxation pattern analysis method of spinal cord injured patient whose EMG signal is composed of the sum of motor unit action potential train with additive white Gaussian noise and impulsive noise. From the EMG model, we denoise impulsive noise using median filter which is a kind of nonlinear filter and the output of median filter is transformed to wavelet transform domain for denoising additive white Gaussian noise using threshold level removal technique. As a result, we can obtain the clear contraction and relaxation pattern.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.899-906
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• 2005

In this paper, the optimum maximum-likelihood rake receiver for impulsive, fading channel is derived, and a sub-optimun rake receiver with reduced complexity is obtained for practical purposes. Numerical results show that, when the noise is impulsive, the rake receivers designed for impulsive noise offer performance improvement over those optimized for Gaussian environment. It is also observed that, as the number of fingers increases, the performance of the rake receivers designed for impulsive environment improves while the rake receiver optimized for Gaussian environment experiences performance degradation in impulsive environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.1064-1070
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• 2012

Nowadays, image processing has been applied in a variety of fields. In order to preserve the high quality of visual the degradation phenomenon for images should be removed. Noise is one of the representative elements cause of the degradation phenomenon and AWGN(additive white Gaussian noise) always damages images. In this paper, an mixed filter algorithm, which is based on parallel denoising method, is proposed to suppress AWGN. This algorithm parallels the spatial domain wiener filter and the wavelet domain thresholding method which thresholding function is selected based on scale level. The proposed modified thresholding function which considers the dependency between parent and child coefficient performs well on suppressing noise.

• Journal of information and communication convergence engineering
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• v.9 no.1
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• pp.59-63
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• 2011

Conventional noise reduction filtering schemes realize limited improvements of the peak signal-to-noise ratio (PSNR) in the low-level noisy images. The flatness degree and the edge information are effectively used to estimate the noise volume. We propose a noise estimator for reducing noise in the AWGN (additive white gaussian noise) corrupted images using three intermediate image maps (FGM(flatness gray map), FIM(flatness index map), NEM(noise estimate map)). The proposed noise estimator is fed into the conventional noise reduction filters as a pre-processor. The performance of noise reduction is tested in the various AWGN corrupted images.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.455-458
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• 2008

Former times Information Technology generally has only depended on text or sound, while nowadays information is being moved through a variety of image media. Cell phone, TV and computer have been major elements of modem society as mediators using image signal. Therefore, image signal processing also has been treated importantly and done actively. The processing has been developed in many fields of digital image processing technologies as image data compression, recognition, restoration, etc. Noises are inevitably generated by using the signals during the processing, and typical types of the noise are Impulse(salt & pepper) and AWGN(Addiction White Gaussian Noise). To reduce the noise, various kinds of filters have been developed, and according to each noise, it is being used different filter each. However, the noise is not generated by one signal but by a complex. In this paper, I suggested an image filter to remove the complex noise, and compared with existing filters' methods for verification.