• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.491-501
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• 2019

In side-channel analysis, which exploit physical leakage from a cryptographic device, deep learning based attack has been significantly interested in recent years. However, most of the state-of-the-art methods have been focused on classifying side-channel information in a profiled scenario where attackers can obtain label of training data. In this paper, we propose a new method based on deep learning to improve non-profiling side-channel attack such as Differential Power Analysis and Correlation Power Analysis. The proposed method is a signal preprocessing technique that reduces the noise in a trace by modifying Auto-Encoder framework to the context of side-channel analysis. Previous work on Denoising Auto-Encoder was trained through randomly added noise by an attacker. In this paper, the proposed model trains Auto-Encoder through the noise from real data using the noise-reduced-label. Also, the proposed method permits to perform non-profiled attack by training only a single neural network. We validate the performance of the noise reduction of the proposed method on real traces collected from ChipWhisperer board. We demonstrate that the proposed method outperforms classic preprocessing methods such as Principal Component Analysis and Linear Discriminant Analysis.

• The KIPS Transactions:PartB
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• v.10B no.4
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• pp.459-464
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• 2003

This paper proposes the use of a wavelet based image transform algorithm in human iris recognition method and the effectiveness of this technique will be determined in preprocessing of extracting Iris image from the user´s eye obtained by imaging device such as CCD Camera or due to torsional rotation of the eye, and it also resolves the problem caused by invariant under translations and dilations due to tilt of the head. This technique values through the proposed translation-invariant wavelet transform algorithm rather than the conventional wavelet transform method. Therefore we extracted the best-matching iris feature values and compared the stored feature codes with the incoming data to identify the user. As result of our experimentation, this technique demonstrate the significant advantage over verification when it compares with other general types of wavelet algorithm in the measure of FAR & FRR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1466-1488
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• 2022

Recently, the importance and necessity of artificial intelligence (AI), especially machine learning, has been emphasized. In fact, studies are actively underway to solve complex and challenging problems through the use of AI systems, such as intelligent CCTVs, intelligent AI security systems, and AI surgical robots. Information security that involves analysis and response to security vulnerabilities of software is no exception to this and is recognized as one of the fields wherein significant results are expected when AI is applied. This is because the frequency of malware incidents is gradually increasing, and the available security technologies are limited with regard to the use of software security experts or source code analysis tools. We conducted a study on MalDC, a technique that converts malware into images using machine learning, MalDC showed good performance and was able to analyze and classify different types of malware. MalDC applies a preprocessing step to minimize the noise generated in the image conversion process and employs an image augmentation technique to reinforce the insufficient dataset, thus improving the accuracy of the malware classification. To verify the feasibility of our method, we tested the malware classification technique used by MalDC on a dataset provided by Microsoft and malware data collected by the Korea Internet & Security Agency (KISA). Consequently, an accuracy of 97% was achieved.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.1
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• pp.81-90
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• 2010

This paper presents H.264 coding schemes for multi-view video using the concept of layered depth image(LDI) representation and efficient compression technique for LDI. After converting those data to the proposed representation, we encode color, depth, and auxiliary data representing the hierarchical structure, respectively, Two kinds of preprocessing approaches are proposed for multiple color and depth components. In order to compress auxiliary data, we have employed a near lossless coding method. Finally, we have reconstructed the original viewpoints successfully from the decoded approach that is useful for dealing with multiple color and depth data simultaneously.

• Korean Journal of Remote Sensing
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• v.30 no.3
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• pp.351-366
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• 2014

The aim of this study was to develop software for beach litter detection that includes a Graphical User Interface (GUI) and uses images taken by a micro-unmanned aerial vehicle. Videos were taken over Doomo pebble beach, Sogye pebble beach, and Heungnam sand beach on the northeast coast of Geojedo (Geoje Island), Korea. Still images of actual beach litter were obtained from the videos. The image processing involved preprocessing, morphological image processing, and image recognition. Comparison with still images showing beach litter demonstrated that the software could generally detect litter larger than 50 cm in size such as Styrofoam buoys and circular fish traps (excluding small pixel-size ropes). Combining the proposed method with the conventional surveying approach is expected to enhance the accuracy of beach litter detection. The new technique will also aid in predicting the amount of beach litter generated along coastlines, which is currently difficult to monitor.

• Smart Structures and Systems
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• v.10 no.4_5
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• pp.331-351
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• 2012

In this study an output-only system identification technique for civil structures under ambient vibrations is carried out, mainly focused on using the Stochastic Subspace Identification (SSI) based algorithms. A newly developed signal processing technique, called Singular Spectrum Analysis (SSA), capable to smooth a noisy signal, is adopted for preprocessing the measurement data. An SSA-based SSI algorithm with the aim of finding accurate and true modal parameters is developed through stabilization diagram which is constructed by plotting the identified system poles with increasing the size of data matrix. First, comparative study between different approaches, with and without using SSA to pre-process the data, on determining the model order and selecting the true system poles is examined in this study through numerical simulation. Finally, application of the proposed system identification task to the real large scale structure: Canton Tower, a benchmark problem for structural health monitoring of high-rise slender structures, using SSA-based SSI algorithm is carried out to extract the dynamic characteristics of the tower from output-only measurements.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.19 no.2
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• pp.162-168
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• 2006

Recently, diagnosis techniques have been investigated to detect a Partial Discharge associated with a dielectric material defect in a high voltage electrical apparatus, However, the properties of detection technique of Partial Discharge aren't completely understood because the physical process of Partial Discharge. Therefore, this paper analyzes the process on surface discharge of polymer insulator using wavelet transform. Wavelet transform provides a direct quantitative measure of spectral content in the time~frequency domain. As it is important to develop a non-contact method for detecting the kaolin contamination degree, this research analyzes the electromagnetic waves emitted from Partial Discharge using wavelet transform. This result experimentally shows the process of Partial Discharge as a two-dimensional distribution in the time-frequency domain. Feature extraction parameter namely, maximum and average of wavelet coefficients values, wavelet coefficients value at the point of $95\%$ in a histogram and number of maximum wavelet coefficient have used electromagnetic wave signals as input signals in the preprocessing process of neural networks in order to identify kaolin contamination rates. As result, root sum square error was produced by the test with a learning of neural networks obtained 0.00828.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.

• Journal of Korea Multimedia Society
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• v.6 no.5
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• pp.797-804
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• 2003

Scene change detection algorithm, which is very important preprocessing technique for video indexing and retrieval and determines the performance of video database system, is being studied widely. In this paper, we propose a more effective abrupt scene change detection, which is robust to large motion, sudden change of light and successive abrupt shot transitions rapidly. And we also propose a new gradual scene change detection algorithm, which can detect dissolve, and fade in/out precisely. Furthermore, we also propose a representative frame extraction algorithm which performs content-based video summary by novel DCT DC image buffering technique and accumulative histogram intersection measure (AHIM).

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.592-595
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• 2009

In this paper, we propose a face detection technique for still pictures which sequentially uses a skin-color model and a support vector machine (SVM). SVM is a learning algorithm for solving the classification problem. Some studies on face detection have reported superior results of SVM over neural networks. The SVM method searches for a face in a picture while changing the size of the window. The detection accuracy and the processing time of SVM vary largely depending on the complexity of the background of the picture or the size of the face. Therefore, we apply a face candidate area detection method using a skin-color model as a preprocessing technique. We compared the method using SVM alone with that of the proposed method in respect to face detection accuracy and processing time. As a result, the proposed method showed improved processing time while maintaining a high recognition rate.