• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.345-346
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• 2024

디지털 기술과 정보통신 기술이 발전하면서 디지털 콘텐츠의 불법복제 및 유통으로 인한 저작권 침해 피해가 증가하고 있다. 저작권 침해 문제를 예방하기 위해 다양한 디지털 워터마킹 기술이 제안되었지만, 디지털 이미지 워터마킹은 이미지에 기하학적 변형을 가하면 삽입된 워터마크가 훼손되어 탐지가 어렵다는 문제가 있다. 본 연구에서는 왜곡 공격에 강인한 상관관계 측정 기반 워터마킹 기법을 제안한다. 제안한 방식은 교차 상관 기법을 이용해 이미지와 워터마크의 상관관계를 계산하고 임계값과 비교하여 공간 영역에서의 비가시성 워터마크의 존재 여부를 검증할 수 있는 디지털 워터마킹 방법이다. 실험 결과에 따르면 표준편차 120의 가우시안 노이즈 공격을 가해도 원본 워터마크와 0.1 이상의 상관관계를 보이며, 종래의 방식보다 높은 탐지 성능을 나타냈다.

• Journal of IKEEE
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• v.9 no.2 s.17
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• pp.87-100
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• 2005

In this paper, a design of an integral augmented optimal variable structure system(IOVSS) is presented for the prescribed output control of uncertain SISO systems under persistent disturbances. This algorithm aims at removing the problems of the reaching phase by incorporating advanced optimal control theory. By means of an integral sliding surface, the reaching phase is completely removed, and the integral sliding surface can be defined from a given initial state to origin without any reaching phase. The ideal sliding dynamics of the integral sliding surface is obtained in the form of the state equation and is designed in an optimal sense by targeting the design of the integral sliding surface and equivalent control input. The corresponding control input is selected in order to generate the sliding mode on the predetermined integral sliding surface. As a result, the whole sliding output from a given initial state to origin is completely guaranteed against persistent disturbances. Moreover the prediction/predetermination of output is enabled, which helps in improving the performance over previously implemented VSS's. Through an illustrative example, the usefulness of the algorithm is shown.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.115-120
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• 2005

In this paper, we propose a new robust content-based musical genre classification algorithm using multi-feature clustering(MFC) method. In contrast to previous works, this paper focuses on two practical issues of the system dependency problem on different input query patterns(or portions) and input query lengths which causes serious uncertainty of the system performance. In order to solve these problems, a new approach called multi-feature clustering(MFC) based on k-means clustering is proposed. To verify the performance of the proposed method, several excerpts with variable duration were extracted from every other position in a queried music file. Effectiveness of the system with MFC and without MFC is compared in terms of the classification accuracy. It is demonstrated that the use of MFC significantly improves the system stability of musical genre classification performance with higher accuracy rate.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.655-660
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• 2005

This paper studied the feature parameters less affected by the emotional variation for the development of the robust speech recognition technologies. For this purpose, the effect of emotional variation on the speech recognition system and robust feature parameters of speech recognition system were studied using speech database containing various emotions. In this study, LPC cepstral coefficient, met-cepstral coefficient, root-cepstral coefficient, PLP coefficient, RASTA met-cepstral coefficient were used as a feature parameters. And CMS and SBR method were used as a signal bias removal techniques. Experimental results showed that the HMM based speaker independent word recognizer using RASTA met-cepstral coefficient :md its derivatives and CMS as a signal bias removal showed the best performance of $7.05\%$ word error rate. This corresponds to about a $52\%$ word error reduction as compare to the performance of baseline system using met - cepstral coefficient.

• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.33-43
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• 2007

In this proper, we proposes a robust watermarking against the lossy compression in the Hadamard domain. The Hadamard matrix consists of only 1 or -1 and can be computed veru fast. The Hadamrd transform has the inverse transform therefore it is able to be applied into the watermarking technology. In embedding process, we select 10 coefficients from intermediate frequency domain and create two watermark patterns. In extraction process, we use the watermark patterns and compare them to detect the watermark information. When we use the standard image ($512{\times}512$) and binary watermark image ($64{\times}64$), the results of these examines are PSNR for $38{\sim}42dB$ and BER for $3.9{\sim}12.5%$. The JPEG QF between 30 and100, naked human eyes can detect to watermark image easily. The experimental results show that performance of Hadamard domain is better than those of DCT, FFT, and DWT.

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.70-78
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• 2007

The performance of face recognition is greatly affected by the illumination effect because intra-person variation under different lighting conditions can be much bigger than the inter-person variation. In this paper, we propose an illumination robust face recognition by separating identity factor and illumination factor using the symmetric bilinear models. The translation procedure in the bilinear model requires a repetitive computation of matrix inverse operation to reach the identity and illumination factors. Sometimes, this computation may result in a nonconvergent case when the observation has an noisy information. To alleviate this situation, we suggest a ridge regressive bilinear model that combines the ridge regression into the bilinear model. This combination provides some advantages: it makes the bilinear model more stable by shrinking the range of identity and illumination factors appropriately, and it improves the recognition performance by reducing the insignificant factors effectively. Experiment results show that the ridge regressive bilinear model outperforms significantly other existing methods such as the eigenface, quotient image, and the bilinear model in terms of the recognition rate under a variety of illuminations.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.2
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• pp.325-330
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• 2017

Face recognition technology has been studied for decades and is being used in various areas such as security, entertainment, and mobile services. The main problem with face recognition technology is that the recognition rate is significantly reduced depending on the environmental factors such as brightness, illumination angle, and image rotation. Therefore, in this paper, we propose a robust face recognition against lighting variation using CNN which has been recently re-evaluated with the development of computer hardware and algorithms capable of processing a large amount of computation. For performance verification, PCA, LBP, and DCT algorithms were compared with the conventional face recognition algorithms. The recognition was improved by 9.82%, 11.6%, and 4.54%, respectively. Also, the recognition improvement of 5.24% was recorded in the comparison of the face recognition research result using the existing neural network, and the final recognition rate was 99.25%.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.670-677
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• 2019

As the presence of background noise in acoustic signal degrades the performance of speech or acoustic event recognition, it is still challenging to extract noise-robust acoustic features from noisy signal. In this paper, we propose a combined structure of Wasserstein Generative Adversarial Network (WGAN) and MultiTask AutoEncoder (MTAE) as deep learning architecture that integrates the strength of MTAE and WGAN respectively such that it estimates not only noise but also speech features from noisy acoustic source. The proposed MTAE-WGAN structure is used to estimate speech signal and the residual noise by employing a gradient penalty and a weight initialization method for Leaky Rectified Linear Unit (LReLU) and Parametric ReLU (PReLU). The proposed MTAE-WGAN structure with the adopted gradient penalty loss function enhances the speech features and subsequently achieve substantial Phoneme Error Rate (PER) improvements over the stand-alone Deep Denoising Autoencoder (DDAE), MTAE, Redundant Convolutional Encoder-Decoder (R-CED) and Recurrent MTAE (RMTAE) models for robust speech recognition.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.116-122
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• 2010

This paper proposes a method for classifying targets robust to geometric transformations of targets such as rotation, scale change, translation, and pose change. Targets which have rotation, scale change, and shift is firstly classified based on CM(Confidence Map) which is generated by similarity, scale ratio, and range of orientation for SIFT(Scale-Invariant Feature Transform) feature vectors. On the other hand, DB(DataBase) which is acquired in various angles is used to deal with pose variation of targets. Range of the angle is determined by comparing and analyzing the execution time and performance for sampling intervals. We experiment on various images which is geometrically changed to evaluate performance of proposed target classification method. Experimental results show that the proposed algorithm has a good classification performance.

• KIPS Transactions on Computer and Communication Systems
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• v.1 no.1
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• pp.47-54
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• 2012

Video contents in the mobile environment are processed with the low bit-rate relative to normal video contents due to the consideration of network traffic; hence, it is necessary to protect the copyright of the low bit-rate video contents. The algorithm for watermarking appropriate for the mobile environment should be developed because the performance of the mobile devices is much lower than that of personal computers. This paper suggested the invisible spread spectrum watermarking method to the low bit-rate video contents, considering the low performance of the mobile device in the M-Commerce environment; it also enables to track down illegal users of the video contents to protect the copyright. The robustness of the contents with watermark is expressed with the correlation of extraction algorithm from watermark removed or distorted contents. The results of our experiment showed that we could extract the innate frequencies of M-Sequence when we extracted M-Sequence after compressing the contents with watermark easily. Therefore, illegal users of the contents can be tracked down because watermark can be extracted from the low bit-rate video contents.