• Journal of Korea Multimedia Society
• /
• v.14 no.11
• /
• pp.1365-1372
• /
• 2011

This paper proposes a cognitive contrast enhancement algorithm based on the non-linear masking to advance low cognitive contrast in dark regions of images. In order to improve brightness in dark regions of an image, we propose a new contrast enhancement algorithm based on the non-linear masking using regional adaptive parameters of an image. For performance evaluation of the proposed method, chromaticity and saturation comparison as a quantitative assessment and z-score comparison as a qualitative assessment were executed between test images and their simulated images by SSR, MSR, a conventional non-linear masking and the proposed method, respectively. As a result, the proposed method showed low chromaticity and saturation difference and improved cognitive contrast for the three methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2007.10a
• /
• pp.985-988
• /
• 2007

This paper presents techniques to make image enhancement using unsharp masking. It is the technique to make image enhancement by automatically find the three parameters that makes hard to use the unsharp mask technique. To optimize the three parameters(Threshold, Amount, Radius), at first classify the pixels in the image to three groups, and then according to the groups, apply the unsharp mask to the image differently. We experimented and analyzed the rate of image enhancement by comparing images which is enhanced by human and which is enhanced by proposed technique.

• ETRI Journal
• /
• v.33 no.2
• /
• pp.267-274
• /
• 2011

This paper proposes an efficient masking method for the block cipher SEED that is standardized in Korea. The nonlinear parts of SEED consist of two S-boxes and modular additions. However, the masked version of these nonlinear parts requires excessive RAM usage and a large number of operations. Protecting SEED by the general masking method requires 512 bytes of RAM corresponding to masked S-boxes and a large number of operations corresponding to the masked addition. This paper proposes a new-style masked S-box which can reduce the amount of operations of the masking addition process as well as the RAM usage. The proposed masked SEED, equipped with the new-style masked S-box, reduces the RAM requirements to 288 bytes, and it also reduces the processing time by 38% compared with the masked SEED using the general masked S-box. The proposed method also applies to other block ciphers with the same nonlinear operations.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.4
• /
• pp.709-718
• /
• 2019

A lightweight block cipher CHAM designed for suitability in resource-constrained environment has reasonable security level and high computational performance. Since this cipher may contain intrinsic weakness on side channel attack, it should adopt a countermeasure such as masking method. In this paper, we implement the masked CHAM cipher on 32-bit microprosessor Cortex-M3 platform to resist against side channel attack and analyze their computational performance. Based on the shortcoming of having many round functions, we apply reduced masking method to the implementation of CHAM cipher. As a result, we show that the CHAM-128/128 algorithm applied reduced masking technique requires additional operations about four times.

• Journal of Information Processing Systems
• /
• v.19 no.6
• /
• pp.767-777
• /
• 2023

In modern society, user privacy is emerging as an important issue as closed-circuit television (CCTV) systems increase rapidly in various public and private spaces. If CCTV cameras monitor sensitive areas or personal spaces, they can infringe on personal privacy. Someone's behavior patterns, sensitive information, residence, etc. can be exposed, and if the image data collected from CCTV is not properly protected, there can be a risk of data leakage by hackers or illegal accessors. This paper presents an innovative approach to "machine learning based reversible chaotic masking method for user privacy protection in CCTV environment." The proposed method was developed to protect an individual's identity within CCTV images while maintaining the usefulness of the data for surveillance and analysis purposes. This method utilizes a two-step process for user privacy. First, machine learning models are trained to accurately detect and locate human subjects within the CCTV frame. This model is designed to identify individuals accurately and robustly by leveraging state-of-the-art object detection techniques. When an individual is detected, reversible chaos masking technology is applied. This masking technique uses chaos maps to create complex patterns to hide individual facial features and identifiable characteristics. Above all, the generated mask can be reversibly applied and removed, allowing authorized users to access the original unmasking image.

• Proceedings of the Acoustical Society of Korea Conference
• /
• spring
• /
• pp.335-336
• /
• 2000

A psychoacoustic model based noise shaping method is proposed, where noise's presence with a host signal will not be perceptually noticeable. The derivation of imperceptible noise levels from the masking thresholds of the signal involves a deconvolution associated with the spreading function in the psychoacoustic model, which results in an ill-conditioned problem. In this paper, the problem is formulated as a constrained optimization, and it is demonstrated that the solution provides noise shaping where the noise excitation level conforms to the masking thresholds of the signal.

• Proceedings of the IEEK Conference
• /
• 1998.06a
• /
• pp.599-602
• /
• 1998

In this paper, a speech enhancement method based on phonemic properties and masking effect is propsoed. It is a modified type of spectral subtraction wherein the spectral sharpening process is exploited in unvoiced state considering the phonemic properties. The masking threshold is used to remove the residual noise. The proposed spectral subtraction shows similar performance as that of the classical spectral subtraction method in view of the SNR. But by the prposed scheme, the unvoiced sound region is shown to exhibit relatively less signal distortion in the enhanced speech.

• Communications for Statistical Applications and Methods
• /
• v.16 no.2
• /
• pp.309-315
• /
• 2009

We extended the results of Roy and Guria (2008) to multiple deletions in logistic regression models. Since single deletions may not exactly detect outliers or influential observations due to swamping effects and masking effects, it needs multiple deletions. We developed conditional deletion diagnostics which are designed to overcome problems of masking effects. We derived the closed forms for several statistics in logistic regression models. They give useful diagnostics on the statistics.

• Proceedings of the Korean Information Science Society Conference
• /
• 2001.04a
• /
• pp.82-84
• /
• 2001

Real-Time Linux는 기존의 Linux에 실시간 기능을 추가한 것으로서, 태스크 스케줄링 방법은 우선순위 기반의 스케줄링 방법을 사용한다. 그러나, 태스크의 개수가 많아지면 가장 높은 우선순위의 태스크를 찾는데 걸리는 시간이 태스크 개수에 비례해서 많이 걸린다. 이러한 이유로 태스크의 개수가 제한적일 수밖에 없다. 본 논문에서는 우선순위별로 서로 다른 목록을 유지하고, Bit Masking 기법을 사용함으로써 가장 높은 우선순위 태스크를 선택하는데 걸리는 시간을 상수시간으로 줄이고 각 태스크들의 시그널을 처리하는 부분을 좀더 효율적으로 처리하도록 함으로써 Real-Time Linux의 실시간 스케줄링 기능을 개선하였다.

• The Journal of the Acoustical Society of Korea
• /
• v.18 no.3E
• /
• pp.3-8
• /
• 1999

In speech recognition for real-world applications, the performance degradation due to the mismatch introduced between training and testing environments should be overcome. In this paper, to reduce this mismatch, we provide a hybrid method of spectral subtraction and residual noise masking. We also employ multiple model approach to obtain improved robustness over various noise environments. In this approach, multiple model sets are made according to several noise masking levels and then a model set appropriate for the estimated noise level is selected automatically in recognition phase. According to speaker independent isolated word recognition experiments in car noise environments, the proposed method using model sets with only two masking levels reduced average word error rate by 60% in comparison with spectral subtraction method.