• Title/Summary/Keyword: masked data

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Masked data 분석

  • 백재욱
    • Proceedings of the Korean Reliability Society Conference
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    • 2000.04a
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    • pp.131-131
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    • 2000
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Masked Face Recognition via a Combined SIFT and DLBP Features Trained in CNN Model

  • Aljarallah, Nahla Fahad;Uliyan, Diaa Mohammed
    • International Journal of Computer Science & Network Security
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    • v.22 no.6
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    • pp.319-331
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    • 2022
  • The latest global COVID-19 pandemic has made the use of facial masks an important aspect of our lives. People are advised to cover their faces in public spaces to discourage illness from spreading. Using these face masks posed a significant concern about the exactness of the face identification method used to search and unlock telephones at the school/office. Many companies have already built the requisite data in-house to incorporate such a scheme, using face recognition as an authentication. Unfortunately, veiled faces hinder the detection and acknowledgment of these facial identity schemes and seek to invalidate the internal data collection. Biometric systems that use the face as authentication cause problems with detection or recognition (face or persons). In this research, a novel model has been developed to detect and recognize faces and persons for authentication using scale invariant features (SIFT) for the whole segmented face with an efficient local binary texture features (DLBP) in region of eyes in the masked face. The Fuzzy C means is utilized to segment the image. These mixed features are trained significantly in a convolution neural network (CNN) model. The main advantage of this model is that can detect and recognizing faces by assigning weights to the selected features aimed to grant or provoke permissions with high accuracy.

Improved Side-Channel Attack on DES with the First Four Rounds Masked

  • Kim, Jong-Sung;Hong, Seok-Hie;Han, Dong-Guk;Lee, Sang-Jin
    • ETRI Journal
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    • v.31 no.5
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    • pp.625-627
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    • 2009
  • This letter describes an improved side-channel attack on DES with the first four rounds masked. Our improvement is based on truncated differentials and power traces which provide knowledge of Hamming weights for the intermediate data computed during the enciphering of plaintexts. Our results support the claim that masking several outer rounds rather than all rounds is not sufficient for the ciphers to be resistant to side-channel attacks.

Reliability Modeling and Analysis for a Unit with Multiple Causes of Failure (다수의 고장 원인을 갖는 기기의 신뢰성 모형화 및 분석)

  • Baek, Sang-Yeop;Lim, Tae-Jin;Lie, Chang-Hoon
    • Journal of Korean Institute of Industrial Engineers
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    • v.21 no.4
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    • pp.609-628
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    • 1995
  • This paper presents a reliability model and a data-analytic procedure for a repairable unit subject to failures due to multiple non-identifiable causes. We regard a failure cause as a state and assume the life distribution for each cause to be exponential. Then we represent the dependency among the causes by a Markov switching model(MSM) and estimate the transition probabilities and failure rates by maximum likelihood(ML) method. The failure data are incomplete due to masked causes of failures. We propose a specific version of EM(expectation and maximization) algorithm for finding maximum likelihood estimator(MLE) under this situation. We also develop statistical procedures for determining the number of significant states and for testing independency between state transitions. Our model requires only the successive failure times of a unit to perform the statistical analysis. It works well even when the causes of failures are fully masked, which overcomes the major deficiency of competing risk models. It does not require the assumption of stationarity or independency which is essential in mixture models. The stationary probabilities of states can be easily calculated from the transition probabilities estimated in our model, so it covers mixture models in general. The results of simulations show the consistency of estimation and accuracy gradually increasing according to the difference of failure rates and the frequency of transitions among the states.

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SITM Attacks on GIFT-128: Application to NIST Lightweight Cryptography Finalist GIFT-COFB (GIFT-128에 대한 SITM 공격: NIST 경량암호 최종 후보 GIFT-COFB 적용 방안 연구)

  • Park, Jonghyun;Kim, Hangi;Kim, Jongsung
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.32 no.4
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    • pp.607-615
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    • 2022
  • The SITM (See-In-The-Middle) proposed in CHES 2020 is a methodology for side-channel assisted differential cryptanalysis. This technique analyzes the power traces of unmasked middle rounds in partial masked SPN block cipher implementation, and performs differential analysis with the side channel information. Blockcipher GIFT is a lightweight blockcipher proposed in CHES 2017, designed to correct the well-known weaknesses of block cipher PRESENT and provide the efficient implementation. In this paper, we propose SITM attacks on partial masked implementation of GIFT-128. This attack targets 4-round and 6-round masked implementation of GIFT-128 and time/data complexity is 214.01 /214.01, 216 /216. In this paper, we compare the masterkey recovery logic available in SITM attacks, establishing a criterion for selecting more efficient logic depending on the situation. Finally, We introduce how to apply the this attack to GIFT-COFB, one of the finalist candidates in NIST lightweight cryptography standardization process.

Masked language modeling-based Korean Data Augmentation Techniques Using Label Correction (정답 레이블을 고려한 마스킹 언어모델 기반 한국어 데이터 증강 방법론)

  • Myunghoon Kang;Jungseob Lee;Seungjun Lee;Hyeonseok Moon;Chanjun Park;Yuna Hur;Heuiseok Lim
    • Annual Conference on Human and Language Technology
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    • 2022.10a
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    • pp.485-490
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    • 2022
  • 데이터 증강기법은 추가적인 데이터 구축 혹은 수집 행위 없이 원본 데이터셋의 양과 다양성을 증가시키는 방법이다. 데이터 증강기법은 규칙 기반부터 모델 기반 방법으로 발전하였으며, 최근에는 Masked Language Modeling (MLM)을 응용한 모델 기반 데이터 증강 연구가 활발히 진행되고 있다. 그러나 기존의 MLM 기반 데이터 증강 방법은 임의 대체 방식을 사용하여 문장 내 의미 변화 가능성이 큰 주요 토큰을 고려하지 않았으며 증강에 따른 레이블 교정방법이 제시되지 않았다는 한계점이 존재한다. 이러한 문제를 완화하기 위하여, 본 논문은 레이블을 고려할 수 있는 Re-labeling module이 추가된 MLM 기반 한국어 데이터 증강 방법론을 제안한다. 제안하는 방법론을 KLUE-STS 및 KLUE-NLI 평가셋을 활용하여 검증한 결과, 기존 MLM 방법론 대비 약 89% 적은 데이터 양으로도 baseline 성능을 1.22% 향상시킬 수 있었다. 또한 Gate Function 적용 여부 실험으로 제안 방법 Re-labeling module의 구조적 타당성을 검증하였다.

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Gaussian Filtering Effects on Brain Tissue-masked Susceptibility Weighted Images to Optimize Voxel-based Analysis (화소 분석의 최적화를 위해 자화감수성 영상에 나타난 뇌조직의 가우시안 필터 효과 연구)

  • Hwang, Eo-Jin;Kim, Min-Ji;Jahng, Geon-Ho
    • Investigative Magnetic Resonance Imaging
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    • v.17 no.4
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    • pp.275-285
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    • 2013
  • Purpose : The objective of this study was to investigate effects of different smoothing kernel sizes on brain tissue-masked susceptibility-weighted images (SWI) obtained from normal elderly subjects using voxel-based analyses. Materials and Methods: Twenty healthy human volunteers (mean $age{\pm}SD$ = $67.8{\pm}6.09$ years, 14 females and 6 males) were studied after informed consent. A fully first-order flow-compensated three-dimensional (3D) gradient-echo sequence ran to obtain axial magnitude and phase images to generate SWI data. In addition, sagittal 3D T1-weighted images were acquired with the magnetization-prepared rapid acquisition of gradient-echo sequence for brain tissue segmentation and imaging registration. Both paramagnetically (PSWI) and diamagnetically (NSWI) phase-masked SWI data were obtained with masking out non-brain tissues. Finally, both tissue-masked PSWI and NSWI data were smoothed using different smoothing kernel sizes that were isotropic 0, 2, 4, and 8 mm Gaussian kernels. The voxel-based comparisons were performed using a paired t-test between PSWI and NSWI for each smoothing kernel size. Results: The significance of comparisons increased with increasing smoothing kernel sizes. Signals from NSWI were greater than those from PSWI. The smoothing kernel size of four was optimal to use voxel-based comparisons. The bilaterally different areas were found on multiple brain regions. Conclusion: The paramagnetic (positive) phase mask led to reduce signals from high susceptibility areas. To minimize partial volume effects and contributions of large vessels, the voxel-based analysis on SWI with masked non-brain components should be utilized.

A Implementation of a Line Scan System for Continuous Manufacturing Process (연속 생산 라인에서의 고속 라인 스캔 검사 시스템 구축)

  • Lim, Cheung;Lee, Jong-Min;Kim, Yong-Deak
    • Proceedings of the IEEK Conference
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    • 2002.06e
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    • pp.211-214
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    • 2002
  • Under a continuous manufacturing process, two dimension inspection system causes problems as blurring effect and low resolution and requires position calibration between frames. One dimension inspection system is, therefore, being researched as a substitution. In this paper, we implement mechanism of switching memory and processing data for reasonable one dimension inspection system. Redundant weft image and noise was suggested to be reduced by new method using modified morphological process and masked erosion process. From resulting image, line data and possible error information were obtained and constructed as a structure. Finally, error detecting algorithm was performed with this data structure. Processing time of error detecting was 0.625ms per line in applied system and experiment showed 94.7% of error detecting ability. This method is 20% faster in speed and 2.7% higher in error detecting ability comparing with the present method.

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Shilling Attacks Against Memory-Based Privacy-Preserving Recommendation Algorithms

  • Gunes, Ihsan;Bilge, Alper;Polat, Huseyin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.7 no.5
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    • pp.1272-1290
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    • 2013
  • Privacy-preserving collaborative filtering schemes are becoming increasingly popular because they handle the information overload problem without jeopardizing privacy. However, they may be susceptible to shilling or profile injection attacks, similar to traditional recommender systems without privacy measures. Although researchers have proposed various privacy-preserving recommendation frameworks, it has not been shown that such schemes are resistant to profile injection attacks. In this study, we investigate two memory-based privacy-preserving collaborative filtering algorithms and analyze their robustness against several shilling attack strategies. We first design and apply formerly proposed shilling attack techniques to privately collected databases. We analyze their effectiveness in manipulating predicted recommendations by experimenting on real data-based benchmark data sets. We show that it is still possible to manipulate the predictions significantly on databases consisting of masked preferences even though a few of the attack strategies are not effective in a privacy-preserving environment.

Dynamic Runoff of Non-point Sources Pollutants from Agricultural Areas (농촌지역에서 유출시간에 따른 비점오염물질의 유출평가)

  • Yi, Qitao;Hur, Chinhyu;Kim, Youngchul
    • Journal of Korean Society on Water Environment
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    • v.24 no.6
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    • pp.773-783
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    • 2008
  • In this paper, data collected from 22 different rural watersheds during stormflow conditions were analyzed. Those watersheds consisted of forest and cultivated land. EMC data analysis indicates that as agricultural land use increases, EMC values of TSS, COD and TN clearly tends to increase, but TP does not show a significant increase. Pattern of the pollutographs mostly has a similarity in hydrograph shape except nitrogen which inherently shows a variability and complication. The fraction of soluble reactive-P to TP increases as cultivated land use increases while mobile-nitrogen portion was higher in the runoff from forested watersheds than agricultural areas. During stormflow, pollutograph of the nitrogen was determined mainly by change in Particle-TKN as other pollutants but its effect is thought to be masked by decrease of dissolved form of nitrogen due to the dilution.