Maximum Likelihood Decision Rule Research Articles

Template attacks exploiting static power and application to CMOS lightweight crypto‐hardware

SummaryA new class of template attacks aiming at recovering the secret key of a cryptographic core from measurements of its static power consumption is presented in this paper. These attacks exploit the dependence of the static current of Complementary metal–oxide–semiconductor Integrated Circuits on the input vector and the maximum likelihood decision rule as a statistical distinguisher. In the proposed Template Attacks Exploiting Static Power (TAESP), we take advantage of the temperature dependence of static currents in order to build a new multivariate approach able to extract relevant information from cryptographic devices. As a validation case study, we consider the PRESENT‐80 block cypher algorithm and its implementation on a 40 nm Complementary metal–oxide–semiconductor process. Monte Carlo and corner simulations at transistor level are used to show the effectiveness of the TAESP in the presence of die‐to‐die and intra‐die process variations. A real attack scenario is then built by adding Gaussian noise to current samples extracted from transistor‐level simulations. The univariate TAESP in which just one temperature is considered to build the templates is compared against the multivariate TAESP in which measurements at different controlled temperatures are exploited. This comparison shows that using just a few different temperatures to build multivariate templates allows to strongly increase the effectiveness of the attack. Copyright © 2016 John Wiley & Sons, Ltd.

Blind optimum detector for robust image watermarking in nonsubsampled shearlet Domain

In this paper, we propose a blind robust image watermarking approach in the nonsubsampled Shearlet domain using Bessel K Form (BKF) modeling. Nonsubsampled shearlet transform (NSST) is an effective multi-scale and multi-direction analysis method, it not only can exactly compute the shearlet coefficients based on a multiresolution analysis, but also can provide nearly optimal approximation for a piecewise smooth function. In order to achieve more robustness and imperceptibility, watermark information is embedded into the most significant NSST directional subband with the highest energy, by simply modifying the NSST coefficient amplitude according to the watermark data. By modeling the NSST coefficients with BKF probability density function, the distribution of watermarked noisy coefficients is analytically calculated. The tradeoff between the imperceptibility and robustness of watermark data is solved in a novel fashion. At the receiver, based on the Maximum Likelihood (ML) decision rule, a blind statistical watermark detector is proposed. Experimental results on test images demonstrate that the proposed approach can provide better imperceptibility and robustness against various attacks, such as additive white Gaussian noise, salt & pepper noise, median filtering, JPEG compression, rotation, and scaling, in comparison with the recently proposed techniques.

A new detector for contourlet domain multiplicative image watermarking using Bessel K form distribution

This paper proposes a novel multiplicative contourlet domain watermark detector. We use contourlet transform since this transform represents image edges sparsely and this makes it suitable for human visual system. Watermark detection can be formulated as a binary statistical decision problem, so, its performance is dependent on the accuracy of statistical modeling. Studying the statistical properties of contourlet coefficients, we demonstrate the high efficiency of Bessel K form (BKF) distribution to model these coefficients. Consequently, we design an optimal detector for multiplicative watermarking based on using the Maximum Likelihood (ML) decision rule and BKF distribution. Also, we derive its receiver operating characteristics analytically. Experimental results demonstrate the high efficiency of the proposed scheme under different types of attacks. Finally, we compare our proposed detector with other related detectors experimentally using Monte Carlo simulations and verify the performance improvement in utilizing the new strategy.

Radar speckle reduction and derived texture measures for land cover/use classification: a case study

This study examined the appropriateness of radar speckle reduction for deriving texture measures for land cover/use classifications. Radarsat-2 C-band quad-polarised data were obtained for Washington, DC, USA. Polarisation signatures were extracted for multiple image components, classified with a maximum-likelihood decision rule and thematic accuracies determined. Initial classifications using original and despeckled scenes showed despeckled radar to have better overall thematic accuracies. However, when variance texture measures were extracted for several window sizes from the original and despeckled imagery and classified, the accuracy for the radar data was decreased when despeckled prior to texture extraction. The highest classification accuracy obtained for the extracted variance texture measure from the original radar was 72%, which was reduced to 69% when this measure was extracted from a 5 × 5 despeckled image. These results suggest that it may be better to use despeckled radar as original data and extract texture measures from the original imagery.

An evaluation of Radarsat-2 individual and combined image dates for land use/cover mapping

Various land use/cover types exhibit seasonal characteristics which can be captured in remotely sensed imagery. This study examined how different seasons of Radarsat-2 data influence land use/cover classification accuracies for two study sites. Two dates of Radarsat-2 C-band quad-polarised images were obtained for Washington, DC, USA and Wad Madani, Sudan. Spectral signatures were extracted and used with a maximum likelihood decision rule for classification and thematic accuracies were then determined. Both despeckled radar and derived texture measures were examined. Thematic accuracies for the two despeckled image dates were similar with a difference of 3% for Washington and 6% for Sudan. Merging the despeckled images for both seasons increased overall accuracy by 2% for Washington and 9% for Sudan. Further combining the original radar for both seasons with derived texture measures increased overall accuracies by 9% for Washington and 16% for Sudan for final overall accuracy values of 73 and 82%.

Comparison and integration of spaceborne optical and radar data for mapping in Sudan

The purpose of this study was to determine how different procedures and data, such as multiple wavelengths of radar imagery and radar texture measures, independently and in combination with optical imagery influence land-cover/use classification accuracies for a study site in Sudan. Radarsat-2 C-band and phased array L-band synthetic aperture radar (PALSAR) L-band quad-polarized radar were registered with ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) optical data. Spectral signatures were obtained for multiple landscape features, classified using a maximum-likelihood decision rule, and thematic accuracies were obtained using separate validation data. There were surprising differences between the thematic accuracies of the two radar data sets, with Radarsat-2 only having a 51% accuracy and PALSAR 73%. In contrast, the optical ASTER overall accuracy was 81%. Combining the original radar and a variance texture measure increased the Radarsat-2 to 78% and PALSAR to 80%, whereas the two original radar bands together had an accuracy of 87%. Sensor fusion of optical and radar obtained an accuracy of 93%. Based on these results, the use of multiwavelength quad-polarized radar imagery combined or integrated with optical imagery has great potential in improving the accuracy of land-cover/use classifications. In tropical and high-latitude regions of the world, where persistent cloud cover hinders the use of optical satellite systems, land management programmes may find this research promising.

Mapping wetlands in the Hudson Highlands ecoregion with ALOS PALSAR: an effort to identify potential swamp forest habitat for golden-winged warblers

In New York State, the golden-winged warbler (GWW), a state species of special concern, has recently been found to nest in swamp forest habitat in Sterling Forest State Park in the Hudson Highlands ecoregion. These swamp forest breeding territories are often embedded in a mosaic of wetland cover types. An accurate map of wetlands in the Hudson Highlands would be a useful input to a GWW habitat suitability model and could help conservation managers better allocate limited resources towards GWW monitoring and habitat management. This study tests the effectiveness of single and dual-season dual-polarization L-band ALOS PALSAR data and National Wetlands Inventory ground reference data for classifying wetlands at high resolution in a mountainous landscape using a maximum likelihood decision rule. PALSAR imagery was chosen over Landsat for its ability to detect surface conditions beneath a vegetation canopy and for its higher spatial resolution, necessary for capturing land cover characteristics at a scale that would be meaningful for characterizing GWW territories. Dual-season dual-polarization imagery produced the highest overall accuracy (72 %, kappa = 0.5669) and the highest producer’s accuracy for forested wetlands (~58 %). Errors in classifying forested wetlands were dominated by misclassification to the Urban class, most likely due to the similarity of SAR double bounce trunk-ground and building-ground interactions. Emergent and scrub-shrub wetlands proved difficult to segregate and were classified with maximum accuracies of 49 and 14 %, respectively. These results are comparable to those of similar studies and could be improved with more current ground reference data. In addition, the classification captured the mosaic of wetland cover types present in GWW study sites and territories in Sterling Forest State Park indicating PALSAR imagery could be used for GWW habitat analyses.

Multiuser Detection For DS-CDMA Systems Using Honeybees Mating Optimization Algorithm

The future wireless mobile communication systems will be required to support high-speed transmission rate and high quality of service. Direct sequence code division multiple access (DS-CDMA) is an important scheme for high-rate wireless communication. The capacity of DS-CDMA can be impaired by two problems; near-far effect and multiple-access interference (MAI). The use of conventional-matched filter detector for multiple users in DS-CDMA fails to combat any of these problems. The performance degradation caused by MAI can be overcome using multiuser detection (MUD). The use of maximum likelihood (ML) sequence estimation detector provides excellent results, but involves high computational complexity. In this paper, we propose a new meta-heuristic approach for MUD using honeybees mating optimization (HBMO) algorithm to detect the user bits based on the ML decision rule for DS-CDMA systems in additive white-Gaussian noise and flat Rayleigh fading channels. In order to improve the solutions generated by the HBMO, a second meta-heuristic method simulated annealing is used. By computer simulations, the bit error rate performance and the complexity curves show that the proposed HBMO-SA MUD is capable of outperforming the other conventional detectors and genetic algorithm detector.

Comparison of Landsat image classification methods for detecting mangrove forests in Sundarbans

Remote-sensing images taken from the Landsat Enhanced Thematic Mapper Plus (ETM+) sensor with a spatial resolution of 30 m were applied for mapping and inventory of mangrove forest areas in Sundarbans, on both sides of the border between Bangladesh and India. Three different classification methods – unsupervised classification with k-means clustering, supervised classification using the maximum likelihood decision rule, and band-ratio supervised classification – were tested and compared in terms of the top of the atmosphere reflectance images. Spectral signature and principal component analyses were applied to select the appropriate band combinations prior to the band ratio–supervised classification. Our results show that the band ratio method is superior to the unsupervised or supervised classification methods considering the visual inspection, producer's and user's accuracy, as well as the overall accuracy of the all the classes in the image. The best discrimination of mangrove/nonmangrove boundary can be achieved when the combinations of B4/B2 (band 4/band 2), B5/B7, and B7/B4 are employed from the ETM+ bands.

Novel low-PAPR parallel FSOK transceiver design for MC-CDMA system over multipath fading channels

A low peak-to-average power ratio (PAPR) transceiver using a new parallel frequency-shift orthogonal keying (FSOK) technique is proposed for the multiuser uplink multi-carrier CDMA (MC-CDMA) system over multipath fading channels. By employing the frequency modulated and multiplexed FSOK techniques to combat the multiuser and parallel substream interferences, respectively, the system retains a low-PAPR transmitted signal and a low-complexity equalizer without any matrix inversion. At the basestation, a multiuser receiver is derived, which involves parallel FSOK despreading, demapping, and maximum likelihood decision rule to acquire M-ary modulation gain and frequency diversity gain. For higher link quality, a multiple input single output FSOK uplink system can flexibly be configured. Simulation results are included to demonstrate that the proposed system achieves the low-PAPR property, space-frequency diversity, and M-ary modulation gain. Compared to the existing MC-CDMA and SC-FDMA systems, the proposed system exhibits significant performance superiority.

Contourlet-Based Image Watermarking Using Optimum Detector in a Noisy Environment

In this paper, an improved multiplicative image watermarking system is presented. Since human visual system is less sensitive to the image edges, watermarking is applied in the contourlet domain, which represents image edges sparsely. In the presented scheme, watermark data is embedded in directional subband with the highest energy. By modeling the contourlet coefficients with General Gaussian Distribution (GGD), the distribution of watermarked noisy coefficients is analytically calculated. The tradeoff between the transparency and robustness of the watermark data is solved in a novel fashion. At the receiver, based on the Maximum Likelihood (ML) decision rule, an optimal detector by the aid of channel side information is proposed. In the next step, a blind extension of the suggested algorithm is presented using the patchwork idea. Experimental results confirm the superiority of the proposed method against common attacks, such as Additive White Gaussian Noise (AWGN), JPEG compression, and rotation attacks, in comparison with the recently proposed techniques.

Intelligent multi-user detection using an artificial immune system

Artificial immune systems (AIS) are a kind of new computational intelligence methods which draw inspiration from the human immune system. In this study, we introduce an AIS-based optimization algorithm, called clonal selection algorithm, to solve the multi-user detection problem in code-division multiple-access communications system based on the maximum-likelihood decision rule. Through proportional cloning, hypermutation, clonal selection and clonal death, the new method performs a greedy search which reproduces individuals and selects their improved maturated progenies after the affinity maturation process. Theoretical analysis indicates that the clonal selection algorithm is suitable for solving the multi-user detection problem. Computer simulations show that the proposed approach outperforms some other approaches including two genetic algorithm-based detectors and the matched filters detector, and has the ability to find the most likely combinations.

Land cover classification with an expert system approach using Landsat ETM imagery: a case study of Trabzon

The main objective of this study is to generate a knowledge base which is composed of user-defined variables and included raster imagery, vector coverage, spatial models, external programs, and simple scalars and to develop an expert classification using Landsat 7 (ETM+) imagery for land cover classification in a part of Trabzon city. Expert systems allow for the integration of remote-sensed data with other sources of geo-referenced information such as land use data, spatial texture, and digital elevation model to obtain greater classification accuracy. Logical decision rules are used with the various datasets to assign class values for each pixel. Expert system is very suitable for the work of image interpretation as a powerful means of information integration. Landsat ETM data acquired in the year 2000 were initially classified into seven classes for land cover using a maximum likelihood decision rule. An expert system was constructed to perform post-classification sorting of the initial land cover classification using additional spatial datasets such as land use data. The overall accuracy of expert classification was 95.80%. Individual class accuracy ranged from 75% to 100% for each class.

Estabilidade de Classificadores de Decisão em Árvore Binária para Dados Imagem em Alta Dimensão

This paper deals with the problem of classifying high-dimensional image data image data using a multiple stage classifier structured as a binary tree. The aim here consists in finding the optimal structure for the binary tree in the sense of achieving a stable accuracy. The advantage presented by a multiple stage classifier lies on the fact that only a sub-set of classes is considered at each stage, allowing a better selection of the features to be used at each node. The binary tree is a particular case of a tree structured classifier, on which only two classes are considered at each node. This peculiarity makes possible the direct use of statistical distances for feature reduction (selection or extraction). In this study the criterion used for feature reduction at each node consists in optimizing the Bhattacharyya distance separating both classes in the node. The optimization of Bhattacharyya distance was based on the covariance matrices. Once the final set of features is obtained at each particular node, the classification is performed using the Gaussian Maximum Likelihood decision rule. Tests were performed using high-dimensional image data collected by the sensor system AVIRIS covering a test area. The criteria to evaluate the performance of the classifiers are: the final accuracy yielded by the classifier, its stability, and the required computer time.

Classification of cries of infants with cleft-palate using parallel hidden Markov models

This paper addresses the problem of classification of infants with cleft palate. A hidden Markov model (HMM)-based cry classification algorithm is presented. A parallel HMM (PHMM) for coping with age masking, based on a maximum-likelihood decision rule, is introduced. The performance of the proposed algorithm under different model parameters and different feature sets is studied using a database of cries of infants with cleft palate (CLP). The proposed algorithm yields an average of 91% correct classification rate in a subject- and age-dependent experiment. In addition, it is shown that the PHMM significantly outperforms the HMM performance in classification of cries of CLP infants of different ages.

Three-dimensional color object visualization and recognition using multi-wavelength computational holography

In this paper, we address 3D object visualization and recognition with multi-wavelength digital holography. Color features of 3D objects are obtained by the multiple-wavelengths. Perfect superimposition technique generates reconstructed images of the same size. Statistical pattern recognition techniques: principal component analysis and mixture discriminant analysis analyze multi-spectral information in the reconstructed images. Class-conditional probability density functions are estimated during the training process. Maximum likelihood decision rule categorizes unlabeled images into one of trained-classes. It is shown that a small number of training images is sufficient for the color object classification.

European forest cover mapping with high resolution satellite data: The Carpathians case study

The aim of the study was to elaborate a methodology for forest mapping based on high resolution satellite data, relevant for reporting on forest cover and spatial pattern changes in Europe. The Carpathians were selected as a case study area and mapped using 24 Landsat scenes, processed independently with a supervised approach combining image segmentation, knowledge-based rules to extract a training set and the maximum likelihood decision rule. Validation was done with available very high resolution imagery. Overall accuracies per scene ranged from 93 to 96%. The labelling disagreement in overlapping areas of adjacent scenes was 6.8% on average.The proposed methodology is easily applicable to other regions in Europe. It allows an accurate forest–non-forest map to be obtained in a quick and cost effective manner, spatially more detailed than the currently available European data sets.

Applying class-based feature extraction approaches for supervised classification of hyperspectral imagery

Global band selection or feature extraction methods have been applied to hyperspectral image classification to overcome the "curse of dimension". We applied class-based feature extraction approaches and compressed the class data into different lower dimensional subspaces. Land cover classes in hyperspectral imagery could be roughly modelled as low-dimensional Gaussian clusters (i.e., "Gaussian pancakes") floating in sparse hyperspace. Each pixel was labelled accordingly based on conventional classifiers. We evaluated and compared the class-based version of principal components analysis (PCA), probabilistic principal components analysis (PPCA), and probabilistic factor analysis (PFA) algorithms to find the lower dimensional class subspaces in the training stage, projected each pixel, and then assigned the class label according to the maximum likelihood decision rule. Results from simulations and the classification of a compact airborne spectrographic imager 2 (CASI 2) hyperspectral dataset were presented. The proposed class-based PCA (CPCA) algorithm provided a reasonable trade-off between classification accuracy and computational efficiency for hyperspectral image classification. It proved more efficient and provided the highest classification kappa coefficient (0.946) among all band selection and feature extraction classifiers in our study. CPCA is recommended as a useful class-based feature extraction method for classification of hyperspectral imagery.

Mapping a specific class for priority habitats monitoring from satellite sensor data

The European Union's Habitats Directive aims to protect biodiversity through the conservation of habitats. If a habitat of interest corresponds to spectrally separable land cover class(es), then this activity can benefit from the production of accurate land cover maps from remotely sensed imagery. Traditionally, the image classification techniques used assume that the set of classes has been defined exhaustively, which requires all of the classes in the region to be included explicitly in the analysis. Often, however, interest focuses on just one or a small sub‐set of the classes occurring in the region that represent the habitat(s) of particular interest. Moreover, given that the size of a training set required for an image classification is typically taken to be a function of the number of classes and discriminating variables (e.g. wavebands) used in the classification, the satisfaction of the assumption of an exhaustively defined set of classes requires that much effort is directed wastefully on classes of little, if any, direct interest. Savings in training could be achieved by focusing on the class(es) of specific interest. A more appropriate approach for mapping a specific class may be to adopt a binary classification analysis that simply seeks to separate the class of interest from all others. In this way the analysis focuses on the class(es) of interest and a small training set may be used. An attractive means to achieve this is through the adoption of decision tree‐ and support vector machine‐based approaches to classification. This paper evaluates the accuracy with which a habitat of interest to the EU Habitats Directive, fen, can be mapped from Landsat ETM+ imagery of the Norfolk Broads using such classifiers as well as, for comparative purposes, a standard maximum likelihood decision rule implemented by a discriminant analysis. All analyses yielded accurate classifications, with a conventional approach based on a maximum likelihood allocation providing an overall classification accuracy of 88.4%. However, both the decision tree‐ and support vector machine‐based approaches provided classifications that were significantly more accurate than conventional maximum likelihood classification (p<0.05), with overall accuracies of 91.6 and 93.6%, respectively (table 3). The results highlight the ability to focus the analysis on the class of interest in a manner that is less wasteful of resources and effort and that yields a more accurate classification than the standard approach.

洪水による都市域の浸水区域特定へのレーダーリモートセンシングデータ（ラダルサット）の適用性について

洪水災害マネジメントにおいて詳細な洪水区域図は必須条件である．冠水し易い区域の特定に基づいて詳細な洪水災害の情報を得る手法を用いれば，損失を引き起こす洪水を最小限に抑えることができる．本研究では，レーダーリモートセンシング（ラダルサット）のデータを利用してこれを評価し，バングラデシュ国のダッカ市で1998年に発生した洪水に伴う浸水区域の分布を高い精度で抽出した．適切な洪水対策を講じるために有用と考えられる最大浸水区域を正確に特定するために，洪水期間の7画像と乾期の1画像を使用した．レーダー画像をデジタル処理してピクセルデータに変換し，微小ノイズを除去するためにフィルタリング処理を施した．最大尤度判定規則を用いて画像を浸水区域と非浸水区域に区分した．レーダーデータの区分の精度は，地表データを参照して評価した．洪水の多時点解析によると，浸水区域は1998年7月（浸水率34.65％）から増加し始め，主にモンスーン性の豪雨と河川水位の早い時期の上昇により8月25日にピークを迎えた（浸水率51.29％）ことが明らかとなった．市内東部では深刻な洪水被害が発生した一方で，西部では相対的に標高が高く洪水制御設備も完成していたことから被害はなかった．画像の区分の精度からは，本研究で用いたデータ画像は十分な結果を得られたことは明らかである．最も高い精度だったのは7月31日の画像で88.57％に達し，つづいて7月7日（82.86％），9月10日（81.90％）の順となった．また，すべての画像に対して算出したKappa係数は，8月の画像をのぞいて良い一致を得た．この結果，SARデータは，ダッカ市などの急成長している都市での実時間洪水モニタリングにおいて極めて重要であり，洪水災害の情報とマネジメントのために重要な情報を与えることが可能であることが明らかとなった．