Recognizing Substitution Steganography of Spatial Domain Based on the Characteristics of Pixels Correlation

2017 ◽  
Vol 9 (4) ◽  
pp. 48-61 ◽  
Author(s):  
Zhe Chen ◽  
Jicang Lu ◽  
Pengfei Yang ◽  
Xiangyang Luo
Keyword(s):  
Feature Extraction ◽  
Digital Image ◽  
Spatial Domain ◽  
Recognition Algorithm ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Image Steganalysis ◽  
Statistical Feature ◽  
Better Than

Steganographic algorithm recognition is currently a key issue in digital image steganalysis. For the typical substitution steganographic algorithm in spatial domain, we analyze the modification way and construct the feature extraction source based on the adjacent pixels correlation; extract the special statistical feature which could distinguish the substitution steganography from other types of steganographic algorithms. Finally, a substitution steganography recognition algorithm is presented and tested by experiments. The experimental results show that, the proposed algorithm could recognize the substitution steganography in spatial domain efficiently, and the detection accuracy is better than existing algorithms.

scholarly journals Privacy Leakage in Mobile Sensing: Your Unlock Passwords Can Be Leaked through Wireless Hotspot Functionality

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Jie Zhang ◽  
Xiaolong Zheng ◽  
Zhanyong Tang ◽  
Tianzhang Xing ◽  
Xiaojiang Chen ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Mobile Sensing ◽  
Experimental Results ◽  
Smart Devices ◽  
Detection Accuracy ◽  
Phase Information ◽  
Current Sensing ◽  
Privacy Leakage ◽  
Sensing Systems ◽  
Additional Hardware

Mobile sensing has become a new style of applications and most of the smart devices are equipped with varieties of sensors or functionalities to enhance sensing capabilities. Current sensing systems concentrate on how to enhance sensing capabilities; however, the sensors or functionalities may lead to the leakage of users’ privacy. In this paper, we present WiPass, a way to leverage the wireless hotspot functionality on the smart devices to snoop the unlock passwords/patterns without the support of additional hardware. The attacker can “see” your unlock passwords/patterns even one meter away. WiPass leverages the impacts of finger motions on the wireless signals during the unlocking period to analyze the passwords/patterns. To practically implement WiPass, we are facing the difficult feature extraction and complex unlock passwords matching, making the analysis of the finger motions challenging. To conquer the challenges, we use DCASW to extract feature and hierarchical DTW to do unlock passwords matching. Besides, the combination of amplitude and phase information is used to accurately recognize the passwords/patterns. We implement a prototype of WiPass and evaluate its performance under various environments. The experimental results show that WiPass achieves the detection accuracy of 85.6% and 74.7% for passwords/patterns detection in LOS and in NLOS scenarios, respectively.

Combining Block DCV and Support Vector Machine for Ear Recognition

2016 ◽  
Vol 8 (2) ◽  
pp. 36-44 ◽  
Author(s):  
Zhao Hailong ◽  
Yi Junyan
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Construction Method ◽  
Experimental Results ◽  
Support Vector ◽  
Recognition Method ◽  
Ear Recognition ◽  
Human Ear ◽  
Better Than

In recent years, automatic ear recognition has become a popular research. Effective feature extraction is one of the most important steps in Content-based ear image retrieval applications. In this paper, the authors proposed a new vectors construction method for ear retrieval based on Block Discriminative Common Vector. According to this method, the ear image is divided into 16 blocks firstly and the features are extracted by applying DCV to the sub-images. Furthermore, Support Vector Machine is used as classifier to make decision. The experimental results show that the proposed method performs better than classical PCA+LDA, so it is an effective human ear recognition method.

scholarly journals PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yanjuan Li ◽  
Zitong Zhang ◽  
Zhixia Teng ◽  
Xiaoyan Liu
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Prediction Model ◽  
Multilayer Perceptron ◽  
Type Ii Diabetes ◽  
Cross Validation ◽  
Experimental Results ◽  
Type Ii ◽  
Fold Cross Validation ◽  
Better Than

Amyloid is generally an aggregate of insoluble fibrin; its abnormal deposition is the pathogenic mechanism of various diseases, such as Alzheimer’s disease and type II diabetes. Therefore, accurately identifying amyloid is necessary to understand its role in pathology. We proposed a machine learning-based prediction model called PredAmyl-MLP, which consists of the following three steps: feature extraction, feature selection, and classification. In the step of feature extraction, seven feature extraction algorithms and different combinations of them are investigated, and the combination of SVMProt-188D and tripeptide composition (TPC) is selected according to the experimental results. In the step of feature selection, maximum relevant maximum distance (MRMD) and binomial distribution (BD) are, respectively, used to remove the redundant or noise features, and the appropriate features are selected according to the experimental results. In the step of classification, we employed multilayer perceptron (MLP) to train the prediction model. The 10-fold cross-validation results show that the overall accuracy of PredAmyl-MLP reached 91.59%, and the performance was better than the existing methods.

A NEWLY MODIFIED ALGORITHM OF HOUGH TRANSFORM FOR LINE DETECTION

2005 ◽  
Vol 05 (04) ◽  
pp. 715-727
Author(s):  
QIANG WANG ◽  
HONGBO CHEN ◽  
XIAORONG XU ◽  
HAIYAN LIU
Keyword(s):  
Computational Complexity ◽  
Digital Image ◽  
Hough Transform ◽  
Experimental Results ◽  
Line Detection ◽  
Storage Requirement ◽  
Heavy Burden ◽  
Modified Algorithm ◽  
Better Than

The heavy burden of computational complexity and massive storage requirement is the drawback of the standard Hough transform (SHT). To overcome the weakness of SHT, many modified approaches, for example, the probabilistic Hough transform (PHT), have been presented. However, a very important fact, which is that a line has its own width in a real digital image and the width of the line is uniform, was ignored by all of these modified algorithms of Hough transform. This phenomenon influenced the result of line detection. In this paper a new modified algorithm of Hough transform for line detection is proposed. In our algorithm, the fact mentioned above is fully considered and a strip-shaped area corresponding to the accumulate cells of HT is proposed. Experimental results have shown that our approach is efficient and promising, and the effect of detection is far better than the popular modified approaches.

scholarly journals Adaptive Steganalysis Based on Selection Region and Combined Convolutional Neural Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Donghui Hu ◽  
Qiang Shen ◽  
Shengnan Zhou ◽  
Xueliang Liu ◽  
Yuqi Fan ◽  
...  
Keyword(s):  
Performance Improvement ◽  
Digital Image ◽  
Information Hiding ◽  
Digital Image Analysis ◽  
Hybrid Approach ◽  
Selection Method ◽  
Detection Accuracy ◽  
Adaptive Steganography ◽  
Image Steganalysis ◽  
Region Selection

Digital image steganalysis is the art of detecting the presence of information hiding in carrier images. When detecting recently developed adaptive image steganography methods, state-of-art steganalysis methods cannot achieve satisfactory detection accuracy, because the adaptive steganography methods can adaptively embed information into regions with rich textures via the guidance of distortion function and thus make the effective steganalysis features hard to be extracted. Inspired by the promising success which convolutional neural network (CNN) has achieved in the fields of digital image analysis, increasing researchers are devoted to designing CNN based steganalysis methods. But as for detecting adaptive steganography methods, the results achieved by CNN based methods are still far from expected. In this paper, we propose a hybrid approach by designing a region selection method and a new CNN framework. In order to make the CNN focus on the regions with complex textures, we design a region selection method by finding a region with the maximal sum of the embedding probabilities. To evolve more diverse and effective steganalysis features, we design a new CNN framework consisting of three separate subnets with independent structure and configuration parameters and then merge and split the three subnets repeatedly. Experimental results indicate that our approach can lead to performance improvement in detecting adaptive steganography.

A Novel Method for Grinding Wheel Setting Based on Acoustic Emissions

2016 ◽  
Vol 874 ◽  
pp. 79-84 ◽  
Author(s):  
Xiang Long Zhu ◽  
Zhen Hua Jiao ◽  
Ren Ke Kang ◽  
Zi Guang Wang ◽  
Hui Xu
Keyword(s):  
Acoustic Emission ◽  
Negative Impact ◽  
Acoustic Emissions ◽  
Experimental Results ◽  
Grinding Wheel ◽  
Detection Accuracy ◽  
Grinding Process ◽  
Novel Method ◽  
Ae Signals ◽  
Better Than

Wheel setting is difficult in a grinding process and may directly apply a negative impact on grinding accuracy and efficiency. This study presents a novel method for grinding wheel setting based on acoustic emissions. The method experimentally detects the acoustic emission (AE) signals that come from the touch-down of the grinding wheel with the workpiece. The experimental results show that the measured AE signals monotonically increase with grinding depth and can be used for detection of wheel setting in a grinding process with a detection accuracy better than 0.5μm.

scholarly journals Simple, Flexible Method to Extract Digital Image Features

2021 ◽  
Vol 38 (5) ◽  
pp. 1509-1514
Author(s):  
Mohammad S. Khrisat ◽  
Hatim Ghazi Zaini ◽  
Ziad A. Alqadi
Keyword(s):  
Feature Extraction ◽  
Digital Image ◽  
Extraction Process ◽  
Image Features ◽  
Features Extraction ◽  
Experimental Results ◽  
Speed Up ◽  
Classification Prediction

The process of digital image features extraction is very important and it is required in many applications such as classification, prediction and regression. The extracted features for each image must be unique and capable to be used as an image identifier. In this paper we will introduce a method of image features extraction; it will be shown that this method will enhance the efficiency of the features extraction process. The proposed method will be experimentally tested using various images; the obtained experimental results will be compared with other existing methods of feature extraction to show the advantages of the proposed method and to show how to increase the speed up of the method.

Combining Block DCV and Support Vector Machine for Ear Recognition

2018 ◽  
pp. 774-783
Author(s):  
Zhao Hailong ◽  
Yi Junyan
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Construction Method ◽  
Experimental Results ◽  
Support Vector ◽  
Recognition Method ◽  
Ear Recognition ◽  
Human Ear ◽  
Better Than

In recent years, automatic ear recognition has become a popular research. Effective feature extraction is one of the most important steps in Content-based ear image retrieval applications. In this paper, the authors proposed a new vectors construction method for ear retrieval based on Block Discriminative Common Vector. According to this method, the ear image is divided into 16 blocks firstly and the features are extracted by applying DCV to the sub-images. Furthermore, Support Vector Machine is used as classifier to make decision. The experimental results show that the proposed method performs better than classical PCA+LDA, so it is an effective human ear recognition method.

A Hybrid Approach for Plant Leaf Disease Detection and Classification Using Digital Image Processing Methods

2020 ◽  
pp. 002072092095312
Author(s):  
Anusha Rao ◽  
S.B. Kulkarni
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Digital Image Processing ◽  
Digital Image ◽  
Gabor Filter ◽  
Hybrid Approach ◽  
Project Based Learning ◽  
Detection Accuracy ◽  
Plant Leaf ◽  
Leaf Disease

Detection of plant leaf disease has been considered an interesting research field which is helpful to improve the crop and fruit yield. Computer vision and machine learning based approaches have gained huge attraction in digital image processing field. Several visual computing based techniques have been presented in the past for early prediction of plant leaf diseases. However, detection accuracy is still considered as a challenging task. Hence, in order to overcome this issue, we introduce a novel hybrid approach carried out in three forms. During the first phase, image enhancement and image conversion scheme are incorporated, which helps to overcome the low-illumination and noise related issues. In the next phase, a combined feature extraction technique is developed by using GLCM, Complex Gabor filter, Curvelet and image moments. Finally, a Neuro-Fuzzy Logic classifier is trained with the extracted features. The proposed approach is implemented using MATLAB simulation tool where PlantVillage Database is considered for analysis. The average detection accuracy has been obtained as more than 90% for 2 test cases which shows that the proposed combination of feature extraction and image pre-processing process is able to obtain improved classification accuracy. This work is useful for the students of UG/PG programme to carry out Project-based learning.

Two-Steps Digital Image Correlation and Application to Studies of Micromechanics Behavior of Coating Interface

2007 ◽  
Vol 336-338 ◽  
pp. 1810-1813
Author(s):  
Jin Long Chen ◽  
Quan Yu Liu ◽  
Yu Wen Qin ◽  
Xin Hua Ji
Keyword(s):  
Digital Image Correlation ◽  
Base Metal ◽  
Digital Image ◽  
Spline Interpolation ◽  
Compressive Strain ◽  
Correlation Method ◽  
Experimental Results ◽  
Image Correlation ◽  
Different Thickness ◽  
Better Than

In this paper, two-steps digital image correlation method is well advanced with the sub-pixel reconstruction in sub-image by utilizing the higher precision calculation of bicubic spline interpolation value method, and the accuracy of displacement is extended to better than 0.01 pixel, the strain resolution is limited to less than 0.0002 in micro-region. The experimental results show that the method of two-steps digital image correlation is an up-to-date technique to comprehensive investigations of base metal-coating composition at extremely small size scales. In the meantime, influence of crack in base metal perpendicular to interface on coating is directly observed, and the measurement of the residual compressive strain in base metal is performed according to different thickness of coatings. Lastly, the bond behavior between base metal and coating has been quantitatively analyzed, and the experimental results also prove the Ni-Lan coating is combined with base metal by toughened interface.

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