scholarly journals Improving Image Steganalyser Performance using Second Order SPAM Features Extracting through Contourlet Transform

2019 ◽  
Vol 9 (1S3) ◽  
pp. 137-143
Keyword(s):  
Detection Rate ◽  
Image Features ◽  
Second Order ◽  
Contourlet Transform ◽  
Support Vector ◽  
Detection Accuracy ◽  
Accuracy Rate ◽  
Stego Image ◽  
Natural Noise ◽  
Feature Based

The major challenge posed by feature based blind steganalysers is the scheming of useful image features, which offers true existence of the stego noise rather than the natural noise in the images. Despite hundreds of features being applied in the real time implementation, only low detection accuracy could be achieved. Hence, this paper proposes a new model for detecting the stego image coupled with an examination of the task by applying a two-step process. (a) Extraction of the second order SPAM (Subtractive Pixel Adjacency Matrix) as features and the second order SPAM features of coefficients and co-occurrence matrices of sub band images from the contourlet transform. (b) Implementation of the system, based on an efficient classifier, Support Vector Machine which is capable of providing the higher detection rate than the existing classifers. Full- fledged experimentation with huge database of clean and steganogram images produced from seven steganographic schemes with varying embedding rates, and using five steganalysers were carried out in this study. The study shows that the proposed paradigm enhances the detection accuracy rate substantially and validates its efficiency with its better performance even at low embedding rates.

Research on the Detection Method of the Malicious Attacks on Campus Network

2014 ◽  
Vol 644-650 ◽  
pp. 3291-3294
Author(s):  
Jing Lei Wang
Keyword(s):  
Support Vector Machine ◽  
Detection Method ◽  
Attack Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Campus Network ◽  
Malicious Attacks ◽  
Accuracy Rate ◽  
Malicious Attack ◽  
Conventional Manner

The problem of malicious attacks detection on campus network is studied to improve the accuracy of detection. When detecting malicious attacks on campus network, a conventional manner is usually conducted in malicious attack detection of campus network. If a malicious signature is mutated into a new feature, the conventional detection method cannot recognize the new malicious signature, resulting in a relative low detection accuracy rate of malicious attacks. To avoid these problems, in this paper, the malicious attacks detection method for campus network based on support vector machine algorithm is proposed. The plane of support vector machine classification is constructed, to complete the malicious attacks detection of campus network. Experiments show that this approach can improve the accuracy rate of the malicious attack detection, and achieve satisfactory results.

Contourlet Transform Based Texture Analysis for Smoke and Fog Classification

2011 ◽  
Vol 88-89 ◽  
pp. 537-542 ◽  
Author(s):  
Yang Zhao ◽  
Jian Hui Zhao ◽  
Jing Huang ◽  
Shi Zhong Han ◽  
Cheng Jiang Long ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Local Binary Pattern ◽  
Fire Detection ◽  
Contourlet Transform ◽  
Support Vector ◽  
Smoke Detection ◽  
Accuracy Rate ◽  
Practical Applications ◽  
Detection Algorithms ◽  
Important Research Topic

Fire detection has long been an important research topic in image processing and pattern recognition, while smoke is a vital indication of fire’s existence. However, current smoke detection algorithms are far from meeting the requirements of practical applications. One major reason is that the existing methods can not distinguish smoke from fog because their colors and shapes are both very similar. This paper proposes a novel texture analysis based algorithm which has the ability to classify smoke and fog more efficiently. First the texture images are decomposed using Contourlet Transform (CT), and then we extract the feature vector from Contourlet coefficients, finally we make use of Support Vector Machine (SVM) to classify the textures. Experiments are performed on the sample images of smoke and fog taking accuracy rate of classification as evaluation criterion, and the accuracy rate of our algorithm is 97%. To illustrate its performance, our method has also been compared with the algorithms using Gray Level Co-occurrence Matrixes (GLCM), Local Binary Pattern (LBP) and Wavelet Transform (WT).

scholarly journals An Information Theoretic Image Steganalysis for LSB Steganography

2020 ◽  
Author(s):  
Sonam Chhikara ◽  
Rajeev Kumar
Keyword(s):  
Detection Rate ◽  
Information Gain ◽  
Attack Detection ◽  
False Alarms ◽  
Detection Accuracy ◽  
Joint Entropy ◽  
Ensemble Classifiers ◽  
Stego Image ◽  
Reliability Performance ◽  
Embedded Image

Steganography hides the data within a media file in an imperceptible way. Steganalysis exposes steganography by using detection measures. Traditionally, Steganalysis revealed steganography by targeting perceptible and statistical properties which results in developing secure steganography schemes. In this work, we target LSB image steganography by using entropy and joint entropy metrics for steganalysis. First, the Embedded image is processed for feature extraction then analyzed by entropy and joint entropy with their corresponding original image. Second, SVM and Ensemble classifiers are trained according to the analysis results. The decision of classifiers discriminates cover image from stego image. This scheme is further applied on attacked stego image for checking detection reliability. Performance evaluation of proposed scheme is conducted over grayscale image datasets. We analyzed LSB embedded images by Comparing information gain from entropy and joint entropy metrics. Results conclude that entropy of the suspected image is more preserving than joint entropy. As before histogram attack, detection rate with entropy metric is 70% and 98% with joint entropy metric. However after an attack, entropy metric ends with 30% detection rate while joint entropy metric gives 93% detection rate. Therefore, joint entropy proves to be better steganalysis measure with 93% detection accuracy and less false alarms with varying hiding ratio.

A Method of Visibility Detection Based on the Transfer Learning

2019 ◽  
Vol 36 (10) ◽  
pp. 1945-1956
Author(s):  
Qian Li ◽  
Shaoen Tang ◽  
Xuan Peng ◽  
Qiang Ma
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Transfer Learning ◽  
Image Quality Assessment ◽  
Image Features ◽  
Reference Image ◽  
Support Vector ◽  
Detection Accuracy ◽  
Deep Convolutional Neural Networks ◽  
Support Vector Regression Model

AbstractAtmospheric visibility is an important element of meteorological observation. With existing methods, defining image features that reflect visibility accurately and comprehensively is difficult. This paper proposes a visibility detection method based on transfer learning using deep convolutional neural networks (DCNN) that addresses issues caused by a lack of sufficient visibility labeled datasets. In the proposed method, each image was first divided into several subregions, which were encoded to extract visual features using a pretrained no-reference image quality assessment neural network. Then a support vector regression model was trained to map the extracted features to the visibility. The fusion weight of each subregion was evaluated according to the error analysis of the regression model. Finally, the neural network was fine-tuned to better fit the problem of visibility detection using the current detection results conversely. Experimental results demonstrated that the detection accuracy of the proposed method exceeds 90% and satisfies the requirements of daily observation applications.

scholarly journals Establishment of Relational Model of Congenital Heart Disease Markers and GO Functional Analysis of the Association between Its Serum Markers and Susceptibility Genes

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Min Liu ◽  
Luosha Zhao ◽  
Jiaying Yuan
Keyword(s):  
Support Vector Machine ◽  
Congenital Heart Disease ◽  
Heart Disease ◽  
Congenital Heart ◽  
Susceptibility Genes ◽  
Serum Markers ◽  
Relational Model ◽  
Support Vector ◽  
Detection Accuracy ◽  
Accuracy Rate

Purpose. The purpose of present study was to construct the best screening model of congenital heart disease serum markers and to provide reference for further prevention and treatment of the disease.Methods. Documents from 2006 to 2014 were collected and meta-analysis was used for screening susceptibility genes and serum markers closely related to the diagnosis of congenital heart disease. Data of serum markers were extracted from 80 congenital heart disease patients and 80 healthy controls, respectively, and then logistic regression analysis and support vector machine were utilized to establish prediction models of serum markers and Gene Ontology (GO) functional annotation.Results. Results showed that NKX2.5, GATA4, and FOG2 were susceptibility genes of congenital heart disease. CRP, BNP, and cTnI were risk factors of congenital heart disease (p<0.05); cTnI, hs-CRP, BNP, and Lp(a) were significantly close to congenital heart disease (p<0.01). ROC curve indicated that the accuracy rate of Lp(a) and cTnI, Lp(a) and BNP, and BNP and cTnI joint prediction was 93.4%, 87.1%, and 97.2%, respectively. But the detection accuracy rate of the markers’ relational model established by support vector machine was only 85%. GO analysis suggested that NKX2.5, GATA4, and FOG2 were functionally related to Lp(a) and BNP.Conclusions. The combined markers model of BNP and cTnI had the highest accuracy rate, providing a theoretical basis for the diagnosis of congenital heart disease.

Hybrid diffusion tensor imaging feature-based AD classification

2020 ◽  
pp. 1-19
Author(s):  
Lan Deng ◽  
Yuanjun Wang
Keyword(s):  
Machine Learning ◽  
Diffusion Tensor ◽  
Pearson Correlation ◽  
Brain Networks ◽  
Support Vector ◽  
Detection Accuracy ◽  
Detection Model ◽  
Learning Classifier ◽  
Diffusion Tensor Images ◽  
Feature Based

BACKGROUND: Effective detection of Alzheimer’s disease (AD) is still difficult in clinical practice. Therefore, establishment of AD detection model by means of machine learning is of great significance to assist AD diagnosis. OBJECTIVE: To investigate and test a new detection model aiming to help doctors diagnose AD more accurately. METHODS: Diffusion tensor images and the corresponding T1w images acquired from subjects (AD = 98, normal control (NC) = 100) are used to construct brain networks. Then, 9 types features (198×90×9 in total) are extracted from the 3D brain networks by a graph theory method. Features with low correction in both groups are selected through the Pearson correlation analysis. Finally, the selected features (198×33, 198×26, 198×30, 198×42, 198×36, 198×23, 198×29, 198×14, 198×25) are separately used into train 3 machine learning classifier based detection models in which 60% of study subjects are used for training, 20% for validation and 20% for testing. RESULTS: The best detection accuracy levels of 3 models are 90%, 98% and 90% with the corresponding sensitivity of 92%, 96%, and 72% and specificity of 88%, 100% and 94% when using a random forest classifier trained with the Shortest Path Length (SPL) features (198×14), a support vector machine trained with the Degree Centrality features (198×33), and a convolution neural network trained with SPL features, respectively. CONCLUSIONS: This study demonstrates that the new method and models not only improve the accuracy of detecting AD, but also avoid bias caused by the method of direct dimensionality reduction from high dimensional data.

scholarly journals A Statistical Image Feature-Based Deep Belief Network for Fire Detection

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Dali Sheng ◽  
Jinlian Deng ◽  
Wei Zhang ◽  
Jie Cai ◽  
Weisheng Zhao ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Image Features ◽  
Deep Belief Network ◽  
Image Feature ◽  
Support Vector ◽  
Layer By Layer ◽  
Complex Environment ◽  
Belief Network ◽  
Time Frequency ◽  
Feature Based

Detecting fires is of significance to guarantee the security of buildings and forests. However, it is difficult to fast and accurately detect fire stages in complex environment because of the large variations of the fire features of color, texture, and shapes for flame and smoke images. In this paper, a statistic image feature-based deep belief network (DBN) is proposed for fire detections. Firstly, for each individual image, all the statistic image features extracted from a flame and smoke image in time domain, frequency domain, and time-frequency domain are calculated to construct training and testing samples. Then, the constructed samples are fed into DBN to classify the multiple fire stages in complex environment. DBN can automatically learn fault features layer by layer using restricted Boltzmann machine (RBM). Experiments using the benchmark data of three groups of fire and fire-like images are classified by the present method, and the classification results are also compared with those commonly used support vector machine (SVM) and convolutional deep belief networks (CDBNs) to manifest the superiority of the classification accuracy.

scholarly journals Investigation on Recognition Performance of Harvesting Robot Using Regions of Interest Histogram of Oriented Gradients Feature Based on Improved Fuzzy Least Square Support Vector Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jianping Ou ◽  
Jun Zhang
Keyword(s):  
Support Vector Machine ◽  
Feature Recognition ◽  
Recognition Performance ◽  
Regions Of Interest ◽  
Least Square ◽  
Support Vector ◽  
Detection Accuracy ◽  
Histogram Of Oriented Gradients ◽  
Feature Based ◽  
Harvesting Robot

In order to solve the problems such as big errors, lack of universality, and too much time consuming occurred in the recognition of overlapped fruits, an improved fuzzy least square support vector machine (FLS-SVM) is established based on the fruit ROI-HOG feature. First, the RGB image is transformed into saturation and value (HSV) image, and then the regions of interest (ROI) are detected from HSV color information. Finally, the histogram of oriented gradients (HOG) feature of ROI will be used as the input of FLS-SVM pattern recognizer to realize the recognition of picking fruit. In addition, the verified FLS-SVM is used to investigate the recognition performance of harvesting robot using regions of interest histogram of oriented gradients feature. The results reveal that the vector sizes are effectively reduced and a higher detection speed is achieved without compromising accuracy relative to conventional approaches. Similarly, the detection accuracy for the learning samples, the isolated fruit, the overlapped fruit, and the background can achieve 99.50%, 96.0%, 89.9%, and 97.0%, respectively, which shows the good performance of the proposed improved ROI-HOG feature recognition method.

Detection of Malicious Spatial-Domain Steganography over Noisy Channels Using Convolutional Neural Networks

2020 ◽  
Vol 2020 (4) ◽  
pp. 76-1-76-7
Author(s):  
Swaroop Shankar Prasad ◽  
Ofer Hadar ◽  
Ilia Polian
Keyword(s):  
State Of The Art ◽  
Visual Quality ◽  
Channel Noise ◽  
Detection Accuracy ◽  
Noisy Channel ◽  
Noisy Channels ◽  
Reliable Transmission ◽  
Reliable Detection ◽  
Natural Noise ◽  
Will Force

Image steganography can have legitimate uses, for example, augmenting an image with a watermark for copyright reasons, but can also be utilized for malicious purposes. We investigate the detection of malicious steganography using neural networkbased classification when images are transmitted through a noisy channel. Noise makes detection harder because the classifier must not only detect perturbations in the image but also decide whether they are due to the malicious steganographic modifications or due to natural noise. Our results show that reliable detection is possible even for state-of-the-art steganographic algorithms that insert stego bits not affecting an image’s visual quality. The detection accuracy is high (above 85%) if the payload, or the amount of the steganographic content in an image, exceeds a certain threshold. At the same time, noise critically affects the steganographic information being transmitted, both through desynchronization (destruction of information which bits of the image contain steganographic information) and by flipping these bits themselves. This will force the adversary to use a redundant encoding with a substantial number of error-correction bits for reliable transmission, making detection feasible even for small payloads.

Integrating Second-order Moving Average and Over-sampling Algorithm to Predict Apoptosis Protein Subcellular Localization

2020 ◽  
Vol 15 (6) ◽  
pp. 517-527
Author(s):  
Yunyun Liang ◽  
Shengli Zhang
Keyword(s):  
Subcellular Localization ◽  
Moving Average ◽  
Subcellular Location ◽  
Second Order ◽  
Test Method ◽  
Support Vector ◽  
Protein Subcellular Localization ◽  
Protein Subcellular Location ◽  
Apoptosis Protein ◽  
Leibler Divergence

Background: Apoptosis proteins have a key role in the development and the homeostasis of the organism, and are very important to understand the mechanism of cell proliferation and death. The function of apoptosis protein is closely related to its subcellular location. Objective: Prediction of apoptosis protein subcellular localization is a meaningful task. Methods: In this study, we predict the apoptosis protein subcellular location by using the PSSMbased second-order moving average descriptor, nonnegative matrix factorization based on Kullback-Leibler divergence and over-sampling algorithms. This model is named by SOMAPKLNMF- OS and constructed on the ZD98, ZW225 and CL317 benchmark datasets. Then, the support vector machine is adopted as the classifier, and the bias-free jackknife test method is used to evaluate the accuracy. Results: Our prediction system achieves the favorable and promising performance of the overall accuracy on the three datasets and also outperforms the other listed models. Conclusion: The results show that our model offers a high throughput tool for the identification of apoptosis protein subcellular localization.

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