Pixel Prediction-Based Image Steganography by Support Vector Neural Network

2020 ◽  
Author(s):  
Reshma V K ◽  
Vinod Kumar R S
Keyword(s):  
Neural Network ◽  
Medical Image ◽  
Medical Information ◽  
Similarity Index ◽  
Image Steganography ◽  
Secret Message ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Least Significant Bit ◽  
Prediction Scheme

Abstract Securing the privacy of the medical information through the image steganography process has gained more research interest nowadays to protect the privacy of the patient. In the existing works, least significant bit (LSB) replacement strategy was most popularly used to hide the sensitive contents. Here, every pixel was replaced for achieving higher privacy, but it increased the complexity. This work introduces a novel pixel prediction scheme-based image steganography to overcome the complexity issues prevailing in the existing works. In the proposed pixel prediction scheme, the support vector neural network (SVNN) classifier is utilized for the construction of a prediction map, which identifies the suitable pixels for the embedding process. Then, in the embedding phase, wavelet coefficients are extracted from the medical image based on discrete wavelet transform (DWT) and embedding strength, and the secret message is embedded into the HL wavelet band. Finally, the secret message is extracted from the medical image on applying the DWT. The experimentation of the proposed pixel prediction scheme is done by utilizing the medical images from the BRATS database. The proposed pixel prediction scheme has achieved high performance with the values of 48.558 dB, 0.50009 and 0.9879 for the peak signal to noise ratio (PSNR), Structural Similarity Index (SSIM) and correlation factor, respectively.

scholarly journals Chicken-Moth Search Optimization-based Deep Convolutional Neural Network for Image Steganography

2020 ◽  
Vol 21 (2) ◽  
pp. 217-232
Author(s):  
Reshma V K ◽  
Vinod Kumar R S ◽  
Shahi D ◽  
Shyjith M B
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Deep Convolutional Neural Network ◽  
Image Steganography ◽  
Secret Message ◽  
Search Optimization ◽  
Tetrolet Transform

Image steganography is considered as one of the promising and popular techniques utilized to maintain the confidentiality of the secret message that is embedded in an image. Even though there are various techniques available in the previous works, an approach providing better results is still the challenge. Therefore, an effective pixel prediction based on image stegonography is developed, which employs error dependent Deep Convolutional Neural Network (DCNN) classifier for pixel identification. Here, the best pixels are identified from the medical image based on DCNN classifier using pixel features, like texture, wavelet energy, Gabor, scattering features, and so on. The DCNN is optimally trained using Chicken-Moth search optimization (CMSO). The CMSO is designed by integrating Chicken Swarm Optimization (CSO) and Moth Search Optimization (MSO) algorithm based on limited error. Subsequently, the Tetrolet transform is fed to the predicted pixel for the embedding process. At last, the inverse tetrolet transform is used for extracting the secret message from an embedded image. The experimentation is carried out using BRATS dataset, and the performance of image stegonography based on CMSO-DCNN+tetrolet is evaluated based on correlation coefficient, Structural Similarity Index, and Peak Signal to Noise Ratio, which attained 0.85, 46.981dB, and 0.6388, for the image with noise.  

scholarly journals A Comparative Analysis of LSB, MSB and PVD Based Image Steganography

2021 ◽  
Vol 8 (9) ◽  
pp. 373-377
Author(s):  
Alade Oluwaseun. Modupe ◽  
Amusan Elizabeth Adedoyin ◽  
Adedeji Oluyinka Titilayo
Keyword(s):  
Comparative Analysis ◽  
Performance Metrics ◽  
Similarity Index ◽  
Image Steganography ◽  
Secret Message ◽  
Future Research ◽  
Least Significant Bit ◽  
Comparative Performance ◽  
Pixel Value ◽  
Pixel Value Differencing

Steganography is the art and science of hiding information by embedding data into cover media. Numerous techniques are designed to provide the security for the communication of data over the Internet. A good steganographic algorithm is recognized by the performance of the techniques measured with the support of the performance metrics among which are PSNR, MSE, SSIM, robustness and capacity to hide the information in the cover image. In this paper a comparative analysis of Least Significant Bit (LSB), Most Significant Bit (MSB) and Pixel Value Differencing (PVD) image steganography in grayscale and colored images was performed. Three different cover images was used to hide secret message. A comparative performance analysis of LSB, MSB and PVD methods used in image steganography was performed using peak signal to noise ratio (PSNR), Mean square error (MSE) and Structural Similarity index (SSIM) as performance metrics. LSB technique gives higher PSNR and SSIM values than MSB and PVD method with lower MSE than the other two techniques. Future research can be geared towards investigating the embedding capacity, security, and computational complexity of each technique. Keywords: Least Significant Bit (LSB), Most Significant Bit (MSB), Pixel value differencing (PVD), PSNR, SSIM and MSE,

scholarly journals A Comprehensive Review on Medical Image Steganography Based on LSB Technique and Potential Challenges

2021 ◽  
Vol 18 (2(Suppl.)) ◽  
pp. 0957
Author(s):  
Bushra Abdullah Shtayt ◽  
Nur Haryani Zakaria ◽  
Nor Hazlyna Harun
Keyword(s):  
Medical Imaging ◽  
Medical Image ◽  
Medical Information ◽  
Medical Images ◽  
Rapid Development ◽  
Image Steganography ◽  
General Idea ◽  
Sensitive Information ◽  
Least Significant Bit ◽  
Other Information

The rapid development of telemedicine services and the requirements for exchanging medical information between physicians, consultants, and health institutions have made the protection of patients’ information an important priority for any future e-health system. The protection of medical information, including the cover (i.e. medical image), has a specificity that slightly differs from the requirements for protecting other information. It is necessary to preserve the cover greatly due to its importance on the reception side as medical staff use this information to provide a diagnosis to save a patient's life. If the cover is tampered with, this leads to failure in achieving the goal of telemedicine. Therefore, this work provides an investigation of information security techniques in medical imaging, focusing on security goals. Encrypting a message before hiding them gives an extra layer of security, and thus, will provide an excellent solution to protect the sensitive information of patients during the sharing of medical information. Medical image steganography is a special case of image steganography, while Digital Imaging and Communications in Medicine (DICOM) is the backbone of all medical imaging divisions, whereby it is most broadly used to store and transmit medical images. The main objective of this study is to provide a general idea of what Least Significant Bit-based (LSB) steganography techniques have achieved in medical images.

AUTOMATED RECOGNITION OF SINGLE & HYBRID POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM BASED SUPPORT VECTOR MACHINE

2016 ◽  
Vol 79 (1) ◽  
Author(s):  
Suhail Khokhar ◽  
A. A. Mohd Zin ◽  
M. A. Bhayo ◽  
A. S. Mokhtar
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Classification Performance ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network

The monitoring of power quality (PQ) disturbances in a systematic and automated way is an important issue to prevent detrimental effects on power system. The development of new methods for the automatic recognition of single and hybrid PQ disturbances is at present a major concern. This paper presents a combined approach of wavelet transform based support vector machine (WT-SVM) for the automatic classification of single and hybrid PQ disturbances. The proposed approach is applied by using synthetic models of various single and hybrid PQ signals. The suitable features of the PQ waveforms were first extracted by using discrete wavelet transform. Then SVM classifies the type of PQ disturbances based on these features. The classification performance of the proposed algorithm is also compared with wavelet based radial basis function neural network, probabilistic neural network and feed-forward neural network. The experimental results show that the recognition rate of the proposed WT-SVM based classification system is more accurate and much better than the other classifiers. 

scholarly journals Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization

2020 ◽  
Author(s):  
Amit Kumar Gorai ◽  
Simit Raval ◽  
Ashok Kumar Patel ◽  
Snehamoy Chatterjee ◽  
Tarini Gautam
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Machine Vision ◽  
Vision System ◽  
Color Space ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Machine Vision System ◽  
Coal Characterization ◽  
Artificial Neural

Abstract Coal is heterogeneous in nature, and thus the characterization of coal is essential before its use for a specific purpose. Thus, the current study aims to develop a machine vision system for automated coal characterizations. The model was calibrated using 80 image samples that are captured for different coal samples in different angles. All the images were captured in RGB color space and converted into five other color spaces (HSI, CMYK, Lab, xyz, Gray) for feature extraction. The intensity component image of HSI color space was further transformed into four frequency components (discrete cosine transform, discrete wavelet transform, discrete Fourier transform, and Gabor filter) for the texture features extraction. A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development. The datasets of the optimized features were used as an input for the model, and their respective coal characteristics (analyzed in the laboratory) were used as outputs of the model. The R-squared values were found to be 0.89, 0.92, 0.92, and 0.84, respectively, for fixed carbon, ash content, volatile matter, and moisture content. The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression, support vector regression, and radial basis neural network models. The study demonstrates the potential of the machine vision system in automated coal characterization.

Three Modeling Methods Application in Near-Infrared Spectra Analysis for Determination of Volatile in Lignite Coal Samples

2014 ◽  
Vol 541-542 ◽  
pp. 1438-1441
Author(s):  
Xiao Li Yang ◽  
Fan Wang
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Near Infrared ◽  
Partial Least Square ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Radial Basis ◽  
Lignite Coal

We studied volatile determination in lignite coal samples using near-infrared (NIR) spectra. Firstly, spectra were pre-processed to eliminate useless information. Then, determination model was constructed by partial least squares regression. We used discrete wavelet transform to pre-processing. To study the influence of modeling on determination of volatile for NIR analysis of lignite coal samples, we applied three techniques to build determination model, including support vector regression, partial least square regression and radial basis function neural network. Comparison of the mean absolute percentage error (MAPE) and root mean square error of prediction (RMSEP) of the models show that the models constructed with radial basis function neural network gave the best results.

scholarly journals An efficient and automatic ECG arrhythmia diagnosis system using DWT and HOS features and entropy- based feature selection procedure

2019 ◽  
Vol 10 (1) ◽  
pp. 47-54
Author(s):  
Abdullah Jafari Chashmi ◽  
Mehdi Chehel Amirani
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Heart Diseases ◽  
Selection Procedure ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Machine Method ◽  
The Neural Network ◽  
Ecg Arrhythmia

Abstract Primary recognition of heart diseases by exploiting computer aided diagnosis (CAD) machines, decreases the vast rate of fatality among cardiac patients. Recognition of heart abnormalities is a staggering task because the low changes in ECG signals may not be exactly specified with eyesight. In this paper, an efficient approach for ECG arrhythmia diagnosis is proposed based on a combination of discrete wavelet transform and higher order statistics feature extraction and entropy based feature selection methods. Using the neural network and support vector machine, five classes of heartbeat categories are classified. Applying the neural network and support vector machine method, our proposed system is able to classify the arrhythmia classes with high accuracy (99.83%) and (99.03%), respectively. The advantage of the presented procedure has been experimentally demonstrated compared to the other recently presented methods in terms of accuracy.

scholarly journals Steganographic Scheme based on Message-Cover matching

2018 ◽  
Vol 8 (5) ◽  
pp. 3594
Author(s):  
Youssef Taouil ◽  
El Bachir Ameur
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Secret Message ◽  
Discrete Wavelet ◽  
Large Set ◽  
Integer Part ◽  
Least Significant Bit ◽  
Secret Data ◽  
Trade Off ◽  
Good Trade

Steganography is one of the techniques that enter into the field of information   security, it is the art of dissimulating data into digital files in an imperceptible way that does not arise the suspicion. In this paper, a steganographic method based on the Faber-Schauder discrete wavelet transform is proposed. The embedding of the secret data is performed in Least Significant Bit (LSB) of the integer part of the wavelet coefficients. The secret message is decomposed into pairs of bits, then each pair is transformed into another pair based on a permutation that allows to obtain the most matches possible between the message and the LSB of the coefficients. To assess the performance of the proposed method, experiments were carried out on a large set of images, and a comparison to prior works is accomplished. Results show a good level of imperceptibility and a good trade-off imperceptibility-capacity compared to literature.

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