scholarly journals Data hiding based on noise-like signal addressing

Radiotekhnika ◽  
2020 ◽  
pp. 38-49
Author(s):  
A.A. Kuznetsov ◽  
O.A. Smirnov ◽  
A.S. Kiian ◽  
T.Y. Kuznetsova
Keyword(s):  
Data Hiding ◽  
Spread Spectrum ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Experimental Studies ◽  
Correlation Coefficients ◽  
Semantic Content ◽  
Random Sequences ◽  
Redundant Data ◽  
Hidden Data

There are various computing techniques (methods) to transmit secret messages. For example, cryptographic techniques hide the semantic content of transmitted messages, presenting them in the form of noise-like minor data. Steganographic techniques hide the existence of information messages itself. In this case, messages are hidden inside cover files, i.e., redundant data that are transmitted in an open way and do not cause suspicion in anyone. An outside observer can intercept cover files, analyze and examine them. However, it is very difficult or even impossible to detect and recover hidden data. This article discusses the techniques for hiding data in cover images using direct spread spectrum. We propose a new technique that consists in direct addressing of pseudo-random sequences. On the one hand, it significantly reduces cover file distortion. On the other hand, the error rate in recovered messages does not increase. Our experiments have shown, that Spread Spectrum Steganography technique indeed reduce the distortion in cover images compared to other techniques. We give some illustrative examples and show the advantages of the proposed method. Even with a significant increase in encoding density, the quality of cover images does not degrade. We also conduct experiments and evaluate image quality based on Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). The obtained results of experimental studies confirm the adequacy and reliability of the research results. The main disadvantage of the proposed data hiding technique is a high computational complexity. To recover messages, it is necessary to calculate sequentially the correlation coefficients with a large number of pseudo-random sequences.

scholarly journals A Comprehensive Approach to Image Watermarking, Encryption and Steganography

2015 ◽  
Vol 8 (4) ◽  
pp. 32
Author(s):  
Sabarish Sridhar
Keyword(s):  
Data Hiding ◽  
Data Transmission ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Cover Image ◽  
Second Step ◽  
Squared Error ◽  
Feature Similarity ◽  
Secure Data Transmission

Steganography, water marking and encryption are widely used in image processing and communication. A general practice is to use them independently or in combination of two - for e.g. data hiding with encryption or steganography alone. This paper aims to combine the features of watermarking, image encryption as well as image steganography to provide reliable and secure data transmission .The basics of data hiding and encryption are explained. The first step involves inserting the required watermark on the image at the optimum bit plane. The second step is to use an RSA hash to actually encrypt the image. The final step involves obtaining a cover image and hiding the encrypted image within this cover image. A set of metrics will be used for evaluation of the effectiveness of the digital water marking. The list includes Mean Squared Error, Peak Signal to Noise Ratio and Feature Similarity.

scholarly journals A Novel Scheme for Merging Active and Passive Satellite Soil Moisture Retrievals Based on Maximizing the Signal to Noise Ratio

2020 ◽  
Vol 12 (22) ◽  
pp. 3804
Author(s):  
B. G. Mousa ◽  
Hong Shu ◽  
Mohamed Freeshah ◽  
Aqil Tariq
Keyword(s):  
Soil Moisture ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Correlation Coefficients ◽  
Temporal Correlation ◽  
Global Scale ◽  
Signal To Noise ◽  
Significance Levels ◽  
Land Data Assimilation ◽  
Land Data Assimilation System

In this research, we developed and evaluated a new scheme for merging soil moisture (SM) retrievals from both passive and active microwave satellite estimates, based on maximized signal-to-noise ratios, in order to produce improved SM products using least-squares theory. The fractional mean-squared-error (fMSE) derived from the triple collocation method (TCM) was used for this purpose. The proposed scheme was applied by using a threshold between signal and noise at fMSE equal to 0.5 to maintain the high-quality SM observations. In the regions where TCM is unreliable, we propose four scenarios based on the determinations of correlations between all three SM products of TCM at significance levels (i.e., p-values). The proposed scheme was applied to combine SM retrievals from Soil Moisture Active Passive (SMAP), Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) to produce SMAP+ASCAT and AMSR2+ASCAT SM datasets at a global scale for the period from June 2015 to December 2017. The merged SM dataset performance was assessed against SM data from ground measurements of international soil moisture network (ISMN), Global Land Data Assimilation System-Noah (GLDAS-Noah) and ERA5. The results show that the two merged SM datasets showed significant improvement over their parent products in the high average temporal correlation coefficients (R) and the lowest root mean squared difference (RMSE), compared with in-situ measurements over different networks of ISMN. Moreover, these datasets outperformed their parent products over different land cover types in most regions of the world, with a high overall average temporal R and the lowest overall average RMSE value with GLDAS and ERA5. In addition, the suggested scenarios improved SM performance in the regions with unreliable TCMs.

scholarly journals AN ENHANCED IMAGE STEGANOGRAPHY TECHNIQUE FOR HIGH-SECURITY COMMUNICATION

2021 ◽  
Vol 9 (2) ◽  
pp. 1-6
Author(s):  
Apoorv Mahajan ◽  
Arpan Singh Rajput
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Data Hiding ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Discrete Wavelet ◽  
Minimum Mean Squared Error ◽  
The Matrix ◽  
Value Decomposition

Purpose of the study: We propose an approach to hide data in an image with minimum Mean Squared Error (MSE) and maximum Signal-to-Noise ratio (SNR) using Discrete Wavelet Transform (DWT). Methodology: The methodology used by us considers the application of Discrete Wavelet transform to transform the values of the image into a different domain for embedding the information to be hidden in the image and then using Singular Value decomposition we decomposed the matrix values of the image for better data hiding. Main Findings: The application of the SVD function gave the model a better performance and also RED pixel values with the High-High frequency domain are a better cover for hiding data. Applications of this study: This article can be used for further research on applications of mathematical and frequency transformation functions on data hiding. It can also be used to implement a highly secure image steganography model. Novelty/Originality of this study: The application of Discrete Wavelet Transform has been used before but the application of SVD and hiding data in the H-H domain to obtain better results is original.

scholarly journals Hybrid Data Hiding Based on AMBTC Using Enhanced Hamming Code

2020 ◽  
Vol 10 (15) ◽  
pp. 5336 ◽  
Author(s):  
Cheonshik Kim ◽  
Dong-Kyoo Shin ◽  
Ching-Nung Yang ◽  
Lu Leng
Keyword(s):  
Data Hiding ◽  
Mean Squared Error ◽  
Block Code ◽  
Low Complexity ◽  
Image Distortion ◽  
Hamming Codes ◽  
Redundant Data ◽  
Squared Error ◽  
Hybrid Data ◽  
Grayscale Images

The image-based data hiding method is a technology used to transmit confidential information secretly. Since images (e.g., grayscale images) usually have sufficient redundancy information, they are a very suitable medium for hiding data. Absolute Moment Block Truncation Coding (AMBTC) is one of several compression methods and is appropriate for embedding data due to its very low complexity and acceptable distortion. However, since there is not enough redundant data compared to grayscale images, the research to embed data in the compressed image is a very challenging topic. That is the motivation and challenge of this research. Meanwhile, the Hamming codes are used to embed secret bits, as well as a block code that can detect up to two simultaneous bit errors and correct single bit errors. In this paper, we propose an effective data hiding method for two quantization levels of each block of AMBTC using Hamming codes. Bai and Chang introduced a method of applying Hamming (7,4) to two quantization levels; however, the scheme is ineffective, and the image distortion error is relatively large. To solve the problem with the image distortion errors, this paper introduces a way of optimizing codewords and reducing pixel distortion by utilizing Hamming (7,4) and lookup tables. In the experiments, when concealing 150,000 bits in the Lena image, the averages of the Normalized Cross-Correlation (NCC) and Mean-Squared Error (MSE) of our proposed method were 0.9952 and 37.9460, respectively, which were the highest. The sufficient experiments confirmed that the performance of the proposed method is satisfactory in terms of image embedding capacity and quality.

Pre-Processing of Hyperspectral Images Using Nonlinear Filters

2013 ◽  
Vol 11 (1) ◽  
pp. 8-13
Author(s):  
V. Behar ◽  
V. Bogdanova
Keyword(s):  
Signal To Noise Ratio ◽  
Correlation Coefficients ◽  
Hyperspectral Images ◽  
Spectral Correlation ◽  
Edge Preserving ◽  
Improvement Factor ◽  
Filter Output ◽  
Average Improvement ◽  
Processing Procedure

Abstract In this paper the use of a set of nonlinear edge-preserving filters is proposed as a pre-processing stage with the purpose to improve the quality of hyperspectral images before object detection. The capability of each nonlinear filter to improve images, corrupted by spatially and spectrally correlated Gaussian noise, is evaluated in terms of the average Improvement factor in the Peak Signal to Noise Ratio (IPSNR), estimated at the filter output. The simulation results demonstrate that this pre-processing procedure is efficient only in case the spatial and spectral correlation coefficients of noise do not exceed the value of 0.6

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

A New Approach for Predicting the Value of Gene Expression: Two-way Collaborative Filtering

2019 ◽  
Vol 14 (6) ◽  
pp. 480-490 ◽  
Author(s):  
Tuncay Bayrak ◽  
Hasan Oğul
Keyword(s):  
Gene Expression ◽  
Regression Model ◽  
Collaborative Filtering ◽  
High Throughput Sequencing ◽  
Mean Squared Error ◽  
Experimental Studies ◽  
Kernel Functions ◽  
Feature Representation ◽  
Sequencing Analysis ◽  
Computational Systems Biology

Background: Predicting the value of gene expression in a given condition is a challenging topic in computational systems biology. Only a limited number of studies in this area have provided solutions to predict the expression in a particular pattern, whether or not it can be done effectively. However, the value of expression for the measurement is usually needed for further meta-data analysis. Methods: Because the problem is considered as a regression task where a feature representation of the gene under consideration is fed into a trained model to predict a continuous variable that refers to its exact expression level, we introduced a novel feature representation scheme to support work on such a task based on two-way collaborative filtering. At this point, our main argument is that the expressions of other genes in the current condition are as important as the expression of the current gene in other conditions. For regression analysis, linear regression and a recently popularized method, called Relevance Vector Machine (RVM), are used. Pearson and Spearman correlation coefficients and Root Mean Squared Error are used for evaluation. The effects of regression model type, RVM kernel functions, and parameters have been analysed in our study in a gene expression profiling data comprising a set of prostate cancer samples. Results: According to the findings of this study, in addition to promising results from the experimental studies, integrating data from another disease type, such as colon cancer in our case, can significantly improve the prediction performance of the regression model. Conclusion: The results also showed that the performed new feature representation approach and RVM regression model are promising for many machine learning problems in microarray and high throughput sequencing analysis.

scholarly journals A Comparative Study of 3D UE Positioning in 5G New Radio with a Single Station

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1178
Author(s):  
Bo Sun ◽  
Bo Tan ◽  
Wenbo Wang ◽  
Elena Simona Lohan
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Time Estimation ◽  
Satellite System ◽  
Base Station ◽  
Rectangular Array ◽  
Single Station ◽  
5G Network ◽  
Autonomous Machines ◽  
3D Positioning

The 5G network is considered as the essential underpinning infrastructure of manned and unmanned autonomous machines, such as drones and vehicles. Besides aiming to achieve reliable and low-latency wireless connectivity, positioning is another function provided by the 5G network to support the autonomous machines as the coexistence with the Global Navigation Satellite System (GNSS) is typically supported on smart 5G devices. This paper is a pilot study of using 5G uplink physical layer channel sounding reference signals (SRSs) for 3D user equipment (UE) positioning. The 3D positioning capability is backed by the uniform rectangular array (URA) on the base station and by the multiple subcarrier nature of the SRS. In this work, the subspace-based joint angle-time estimation and statistics-based expectation-maximization (EM) algorithms are investigated with the 3D signal manifold to prove the feasibility of using SRSs for 3D positioning. The positioning performance of both algorithms is evaluated by estimation of the root mean squared error (RMSE) versus the varying signal-to-noise-ratio (SNR), the bandwidth, the antenna array configuration, and multipath scenarios. The simulation results show that the uplink SRS works well for 3D UE positioning with a single base station, by providing a flexible resolution and accuracy for diverse application scenarios with the support of the phased array and signal estimation algorithms at the base station.

Reversible Data Hiding for Encrypted Image Based on Interpolation Error Expansion

2018 ◽  
Vol 9 (4) ◽  
pp. 76-96 ◽  
Author(s):  
Fuqiang Di ◽  
Minqing Zhang ◽  
Yingnan Zhang ◽  
Jia Liu
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Side Information ◽  
Signal To Noise Ratio ◽  
Prediction Method ◽  
Interpolation Error ◽  
Encrypted Image ◽  
Error Expansion ◽  
Pixel Correlation ◽  
Embedding Performance

A novel reversible data hiding algorithm for encrypted image based on interpolation error expansion is proposed. The proposed method is an improved version of Shiu' s. His work does not make full use of the correlation of the neighbor pixels and some additional side information is needed. The proposed method adopts the interpolation prediction method to fully exploit the pixel correlation and employ the Paillier public key encryption method. The algorithm is reversible. In the proposed method, less side information is demanded. The experiment has verified the feasibility and effectiveness of the proposed method, and the better embedding performance can be obtained, compared with some existing RDHEI-P methods. Specifically, the final embedding capacity can be up to 0.74 bpp (bit per pixel), while the peak signal-to-noise ratio (PSNR) for the marked image Lena is 35 dB. This is significantly higher than Shiu's method which is about 0.5 bpp.

scholarly journals Methods of Power Line Interference Elimination in EMG Signal

2019 ◽  
Vol 40 ◽  
pp. 64-70
Author(s):  
Martina Ladrova ◽  
Radek Martinek ◽  
Jan Nedoma ◽  
Marcel Fajkus
Keyword(s):  
Signal To Noise Ratio ◽  
Notch Filter ◽  
Correlation Coefficients ◽  
Power Line ◽  
Shape Distortion ◽  
Emg Signal ◽  
Wide Range ◽  
Interference Elimination ◽  
Power Line Interference ◽  
Line Interference

Electromyogram (EMG) recordings are often corrupted by the wide range of artifacts, which one of them is power line interference (PLI). The study focuses on some of the well-known signal processing approaches used to eliminate or attenuate PLI from EMG signal. The results are compared using signal-to-noise ratio (SNR), correlation coefficients and Bland-Altman analysis for each tested method: notch filter, adaptive noise canceller (ANC) and wavelet transform (WT). Thus, the power of the remaining noise and shape of the output signal are analysed. The results show that the ANC method gives the best output SNR and lowest shape distortion compared to the other methods.

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