A New Data Hiding Scheme Using Laplace Transformation in Frequency Domain Steganography

2020 ◽  
Vol 4 (1) ◽  
pp. 1-12
Author(s):  
Ayan Chatterjee ◽  
Nikhilesh Barik
Keyword(s):  
Frequency Domain ◽  
Laplace Transformation ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Spatial Domain ◽  
Image Steganography ◽  
Secret Information ◽  
Wireless Communication Network ◽  
Communication Technique ◽  
Efficiency And Effectiveness

Today, in the age of wireless communication, network security is an important issue. Image steganography is a popular approach to make communication secure. Among two categories (spatial domain and frequency domain) of image steganography, frequency domain is more secured than the spatial domain. The proposed scheme develops a secret communication technique in frequency domain steganography. In steganography, the concept of Laplace transformation is used during embedding and extraction procedures of secret information in image files. As a result, all the conditions of information security are developed in high density. The efficiency and effectiveness of the scheme is evaluated by measuring the hacking chance from different statistical attacks by RS steganalysis and originality of actual cover image after inserting secret information by Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR).

scholarly journals An Image Steganography Method Hiding Secret Data into Coefficients of Integer Wavelet Transform Using Pixel Value Differencing Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Avinash K. Gulve ◽  
Madhuri S. Joshi
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Spatial Domain ◽  
Image Steganography ◽  
Cover Image ◽  
Integer Wavelet Transform ◽  
Wavelet Coefficients ◽  
Secret Data ◽  
Secret Information ◽  
Integer Wavelet

The image steganography systems use either the spatial domain or the frequency domain to hide the secret information. The proposed technique uses spatial domain technique to hide secret information in the frequency domain. The cover image is transformed using integer wavelet transform to obtain four subbands: LL, LH, HL, and HH. Then, the PVD approach is used to hide the secret information in the wavelet coefficients of all the four subbands. For improving the security of the hidden information, the proposed method first modifies the difference between two wavelet coefficients of a pair and then uses the modified difference to hide the information. This makes extraction of secret data from the stego image difficult even if the steganography method fails. The result shows that the proposed technique outperforms other PVD based techniques in terms of security of secret information and hiding capacity of cover image.

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 Stego-eHealth: An eHealth System for Secured Transfer of Medical Images using Image Steganography

2021 ◽  
Author(s):  
Nandhini Subramanian ◽  
, Jayakanth Kunhoth ◽  
Somaya Al-Maadeed ◽  
Ahmed Bouridane
Keyword(s):  
Medical Information ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Health Care Systems ◽  
Image Steganography ◽  
Cover Image ◽  
Stego Image ◽  
Hiding Capacity ◽  
Secret Image ◽  
Care Systems

COVID pandemic has necessitated the need for virtual and online health care systems to avoid contacts. The transfer of sensitive medical information including the chest and lung X-ray happens through untrusted channels making it prone to many possible attacks. This paper aims to secure the medical data of the patients using image steganography when transferring through untrusted channels. A deep learning method with three parts is proposed – preprocessing module, embedding network and the extraction network. Features from the cover image and the secret image are extracted by the preprocessing module. The merged features from the preprocessing module are used to output the stego image by the embedding network. The stego image is given as the input to the extraction network to extract the ingrained secret image. Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) are the evaluation metrics used. Higher PSNR value proves the higher security; robustness of the method and the image results show the higher imperceptibility. The hiding capacity of the proposed method is 100% since the cover image and the secret image are of the same size.

Secure Text Sharing Using Medical Image Steganography

2019 ◽  
Vol 7 (3) ◽  
pp. 37-43
Author(s):  
Boopathi. G et al.
Keyword(s):  
Digital Media ◽  
Signal To Noise Ratio ◽  
Data Communication ◽  
Image Steganography ◽  
Transform Domain ◽  
Stego Image ◽  
Secret Information ◽  
Multimedia Contents ◽  
Multiple Receivers

The increased popularity of digital media has raised serious concerns over its security related issues. Security attacks in the form of eavesdropping, masquerading and tampering and in many other forms is very common nowadays. Data hiding is one of the emerging techniques that aim to provide for security by hiding secret information into the multimedia contents by altering some nonessential components in the host or cover file. Security of data is very important in data communication. Everyday a lot of information is transferred from one user to another on internet and so the possibility of data theft also increases. Steganography provides a solution for the security of information during data transmission. Steganography is the science which makes the valuable information invisible to prevent it from unauthorized user. A steganography system, in general, is expected to meet three key requirements, namely, imperceptibility of embedding, accurate recovery of embedded information, and large payload (payload is the bits that get delivered to the end user at the destination). So in this project an image steganography technique is proposed to hide the documents in image in the transform domain using CMD approach. The document files are carried by the image without revealing the existence to anybody. When the secret information is hidden in the carrier the result is the stego signal. In this work, the results show good quality stego signal and the stego signal is analyzed for different attacks. The stego signals are transferred to multiple receivers based on network strategies. It is found that the technique is robust and it can withstand the attacks. The quality of the stego image is measured by Peak Signal to Noise Ratio (PSNR) and other measurements.

scholarly journals Notice of Retraction Digital Image Steganography Using Bit Flipping

2018 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
Aditya Kumar Sahu ◽  
Gandharba Swain
Keyword(s):  
Digital Image ◽  
Quality Index ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Expert Committee ◽  
Original Image ◽  
Signal To Noise ◽  
Secret Data ◽  
Hiding Capacity ◽  
Secret Information

<p><strong>Notice of Retraction</strong><br /><br />-----------------------------------------------------------------------<br />After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IAES's Publication Principles.<br /><br />We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.<br /><br />The presenting author of this paper has the option to appeal this decision by contacting [email protected].<br /><br />-----------------------------------------------------------------------</p><p>This article proposes bit flipping method to conceal secret data in the original image. Here a section consists of 2 pixels and there by flipping one or two LSBs of the pixels to hide secret information in it. It exists in 2 variants. The variant-1 and variant-2 both use 7<sup>th</sup> and 8<sup>th</sup> bit to conceal the secret data. Variant-1 hides 3 bits per a pair of pixels and the variant-2 hides 4 bits per a pair of pixels. Our proposed method notably raises the capacity as well as bits per pixel that can be hidden in the image compared to existing bit flipping method. The image steganographic parameters such as, peak signal to noise ratio (PSNR), hiding capacity, and the quality index of the proposed techniques has been compared with the existing bit flipping technique</p>

scholarly journals Adaptive Matrix Pattern Steganography on RGB Images

2021 ◽  
Author(s):  
Amirfarhad Nilizadeh ◽  
Shirin Nilizadeh ◽  
Wojciech Mazurczyk ◽  
Cliff Zou ◽  
Gary T. Leavens
Keyword(s):  
Digital Media ◽  
Signal To Noise Ratio ◽  
Spatial Domain ◽  
Image Steganography ◽  
Image Block ◽  
Hiding Capacity ◽  
Ascii Character ◽  
Blue Channel ◽  
Rgb Images ◽  
Almost All

Almost all spatial domain image steganography methods rely on modifying the Least Significant Bits (LSB) of each pixel to minimize the visual distortions. However, these methods are susceptible to LSB blind attacks and quantitative steganalyses. This paper presents an adaptive spatial domain image steganography algorithm for hiding digital media based on matrix patterns, named “Adaptive Matrix Pattern” (AMP). The AMP method increases the security of the steganography scheme of largely hidden messages since it adaptively generates a unique codebook matrix pattern for each ASCII character in each image block. Therefore, each ASCII character gets a different codebook matrix pattern even in different regions of the same image. Moreover, it uses a preprocessing algorithm to identify the most suitable image blocks for hiding purposes. The resulting stego-images are robust against LSB blind attacks since the middle bits of green and blue channels generate matrix patterns and hiding secrets, respectively. Experimental results show that AMP is robust against quantitative steganalyses. Additionally, the quality of stego-images, based on the peak signal-to-noise ratio metric, remains high in both stego-RGB-image and in the stego-blue-channel. Finally, the AMP method provides a high hiding capacity, up to 1.33 bits per pixel.

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

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.

scholarly journals Phase Clustering Based Modulation Classification Algorithm for PSK Signal over Wireless Environment

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Qi An ◽  
Zi-shu He ◽  
Hui-yong Li ◽  
Yong-hua Li
Keyword(s):  
Frequency Estimation ◽  
Signal To Noise Ratio ◽  
Frequency Offset ◽  
Estimation Accuracy ◽  
Modulation Classification ◽  
Electronic Warfare ◽  
Wireless Communication Network ◽  
Shift Keying ◽  
Analytical Formulas ◽  
Fractional Function

Promptitude and accuracy of signals’ non-data-aided (NDA) identification is one of the key technology demands in noncooperative wireless communication network, especially in information monitoring and other electronic warfare. Based on this background, this paper proposes a new signal classifier for phase shift keying (PSK) signals. The periodicity of signal’s phase is utilized as the assorted character, with which a fractional function is constituted for phase clustering. Classification and the modulation order of intercepted signals can be achieved through its Fast Fourier Transform (FFT) of the phase clustering function. Frequency offset is also considered for practical conditions. The accuracy of frequency offset estimation has a direct impact on its correction. Thus, a feasible solution is supplied. In this paper, an advanced estimator is proposed for estimating the frequency offset and balancing estimation accuracy and range under low signal-to-noise ratio (SNR) conditions. The influence on estimation range brought by the maximum correlation interval is removed through the differential operation of the autocorrelation of the normalized baseband signal raised to the power ofQ. Then, a weighted summation is adopted for an effective frequency estimation. Details of equations and relevant simulations are subsequently presented. The estimator proposed can reach an estimation accuracy of10-4even when the SNR is as low as-15 dB. Analytical formulas are expressed, and the corresponding simulations illustrate that the classifier proposed is more efficient than its counterparts even at low SNRs.

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