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.

scholarly journals A Juxtaposition Between Integer Wavelet Transform and Discrete Wavelet Transform for Secure Image Steganography

2020 ◽  
Vol 9 (5) ◽  
pp. 1019-1023
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Cover Image ◽  
Discrete Wavelet ◽  
Integer Wavelet Transform ◽  
Stego Image ◽  
Hiding Capacity ◽  
Integer Wavelet

Image steganography is a technique that is used to hide information. The information can be of various types like image, video, or audio. Steganography is done so that no one apart from the correct receiver can retrieve the information. This paper consists of all advantages and highlights of the wavelet transform but with the additional features like randomness and some default values that are already built-in it. Various algorithms can be used in steganography and they provide good hiding capacity and low detectability. Here we have hidden the image into the cover image using Integer Wavelet Transform (IWT) and also using Discrete Wavelet Transform (DWT) and compared which technique gives better results. It is very difficult to predict the presence of a hidden image inside the stego image since it looks exactly like the cover image. There is no loss in quality from the secret image to the extracted image since the PSNR (Peak Signal to noise ratio) is high for both of them. This process was done using both DWT and IWT and the results prove that that the IWT technique is not only simpler but also more efficient than the DWT technique since it gives higher PSNR values. Through the proposed algorithm, an increase in the strength and imperceptibility is noticed and it can also maintain various transformations such as scaling, translation, and rotation with algorithms that already exist. The final results, after comparing both the transforms prove that the algorithm which is being proposed in IWT is indeed effective

scholarly journals High-Capacity Image Steganography Algorithm Based on Image Style Transfer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xinliang Bi ◽  
Xiaoyuan Yang ◽  
Chao Wang ◽  
Jia Liu
Keyword(s):  
High Capacity ◽  
Image Features ◽  
Image Steganography ◽  
Cover Image ◽  
Secret Message ◽  
The Internet ◽  
Stego Image ◽  
Style Transfer ◽  
Secret Image ◽  
Secret Information

Steganography is a technique for publicly transmitting secret information through a cover. Most of the existing steganography algorithms are based on modifying the cover image, generating a stego image that is very similar to the cover image but has different pixel values, or establishing a mapping relationship between the stego image and the secret message. Attackers will discover the existence of secret communications from these modifications or differences. In order to solve this problem, we propose a steganography algorithm ISTNet based on image style transfer, which can convert a cover image into another stego image with a completely different style. We have improved the decoder so that the secret image features can be fused with style features in a variety of sizes to improve the accuracy of secret image extraction. The algorithm has the functions of image steganography and image style transfer at the same time, and the images it generates are both stego images and stylized images. Attackers will pay more attention to the style transfer side of the algorithm, but it is difficult to find the steganography side. Experiments show that our algorithm effectively increases the steganography capacity from 0.06 bpp to 8 bpp, and the generated stylized images are not significantly different from the stylized images on the Internet.

scholarly journals Image Steganography Using Pixel Value Differencing (PVD) Technique Based on Firefly Algorithm

2021 ◽  
pp. 80-86
Author(s):  
Oluwaseun M. Alade ◽  
Elizabeth A. Amusan ◽  
Oluyinka T. Adedeji ◽  
Oluwaseun O. Alo
Keyword(s):  
Firefly Algorithm ◽  
Promising Result ◽  
Signal To Noise Ratio ◽  
Text Message ◽  
Image Steganography ◽  
Cover Image ◽  
Experimental Result ◽  
Stego Image ◽  
Pixel Value ◽  
Pixel Value Differencing

Steganography deals with the ways of hiding communicated data in such a way that it remains confidential. Finding best position inside cover image to embed text message, maintaining a reasonable trade-off between security, robustness, higher bit embedding rate and imperceptibility are some of the challenges of steganography system. Hence, this paper presents firefly algorithm for finding best positions inside cover image in order to embed text message into cover image using Pixel Value Differencing (PVD) technique. Four different cover image was used. Experimental result showed the cover image with selected location using firefly algorithm as well as the stego image using PVD technique. The stego image was evaluated using Peak Signal to Noise Ratio (PSNR) and Mean square Error (MSE).  Firefly Algorithm with PVD technique produced a promising result for image steganography.

scholarly journals High-Capacity Image Steganography Based on Improved Xception

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7253
Author(s):  
Xintao Duan ◽  
Mengxiao Gou ◽  
Nao Liu ◽  
Wenxin Wang ◽  
Chuan Qin
Keyword(s):  
Network Architecture ◽  
Signal To Noise Ratio ◽  
Load Capacity ◽  
High Capacity ◽  
Structural Similarity ◽  
Image Steganography ◽  
Cover Image ◽  
Secret Image ◽  
Multi Scale ◽  
High Load Capacity

The traditional cover modification steganography method only has low steganography ability. We propose a steganography method based on the convolutional neural network architecture (Xception) of deep separable convolutional layers in order to solve this problem. The Xception architecture is used for image steganography for the first time, which not only increases the width of the network, but also improves the adaptability of network expansion, and adds different receiving fields to carry out multi-scale information in it. By introducing jump connections, we solved the problems of gradient dissipation and gradient descent in the Xception architecture. After cascading the secret image and the mask image, high-quality images can be reconstructed through the network, which greatly improves the speed of steganography. When hiding, only the secret image and the cover image are cascaded, and then the secret image can be embedded in the cover image through the hidden network in order to obtain the secret image. After extraction, the secret image can be reconstructed by bypassing the secret image through the extraction network. The results show that the results that are obtained by our model have high peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), and the average high load capacity is 23.96 bpp (bit per pixel), thus realizing large-capacity image steganography surgery.

scholarly journals Nested Two-Layer RGB Based Reversible Image Steganography Method

2021 ◽  
Vol 50 (2) ◽  
pp. 264-283
Author(s):  
Ali Durdu
Keyword(s):  
Secure Communication ◽  
Signal To Noise Ratio ◽  
Quality Criterion ◽  
Image Steganography ◽  
Cover Image ◽  
Signal To Noise ◽  
Chi Square ◽  
Hiding Capacity ◽  
Communication Environment ◽  
Steganalysis Method

In this study, a new reversible image steganography method based on Red-Green-Blue (RGB) which hides thecolored image into the colored images in two layers nested is proposed. The proposed method hides the 24-bitimage to be hidden by hiding two layers of data firstly in the resized version of the cover image with the LSBmethod, and then hides the resized cover image to the original cover image with the 4-bit method. The proposedmethod offers a secure communication environment as it hides the hidden image in two layers. When thirdparties extract data by using the LSB method, they only access the resized version of the cover image. The 4-bitmethod divides the image to be hidden into 8-bit segments. While the first 4 bits, which are the most importantbits of 8-bit data, are hidden directly, 4 bits that can be neglected with less significance are completed by roundingat approximate value through the method function. In this way, since the 8-bit data is reduced to 4-bits, themethod performs lossy hiding, but doubles the hiding capacity. Peak signal to noise ratio (PSNR), structuralsimilarity quality criterion (SSIM) and chi-square steganalysis method, which are frequently used in the literature,are used to measure the immunity level of the proposed method. When it is concealed at the same ratewith the LSB method and the proposed method, a higher measurement value is obtained in the PSNR imagecriterion, which is 1.2 dB, SSIM 0.0025, BER 0.0129 and NCC image criterion 0.00027. In additional, it wasshown that the proposed method achieved more successful results in chi-square steganalysis and histogramtests compared to the traditional LSB method.

scholarly journals Image Steganography using Improved Lsb-Mapping Technique with Enhanced Recovery Speed

2019 ◽  
Vol 8 (4) ◽  
pp. 11473-11478
Keyword(s):  
Data Hiding ◽  
Enhanced Recovery ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Cover Image ◽  
Mapping Technique ◽  
Secret Data ◽  
Logical Function ◽  
Hiding Capacity ◽  
Carrier Image

In recent days, for sending secret messages, we require secure internet. Image steganography is considered as the eminent tool for data hiding which provides better security for the data transmitted over internet. In the proposed work, the payload data is embedded using improved LSB-mapping technique. In this approach, two bits from each pixel of carrier image are considered for mapping and addition. Two bits of payload data can be embedded in one cover image pixel hence enhanced the hiding capacity. A logical function on addition is applied on 1st and 2nd bits of cover image pixel, and a mapping table is constructed which gives solution for data hiding and extraction. Simple addition function on stego pixel is performed to extract payload data hence increases the recovery speed. Here the secret data is not directly embedded but instead mapped and added with a number using modulo-4 strategy. Hence the payload data hidden using proposed approach provide more security and it can resist against regular LSB decoding approaches. The proposed work is implemented and tested for several gray scale as well as color images and compared with respect to parameters like peak signal to noise ratio and MSE. The proposed technique gives better results when compared and histogram of cover and stego images are also compared.

scholarly journals High Security and Capacity of Image Steganography for Hiding Human Speech Based on Spatial and Cepstral Domains

2020 ◽  
Vol 8 (1) ◽  
pp. 95
Author(s):  
Yazen A. Khaleel
Keyword(s):  
Speech Signal ◽  
Vocal Tract ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Mel Frequency Cepstral Coefficients ◽  
Secret Data ◽  
Stego Image ◽  
Hiding Capacity ◽  
Original Speech

A new technique of hiding a speech signal clip inside a digital color image is proposed in this paper to improve steganography security and loading capacity. The suggested technique of image steganography is achieved using both spatial and cepstral domains, where the Mel-frequency cepstral coefficients (MFCCs) are adopted, as very efficient features of the speech signal. The presented technique in this paper contributes to improving the image steganography features through two approaches. First is to support the hiding capacity by the usage of the extracted MFCCs features and pitches extracted from the speech signal and embed them inside the cover color image rather than directly hiding the whole samples of the digitized speech signal. Second is to improve the data security by hiding the secret data (MFCCs features) anywhere in the host image rather than directly using the least significant bits substitution of the cover image. At the recovering side, the proposed approach recovers these hidden features and using them to reconstruct the speech waveform again by inverting the steps of MFCCs extraction to recover an approximated vocal tract response and combine it with recovered pitch based excitation signal. The results show a peak signal to noise ratio of 52.4 dB of the stego-image, which reflect a very good quality and a reduction ratio of embedded data to about (6%–25%). In addition, the results show a speech reconstruction degree of about 94.24% correlation with the original speech signal.

scholarly journals A secure cloud system for maintaining COVID-19 patient's data using image steganography

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Nandhini Subramanian ◽  
Somaya Al-Maadeed
Keyword(s):  
Deep Learning ◽  
Image Steganography ◽  
Cover Image ◽  
Main Concern ◽  
Stego Image ◽  
Sensitive Data ◽  
Privacy And Security ◽  
Private Patient ◽  
Hiding Capacity ◽  
Smart Healthcare

Background: The COVID-19 pandemic has been life-threatening for many people and as such, a contactless medical system is necessary to prevent the spread of the virus. Smart healthcare systems collect data from patients at one end and process the acquired data at the other end. The cloud is the central point and the communication happens through insecure channels. The main concern, in this case, is the violation of privacy and security as the channel is untrusted. Traditional methods do not provide enough hiding capacity, security, and robustness. This work proposes an image steganography method using the deep learning method to hide the patient's medical images inside an innocent cover image in such a way that they are not visible to human eyes which reduces the suspicions of the presence of sensitive data. Methods: An auto encoder-decoder-based model is proposed with three components: the pre-processing module, the embedding network, and the extraction network. Features from the cover image and the secret images are extracted and fused to reconstruct the stego image. The stego image is then used to extract the ingrained secret image.shows the overall system workflow. Results: Peak Signal-to-Noise Ratio (PSNR) is the evaluation metrics used. The ImageNet dataset was used for training and testing the proposed model.shows the image results of the proposed method. Conclusion: During a COVID-19 screening test, private patient data such as mobile number and Qatari identity card are collected, transferred, and stored through untrusted channels. It is of paramount importance to preserve the privacy, security, and confidentiality of the collected patient records. A secure deep learning-based image steganography method is proposed to secure the sensitive data transferred through untrusted channels in a cloud-based system.

scholarly journals Efficient Steganography Scheme for Color Images based on Wavelets and Chaotic Maps

2020 ◽  
Vol 2 (4) ◽  
pp. 11-20
Author(s):  
Hikmat N. Abdullah ◽  
Sura F. Yousif ◽  
Alejandro A. Valenzuela
Keyword(s):  
Color Image ◽  
Signal To Noise Ratio ◽  
Color Images ◽  
Cover Image ◽  
Discrete Wavelet ◽  
Transform Domain ◽  
Single Image ◽  
Stego Image ◽  
Secret Image ◽  
Chaotic Sequences

In this paper, a combination of spatial domain as well as transformation domain with the aid of chaotic sequences is used to propose an efficient steganography scheme for color images. The transform domain uses Discrete Wavelet Transform (DWT) for embedding the cover and secret images. Chaotic sequences are used for two purposes: first, to scramble the secret color image before hiding. Second, to randomly select the locations of the cover image for embedding. The two images are then merged together into a single image and the stego image is formed by applying IDWT. The secret image is extracted from the stego image without the need to the original cover image. The simulation results are evaluated in terms of Mean Square Error (MSE), correlation, and Peak Signal to Noise Ratio (PSNR) demonstrate that the proposed scheme has better robustness than the previous schemes in the literature in the presence of common image attacks including filtering and noise attacks. The obtained results for maximum PSNR and correlation were 76.8 dB and 99.99% for the stego image while for the extracted secret image were 55.4 dB and 100%.

J 5 — A Data Processing System for Medical Information

1967 ◽  
Vol 06 (01) ◽  
pp. 1-6
Author(s):  
P. Hall ◽  
Ch. Mellner ◽  
T. Danielsson
Keyword(s):  
Medical Records ◽  
Medical Information ◽  
Health Care Systems ◽  
Processing System ◽  
Linear Pattern ◽  
Flexible Form ◽  
Care Systems ◽  
Pattern Recognition Analysis ◽  
Numeric Information

A system for medical information has been developed. The system is a general and flexible one which without reprogramming or new programs can accept any alphabetic and/or numeric information. Coded concepts and natural language can be read, stored, decoded and written out. Medical records or parts of records (diagnosis, operations, therapy, laboratory tests, symptoms etc.) can be retrieved and selected. The system can process simple statistics but even make linear pattern recognition analysis.The system described has been used for in-patients, outpatients and individuals in health examinations.The use of computers in hospitals, health examinations or health care systems is a problem of storing information in a general and flexible form. This problem has been solved, and now it is possible to add new routines like booking and follow-up-systems.

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