# A Safe and Secured Medical Textual Information Using an Improved LSB Image Steganography

Authors: Roseline Oluwaseun Ogundokun; Oluwakemi Christiana Abikoye
Journal: International Journal of Digital Multimedia Broadcasting
 (2021)

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## Abstract

Safe conveyance of medical data across unsecured networks nowadays is an essential issue in telemedicine. With the exponential growth of multimedia technologies and connected networks, modern healthcare is a huge step ahead. Authentication of a diagnostic image obtained from a specialist at a remote location which is from the sender is one of the most challenging tasks in an automated healthcare setup. Intruders were found to be able to efficiently exploit securely transmitted messages from previous literature since the algorithms were not efficient enough leading to distortion of information. Therefore, this study proposed a modified least significant bit (LSB) technique capable of protecting and hiding medical data to solve the crucial authentication issue. The application was executed and established by utilizing MATLAB 2018a, and it used a logical bit shift operation for execution. The investigational outcomes established that the proposed technique can entrench medical information without leaving a perceptible falsification in the stego image. The result of this implementation shows that the modified LSB image steganography outperformed the standard LSB technique with a higher PSNR value and lower MSE value when compared with previous research works. The number of shifts was added as a new performance metric for the proposed system. The study concluded that the proposed secured medical information system was evidenced to be proficient in secreting medical information and creating undetectable stego images with slight entrenching falsifications when likened to other prevailing approaches.


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## Body

## 1. Introduction

The advancement of information and communication technologies in recent ages has made the delivery of digital information further relevant. It is necessary for various divisions, for instance, government, education, banking, and healthcare, where the Internet has to turn out to be the bedrock for data exchange and distribution [1–5]. The medical industries mainly use the internet to promote remote sharing between hospitals and clinics of digital medical information and to provide patients with e-health facilities [6, 7]. Access to medical records allows patient data to be shared between multiple physicians and provides successful remote diagnosis [7–11]. For the detection and management of several ailments [7, 12–14], digital medical images are necessary and, thus, it is highly critical to ensure safe recording, retrieval, and review of medical information without breaching the Code of Ethics for Health Information Practitioners. The Hospital Information System (HIS) and the Patient Archiving and Communication System (PACS) form the basis of the Digital Imaging and Communication in Medicine (DICOM) standard for digital hospital systems [15–18]. DICOM has been the world standard for treating, preserving, publishing, and distributing medical imaging and related information since its establishment in 1993 [19]. It specifies the format for medical images that can be shared for therapeutic use with the data required. DICOM was first implemented without consideration for network security or privacy safety [17, 18, 20]. Several strategies for preserving diagnostic photographs and health data have recently been implemented [6, 21, 22]. These approaches rely on encryption or information hiding techniques for secure communication.Information hiding is the technique of inserting information into another medium for safe transfer. It has a broad variety of features, including copyright enforcement, tamper detection, and hidden data transfer [23]. In general, according to the purposes for which information hiding is used, information hiding techniques can be divided into two, and they are steganography and watermarking. Steganography is the practice of hiding the presence of a hidden message inside other media, for instance, text, image, audio, and video without causing unintended awareness and at the same time attaining a high entrenching potential [21] while watermarking approaches are employed to validate the identification and validity of the digital image holders by inserting distinct material such as a signature into the host medium [24]. There has been a surge of interest in incorporating these techniques like [1, 25–28] in recent years. Based on the domain type, steganography methods can be divided into two: spatial and transform [21, 28, 29]. In the cover image, spatial domain procedures entrench the hidden data directly. On the other hand, transforming domain procedures embed the data after transforming the image of the cover into another domain [30, 31]. Spatial domain algorithms such as LSB take less time to perform and have a higher embedding performance [29, 32].Therefore, as a result of this research, a steady medical information system based on a modified least significant bit (LSB) algorithm was suggested. The proposed system was introduced in the programming environment of MATLAB 2018a. This functions by moving the LSB of the red (R), green (G), and blue (B) of concealed object pixel components to the given number of times the sender determines. The bits of the undisclosed message would then be substituted for the moving bits.The remainder of the article is written as follows: Section2 discussed the related works in this field of study. Section 3 presented the proposed modified LSB image steganography, and both the embedding and extracting algorithms were outlined and discussed. Section 4 addressed the study proposed prototype. Performance analysis of the system was likewise presented, and discussion on this comparative analysis was presented in this section. Lastly, Section 5 concluded the study.


## 2. Related Works

The most popular spatial steganography approach is the least significant bit (LSB) technique [33, 34]. This LSB technique is proficient in entrenching comparatively huge undisclosed data in a concealment object [35] by substituting the LSBs of the concealment object pixels with the hidden data bits [36, 37]. In the field of medical textual information hiding, several approaches have been suggested for various goals [38–42]. In recent years, different mechanisms of image steganography have been implemented [43–57]. Some of the literature that has utilized image steganography are discussed as thus.Asad and Shayeb [58] proposed the improvement of the Least Significant Digit (LSD). In this research work, the authors implemented the use of the Least Significant Digit (LSD) Digital Watermark Technique and at the same time, the method of optimizing preference is employed. The exact reliability was not attained through the unsystematic choosing of pixel’s value. Optimizing preference lessens the quantity of pixel value that was altered. The digital watermark and digital cover image were in grayscale. The handling domain was in the spatial domain. In 2017, Jung [59] conducted a comparative investigation of information hiding, and the study was based on least significant bit. The diverse implanting proportion had been introduced in numerous varieties of research work. The performance and juxtaposition of image evaluation and histogram evaluation for each one of the pixel layers were conducted in the article. The authors presented that the data hiding approach with individual bpp implanting proportion was strenuous to differentiate between the cover and stego objects. An innovative structure aimed at the rightfulness of optical constituents employing steganography was postulated by Muhammad, Ahmad, Rho, and Baik [60] in 2017. The study made use of the gleaming level surface of contributed representation for concealed records by utilizing Morton scrutinizing the precise least significant bits replacement technique. The undisclosed records were scrambled employing a trio-parallel encoded process preceding implanting, and this led to enhancing an extra steady safety for validation. Al-Shatanawi [61] suggested a generation of a procedure centered upon dual methodologies. These dual methodologies are presented as Distinctive Magnitude Picture Divisions (DMPD) as well as Amended Smallest Substantial (ASS) pieces. The DMPD was administered upon protected representation towards inserting undisclosed representation arbitrarily. This procedure interchanged the number of portions in inconsistent means, whereas it delivers little distortion on the concealed representation. Laskar and Hemachandran [62] recommended a technique in information hiding. In the study, records are implanted within the reddish level surface of the representation, and the constituent is chosen utilizing an arbitrary integer originator. It is nearly not possible to observe the alteration in the concealment image. To single out the pixel locations, a stego password was employed to root the artificial arbitrary figure originator. The authors’ objectives focused on shooting up the confidentiality of the communication and lessen misrepresentation proportion or level. Jung [63] recommended powerful magnitude files obscuring the structure. The recommended structure applies the component rate distinguishing along with smallest important part substitution concurrently in the consistent item level towards expanding the entrenching competence. The empirical outcomes indicated that the recommended structure retained 32.61 dB on moderate once the entrenching competence attained 1,052,641 parts. Hence, the recommended structure ensured sturdiness supported by entrenching competence in the absence of misrepresentation to the mortal optical technique.Gutub and Al-Ghamdi [8] postulated multimedia image steganography for optimized counting-based secret sharing. The authors used multimedia image-based steganography methods to store the optimized shares that are providing comparisons for proofed remarks. The paper experiments measure the function of the improvements by assuming various hidden sharing key sizes of 64-bit, 128-bit, and 256-bit to ensure that real differences within the security analysis. The usability of the shares was further enhanced by experimenting with five different image-based steganography techniques to embed each produced share. The findings showed a major enticing impact, making the streamlined counting-based secret sharing scheme a promising approach for security applications for multi-user authentication.Karakis, Guler, Capraz, and Bilir (2015) proposed an innovative fuzzy logic-based image steganography technique to guarantee medical information safety. The authors proposed to protect patients’ records by employing steganographic techniques to merge them into one file format. The electroencephalogram (EEG) was chosen as the concealed information, and MR objects are employed as the concealment object. Furthermore, to the EEG, the message in the image file header is comprised of doctor’s statements and patient information.It was deduced from related works that most systems developed were less robust and lesser quality images produced. Some researchers did not consider the condition of more PSNR and less MSE, and there was distortion in the cover image used in some research thereby giving low qualities of stego image which made it possible for intruders to detect a secured message being communicated. Finally, distortion, robustness, and imperceptibility were not considered in some previous research.Hence, with all these deductions, the study, therefore, is aimed at solving the problems of imperceptibility, less robustness, distortion, and security by modifying the previous standard LSB steganography techniques which are called the circular shift LSB steganography.


## 3. Proposed System

The suggested system for this study is modified least significant bit (LSB) image steganography for entrenching and securing medical information.Figure1 illustrates the embedding stage of the projected system. The medical information is passed into the modified LSB steganography. The cover image and the medical information are loaded into the system after which the modified LSB is used for the embedding process, and the output is a stego image. Figure 2 displays the extraction stage of the system where the stego image is loaded into the system, and then the modified extraction algorithm is implemented on the stego image given output of the original cover image and secured medical information.Algorithm 1: Embedding stage algorithm.
Cover image read cover imagebinMsg read medical information into binarykey read stego keysum∑k=0lengthkeykeykwhile (i=1: length(binMsg))dofor j=1 to sumright circular shift(binMsg(j))end% replace lsb of cover image with binMsgEndAlgorithm 2: Extraction stage algorithm.
stego image read LSB of stego imagekey read stego keysum∑i=0lengthkeykeyiwhile (! End of message)dofor j=1 to sumleft circular shift(extractedMsg(j))end% extracted messageEndFigure 1
Embedding phase of the projected system.Figure 2
Extraction phase of the projected system.The proposed algorithm employs a key that can be in any length and could contain a mixture of letters, numbers, and symbols. A circular shift function was used to maximize the complexity of the message being covered by moving the coded message bits by several steps at each iteration up to the sum of the password length in an ASCII value. The LSB of the concealment object is replaced in stages up to the appropriate quantity of bits to be substituted. The same method is followed to recover the undisclosed code, but by moving the extracted bits to the left by k steps up to the password sum in the ASCII value.


## 4. Our Prototype

MATLAB was used by the authors to execute the proposed algorithms where two-dimensional cover images were used to implant medical information from the patient. The system often used the text of varying sizes, and it was deduced that it was difficult to discern between the original concealment object and the stego object when the algorithm was applied. The following interfaces show the outcomes of the algorithm’s implementation.


### 4.1. Embedding Phase of the System

The proposed secured medical information system interface is shown in Figure3.Figure 3
Interface of the system.Figure4 displays the system interface illustrating the patient’s medical information which is in text format and the stego key (known to only the source and recipient) being entered into the system.Figure 4
Interface showing text and stego key entered.The final stage of the embedding phase is shown in Figure5. This shows the initial cover image and the steganographic image (that is image hiding the patient’s information). The PSNR value, MSE value, and the no. of shifts are shown as well.Figure 5
Interface of the embedding result.


### 4.2. Extraction Phase for the System

The interface of the extraction phase is shown in Figure6. Here, the recipient entered the secret stego key and then have access to the stego image.Figure 6
Interface of the extraction phase.The concealed medical information and the initial cover image are revealed after the modified extraction LSB algorithm is implemented as shown in Figure7.Figure 7
Interface of the extraction phase.


### 4.3. Performance Analysis

To evaluate the performance of the proposed system, the authors compared the PSNR and MSE of the modified system with the existing methods. For numerical analysis, the system employed MATLAB and also added the number of LSB shifts as a performance metric. Table1 shows and describes the evaluation of the projected scheme using PSNR, MSE, and number of shifts as the evaluation metrics. The study evaluates the system by computing the peak signal-to-noise (PSNR) and mean squared error (MSE). The PSNR mathematical equation is shown in Eq. (1) and the MSE in Eq. (2), respectively.
(i)
Peak signal-to-noise ratio (PSNR): to avoid suspicion, the quality of the stego image and the cover image must be the same. The difference in quality will be measured using PSNR. The higher the PSNR, the higher the quality of the stego image. PSNR could be calculated using Eq. (1):(1)PSNR=10log2552MSE.(ii)
MSE which is the mean square error will be calculated using the equation:(2)MSE=1mn∑0m−1∑0n−1c−s2.Table 1
Evaluation of the proposed system.
SamplesNumber of stego key charactersPSNRMSENo. of shifts1879.14730.000791884102580.3640.00059832640


## 4.1. Embedding Phase of the System

The proposed secured medical information system interface is shown in Figure3.Figure 3
Interface of the system.Figure4 displays the system interface illustrating the patient’s medical information which is in text format and the stego key (known to only the source and recipient) being entered into the system.Figure 4
Interface showing text and stego key entered.The final stage of the embedding phase is shown in Figure5. This shows the initial cover image and the steganographic image (that is image hiding the patient’s information). The PSNR value, MSE value, and the no. of shifts are shown as well.Figure 5
Interface of the embedding result.


## 4.2. Extraction Phase for the System

The interface of the extraction phase is shown in Figure6. Here, the recipient entered the secret stego key and then have access to the stego image.Figure 6
Interface of the extraction phase.The concealed medical information and the initial cover image are revealed after the modified extraction LSB algorithm is implemented as shown in Figure7.Figure 7
Interface of the extraction phase.


## 4.3. Performance Analysis

To evaluate the performance of the proposed system, the authors compared the PSNR and MSE of the modified system with the existing methods. For numerical analysis, the system employed MATLAB and also added the number of LSB shifts as a performance metric. Table1 shows and describes the evaluation of the projected scheme using PSNR, MSE, and number of shifts as the evaluation metrics. The study evaluates the system by computing the peak signal-to-noise (PSNR) and mean squared error (MSE). The PSNR mathematical equation is shown in Eq. (1) and the MSE in Eq. (2), respectively.
(i)
Peak signal-to-noise ratio (PSNR): to avoid suspicion, the quality of the stego image and the cover image must be the same. The difference in quality will be measured using PSNR. The higher the PSNR, the higher the quality of the stego image. PSNR could be calculated using Eq. (1):(1)PSNR=10log2552MSE.(ii)
MSE which is the mean square error will be calculated using the equation:(2)MSE=1mn∑0m−1∑0n−1c−s2.Table 1
Evaluation of the proposed system.
SamplesNumber of stego key charactersPSNRMSENo. of shifts1879.14730.000791884102580.3640.00059832640


## 5. Discussion

The study performance was evaluated using the two most common steganography performance metrics which are PSNR and MSE. It was noticed that the PSNR for fewer text characters is higher than the text with higher characters. Table1 shows that a text with 8 characters has a PSNR of 79.147 and MSE of 0.000791, and a text with 5 characters has a PSNR value of 80.364 and an MSE value of 0.000598. Table 2 displays the comparative examination of the projected system with other previous researches. Abd-El-Atty et al. [54] have a PSNR value of 73.27 and did not use MSE for evaluation, and Hashim et al. [12] have a PSNR value of 72.29 and also did not use MSE for their performance evaluation. Setyono and Setiadi [64] got a PSNR value of 63.52 and a MSE value of 0.0289. The proposed system had a PSNR value of 80.36 and an MSE value of 0.00060 which shows that the proposed modified LSB outperformed previous researches as seen in Figure 8. Table 3 shows the parameter values for the proposed system, and it was discovered that the proposed system security level is high, the MSE value is low, PSNR value is high, and it is highly robust and also possessed a high imperceptibility.Table 2
Comparison with other LSB steganography technique.
AuthorsPSNRMSEAbd-El-Atty et al. [54]73.27—Hashim et al. [12]72.29—Setyono and Setiadi [64]63.520.0289The proposed system, 202080.360.00060Figure 8
Comparative analysis with previous researches.Table 3
Parameter values of the proposed method.
FeaturesProposedSecurityHighMSELowPSNRHighRobustnessHighImperceptiblyHigh


## 6. Conclusions

The protection of patient data in the digital medical system is the focus of this research. The research presented an important information entrenching system for maintaining the confidentiality and privacy of patient information by concealing its presence. This study suggested a modified least significant bit image steganography technique. The system was implemented using MATLAB, and two performance metrics were used to evaluate the proposed system which includes PSNR and MSE. The number of shifts was added as well. Investigational findings showed that with a low degree of embedding distortion, the proposed algorithm obtained a high embedding rate and thus offered a reasonable balance between concealment and stego image quality.Therefore, the proposed secured medical information system is evidenced to be proficient in secreting medical information and creating undetectable stego images with slight entrenching falsifications when likened to other prevailing approaches.



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