A new COVID-19 medical image steganography based on dual encrypted data insertion into minimum mean intensity window of LSB of X-ray scans

Sahoo, Subhaluxmi; Sahoo, Sony Snigdha

doi:10.1109/indicon49873.2020.9342067

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…The authors in [ 143 ] proposed an LSB-based steganography technique for medical images with an encrypted DNA algorithm. The encryption algorithm applied on the patient data in the spatial domain, the proposed algorithm applied on chest x-ray images of the COVID-19 patients.…”

Section: Steganographymentioning

confidence: 99%

Security of medical images for telemedicine: a systematic review

Magdy

Hosny

Ghali

et al. 2022

Multimed Tools Appl

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Recently, there has been a rapid growth in the utilization of medical images in telemedicine applications. The authors in this paper presented a detailed discussion of different types of medical images and the attacks that may affect medical image transmission. This survey paper summarizes existing medical data security approaches and the different challenges associated with them. An in-depth overview of security techniques, such as cryptography, steganography, and watermarking are introduced with a full survey of recent research. The objective of the paper is to summarize and assess the different algorithms of each approach based on different parameters such as PSNR, MSE, BER, and NC.

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Section: Steganographymentioning

confidence: 99%

Security of medical images for telemedicine: a systematic review

Magdy

Hosny

Ghali

et al. 2022

Multimed Tools Appl

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show abstract

“…In [4], by using X Ray scans of images from the dataset of COVID-19, patient information was embedded in a medical image using the LSB steganography technique. Initially, the patient information was converted into a ciphertext using Baconian Cipher Generation before embedding with the medical image.…”

Section: Related Workmentioning

confidence: 99%

QR Steganography for Information Hiding of Patient Record

Subramaniam¹,

Mohd-Isa²,

Yap³

2022

Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)

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In recent research studies, biosignals are used to study the behaviour of a human body function which are useful for medical diagnosis. Biosignals such as electrocardiogram (ECG) signals are used to determine the irregularities in heartbeat meanwhile electroencephalogram (EEG) signal is used to record the brain activity of a patient. This paper aims to put together a mechanism to hide patient details with image of patient medical biosignals using steganography. Patient details are stored in the QR Code meanwhile biosignals that are in 1 dimensional are converted into two-dimensional image. In this process of hiding the patient details and its biosignal, fine details may be lost. Thus, image enhancement process is needed. In this paper, methods such as Local Laplacian filter, Successive Mean Quantization Transform (SMQT) algorithm, Non-Local Means filtering, Bilateral filtering with Gaussian Kernel and Anisotropic Diffusion are used to evaluate the medical image quality of the biosignal. Quantitative metrics are used to evaluate the quality of implementation. The proposed method has given out results of Peak Signal-to-Noise Ratio (PSNR), Mean-Squared Error (MSE), Normalized Cross-Correlation (NCC) that are comparatively well with other established methods. Findings from this paper indicates improvement in the overall image quality of biosignals after extraction from its cover image using the proposed methods.

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Time Efficient Image Encryption-Decryption for Visible and COVID-19 X-ray Images Using Modified Chaos-Based Logistic Map

Bhattacharjee

Gupta

Chatterjee

2022

Appl Biochem Biotechnol

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In this pandemic situation, radiological images are the biggest source of information in healthcare and, at the same time, one of the foremost troublesome sources to analyze. Clinicians now-a-days must depend to a great extent on therapeutic image investigation performed by exhausted radiologists and some of the time analyzed and filtered themselves. Due to an overflow of patients, transmission of these medical data becomes frequent and maintaining confidentiality turns out to be one of the most important aspects of security along with integrity and availability. Chaos-based cryptography has proven a useful technique in the process of medical image encryption. The specialty of using chaotic maps in image security is its capability to increase the unpredictability and this causes the encryption robust. There are large number of literature available with chaotic map; however, most of these are not useful in low-precision devices due to their time-consuming nature. Taking into consideration of all these facts, a modified encryption technique is proposed for 2D COVID-19 images without compromising security. The novelty of the encryption procedure lies in the proposed design which is split into mainly three parts. In the first part, a variable length gray level code is used to generate the secret key to confuse the intruder and subsequently it is used as the initial parameter of both the chaotic maps. In the second part, one-stage image pixels are shuffled using the address code obtained from the sorting transformation of the first logistic map. In the final stage, a complete diffusion is applied for the whole image using the second chaotic map to counter differential and statistical attack. Algorithm validation is done by experimentation with visual image and COVID-19 X-ray images. In addition, a quantitative analysis is carried out to ensure a negligible data loss between the original and the decrypted image. The strength of the proposed method is tested by calculating the various security parameters like correlation coefficient, NPCR, UACI, and key sensitivity. Comparison analysis shows the effectiveness for the proposed method. Implementation statistics shows time efficiency and proves more security with better unpredictability.

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A new COVID-19 medical image steganography based on dual encrypted data insertion into minimum mean intensity window of LSB of X-ray scans

Cited by 4 publications

References 16 publications

Security of medical images for telemedicine: a systematic review

Security of medical images for telemedicine: a systematic review

QR Steganography for Information Hiding of Patient Record

Time Efficient Image Encryption-Decryption for Visible and COVID-19 X-ray Images Using Modified Chaos-Based Logistic Map

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