Hiding medical information in brain MR images without affecting accuracy of classifying pathological brain

Devi, S. Nirmala; Sahoo, Manmath Narayan; Muhammad, Khan; Ding, Weiping; Bakshi, Sambit

doi:10.1016/j.future.2019.01.047

Cited by 24 publications

(30 citation statements)

References 38 publications

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“…Moreover, the entropy of cover images and their corresponding stego‐images are nearly the same. In Section 4, we also show that our scheme outperforms existing compression based steganography schemes [6, 12–14, 16–19].…”

Section: Introductionmentioning

confidence: 79%

“…In this table, we do not compare these results for all the images [6,[12][13][14][16][17][18], and [19], respectively. Here, we can see that our proposed scheme has a higher embedding capacity compared to all schemes except the one, which is [6]. The reason for this is that this scheme is based on embedding secret data in the spatial domain.…”

Section: Performance Comparisonmentioning

confidence: 99%

“…It is combinatorial and an NP-hard problem [39]. The other approach is to substitute the 𝓁 0 − norm by the closest convex norm, that is the 𝓁 1 − norm, or min s ‖s‖ 1 Subject to ΦΨs = y, (6) where ‖ ⋅ ‖ 1 denotes the 𝓁 1 − norm of a vector. The approach to reconstruct the sparse signal s by solving the above equation is termed as a convex optimization method.…”

Section: Reconstruction Of the Approximate Signalmentioning

confidence: 99%

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CSIS: Compressed sensing‐based enhanced‐embedding capacity image steganography scheme

Agrawal

Ahuja

2021

IET Image Processing

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Image steganography plays a vital role in securing secret data by embedding it in the cover images. Usually, these images are communicated in a compressed format. Existing techniques achieve this but have low embedding capacity. Hence, the goal here is to enhance the embedding capacity while preserving the visual quality of the stego-image. It is also intended to ensure that the scheme is resistant to steganalysis attacks. This paper proposes a compressed sensing image steganography (CSIS) scheme to achieve these goals. In CSIS, the cover image is sparsified block-wise, linear measurements are obtained, and then permissible measurements are selected. Next, the secret data is encrypted, and 2 bits of this encrypted data are embedded into each permissible measurement. For the reconstruction of the stego-image, ADMM and LASSO are used for the resultant optimization problem. Experiments are performed on several standard greyscale images and a colour image. Higher embedding capacity, 1.53 times more compared to the most recent scheme, is achieved. An average of 37.92 dB PSNR value, and average values close to 1 for both the mean SSIM index and the NCC coefficients are obtained, which is considered good. These metrics show that CSIS substantially outperforms existing similar steganography schemes. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 79%

Section: Performance Comparisonmentioning

confidence: 99%

Section: Reconstruction Of the Approximate Signalmentioning

confidence: 99%

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CSIS: Compressed sensing‐based enhanced‐embedding capacity image steganography scheme

Agrawal

Ahuja

2021

IET Image Processing

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“…The empirical outcomes displayed big potential in security, embedding capacity, and image quality recovery. Devi et al proposed a novel method to validate the impact of steganography on T2-weighted MR images to distinguish a normal brain from a pathological brain (102). The key goal of the proposed method was to conceal personal data during the processing of patients' medical data.…”

Section: Least Significant Bit (Lsb)mentioning

confidence: 99%

A Comprehensive Review on Medical Image Steganography Based on LSB Technique and Potential Challenges

2021

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The rapid development of telemedicine services and the requirements for exchanging medical information between physicians, consultants, and health institutions have made the protection of patients’ information an important priority for any future e-health system. The protection of medical information, including the cover (i.e. medical image), has a specificity that slightly differs from the requirements for protecting other information. It is necessary to preserve the cover greatly due to its importance on the reception side as medical staff use this information to provide a diagnosis to save a patient's life. If the cover is tampered with, this leads to failure in achieving the goal of telemedicine. Therefore, this work provides an investigation of information security techniques in medical imaging, focusing on security goals. Encrypting a message before hiding them gives an extra layer of security, and thus, will provide an excellent solution to protect the sensitive information of patients during the sharing of medical information. Medical image steganography is a special case of image steganography, while Digital Imaging and Communications in Medicine (DICOM) is the backbone of all medical imaging divisions, whereby it is most broadly used to store and transmit medical images. The main objective of this study is to provide a general idea of what Least Significant Bit-based (LSB) steganography techniques have achieved in medical images.

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“…Data concealment methods often manipulate the least significant bits (LSBs) in images to embed data imperceptibly. In [6], LSB substitution based spatial domain steganography is applied to the medical images. Contrary to LSB techniques, the intermediate significant bit substitution is used in [7].…”

Section: Related Workmentioning

confidence: 99%

Algorithm of Information Embedding into Digital Images Based on the Chinese Remainder Theorem for Data Security

Evsutin

Dzhanashia

2020

Cryptography

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With the huge transfers of data involved in the modern world, it is both crucial and challenging to maintain the security of data. This paper proposes a novel algorithm of information embedding into digital images that could be used to protect confidential information. The presented algorithm makes use of the Chinese remainder theorem and adaptive embedding to achieve good imperceptibility along with the possibility of hiding a decent amount of confidential information. The algorithm is evaluated via computing experiments and evaluation results, as well as comparison with similar works, demonstrate good imperceptibility qualities of the proposed scheme.

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Hiding medical information in brain MR images without affecting accuracy of classifying pathological brain

Cited by 24 publications

References 38 publications

CSIS: Compressed sensing‐based enhanced‐embedding capacity image steganography scheme

CSIS: Compressed sensing‐based enhanced‐embedding capacity image steganography scheme

A Comprehensive Review on Medical Image Steganography Based on LSB Technique and Potential Challenges

Algorithm of Information Embedding into Digital Images Based on the Chinese Remainder Theorem for Data Security

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