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

Shtayt, Bushra Abdullah; Zakaria, Nur Haryani; Harun, Nor Hazlyna

doi:10.21123/bsj.2021.18.2(suppl.).0957

Cited by 8 publications

(6 citation statements)

References 81 publications

(78 reference statements)

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“…Comparison of pixel statistics with those of the background model helps to identify foreground pixels. This method is becoming more popular because of its dependability in scenarios with noise, shifting light, and shadows of any kind 6 . However, the current work is as an attempt to embed sensitive data inside the digital video, based on the technology of detecting the moving object in the video.…”

Section: Figure 1 Different Kinds Of Cover Mediums For Steganographymentioning

confidence: 99%

Building a Statistical Model to Detect Foreground Objects and using it in Video Steganography

2023

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Video steganography has become a popular option for protecting secret data from hacking attempts and common attacks on the internet. However, when the whole video frame(s) are used to embed secret data, this may lead to visual distortion. This work is an attempt to hide sensitive secret image inside the moving objects in a video based on separating the object from the background of the frame, selecting and arranging them according to object's size for embedding secret image. The XOR technique is used with reverse bits between the secret image bits and the detected moving object bits for embedding. The proposed method provides more security and imperceptibility as the moving objects are used for embedding, so it is difficult to notice the changes in the moving objects instead of using background area for embedding in the video. The experimental results showed the better visual quality of the stego video with PSNR values exceeding 58 dB, this indicates that the proposed method works without causing much distortion in the original video and transmitted secret message.

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Section: Figure 1 Different Kinds Of Cover Mediums For Steganographymentioning

confidence: 99%

Building a Statistical Model to Detect Foreground Objects and using it in Video Steganography

2023

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“…The above surveys on data hiding in digital media are dedicated to either digital audio, images, videos, or 3D media while LSB data hiding is covered as one among numerous other approaches. Several conference papers providing surveys that focus on LSB data hiding have been provided as [41][42][43][44][45][46][47]. To the best of our knowledge, a comprehensive survey on LSB data hiding that considers digital audio, images, videos, and 3D media does not exist.…”

Section: Digitalmentioning

confidence: 99%

LSB Data Hiding in Digital Media: A Survey

Tran¹,

Zepernick²,

Chu³

2022

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Data hiding or information hiding is a prominent class of information security that aims at reliably conveying secret data embedded in a cover object over a covert channel. Digital media such as audio, image, video, and three-dimensional (3D) media can act as cover objects to carry such secret data. Digital media security has acquired tremendous significance in recent years and will be even more important with the development and delivery of new digital media over digital communication networks. In particular, least significant bit (LSB) data hiding is easy to implement and to combine with other hiding techniques, offers high embedding capacity for data, can resist steganalysis and several types of attacks, and is well suited for real-time applications. This article provides a comprehensive survey on LSB data hiding in digital media. The fundamental concepts and terminologies used in data hiding are reviewed along with a general data hiding model. The five attributes of data hiding, i.e., capacity, imperceptibility, robustness, detectability, and security, and the related performance metrics used in this survey to compare the characteristics of the different LSB data hiding methods are discussed. Given the classification of data hiding methods with respect to audio, image, video, and 3D media, a comprehensive survey of LSB data hiding for each of these four digital media is provided. In particular, landmark studies, state-of-the-art approaches, and applications of LSB data hiding are described for each of the four digital media. Their performance is compared with respect to the data hiding attributes which illustrates benefits and drawbacks of the reviewed LSB data hiding methods. The article concludes with summarizing main findings and suggesting directions for future research. This survey will be helpful for researchers and practitioners to keep abreast about the potential of LSB data hiding in digital media and to develop novel applications based on suitable performance trade-offs between data hiding attributes.

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“…Medical imaging today plays a significant role in many areas of medicine; it makes it possible and simple to acquire, transmit, and analyze medical images while also aiding in medical diagnosis. Medical imaging is continually growing with the addition of novel imaging modalities and ongoing device capabilities advancements [2][3][4] . Cataract is an age-related eye disease and is the most prevalent ophthalmological public health issue in both industrialized and developing countries.…”

Section: Introductionmentioning

confidence: 99%

An Automated Wavelet Scattering Network Classification Using Three Stages of Cataract Disease

Al-Saadi,

Khdiar,

Al-Saadi

2024

Baghdad Sci.J

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A cataract is an eye disease that causes visual distortion and the late stage of this disease can lead to blindness. It is considered a silent disease that can occur without the appearance of symptoms. Therefore, the most effective way to detect cataracts is through accurate and timely detection to prevent hurting, expensive operations, and to stop blindness. The purpose of this paper is to propose an automated system based on the wavelet scattering network which categorizes the patients into four classes: early cataract, intermediate cataract, late cataract, and non-cataract conditions using 512 images of the ODIR dataset (212 Cataract and 300 of Normal). The first step in this technique is the preprocessing step for the retinal image was a mean filter, which was utilized to reduce the image's noise. The limited contrast adaptive histogram equalization (CLAHE) method was then employed to improve the image's contract level. Then, Low-variance characteristics can be extracted from image data using a wavelet scattering network for use in deep learning applications. In this network, lowpass scaling filters and predefined wavelets are employed. The average accuracy of the suggested method was 100% for four classes (Normal, Early, Moderate, and Severe). The results are promising compared with other similar works.

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

Cited by 8 publications

References 81 publications

Building a Statistical Model to Detect Foreground Objects and using it in Video Steganography

Building a Statistical Model to Detect Foreground Objects and using it in Video Steganography

LSB Data Hiding in Digital Media: A Survey

An Automated Wavelet Scattering Network Classification Using Three Stages of Cataract Disease

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