An image information hiding using adaptation and radix

Tang, Mingwei; Song, Wen; Chen, Xiaoliang; Hu, Jie

doi:10.1016/j.ijleo.2015.07.200

Cited by 17 publications

(9 citation statements)

References 13 publications

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“…In general the secret data were converted into binary notation and then it will be embedded into images; but in the MBNS model the binary secret bits were converted into any one of the following numbers system binary, octal or decimal [33].The embedding pixel is selected by the way of the surrounding pixels in all direction; based on the higher difference between the pixels are used to embed the secret bits. Mostly the edge pixels were commonly used to store more data bits.…”

Section: Multi Base Notation System (Mbns)mentioning

confidence: 99%

Pearl Pixel Steganographic Method for Grayscale Images using Location-Array Method

Raja¹,

Rajalakshmi²

2019

IJEAT

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Nowadays, the user of the internet is growing very fast, in which sends and receiving a messages become very easy using social media applications, meanwhile using these applications, the security is a very big issue. Today providing security for the essential data becomes too hard, intruders become smarter. They are using advanced techniques and models to access our data. For a long period cryptography algorithms were operated to protect the important data, but nowadays these algorithms are easily broken by the intruders. The steganography algorithms are considered as the next generation of cryptography; every user is able to create own algorithms to send and receive the important data. In this method, the secret data will embed into image pixel; many more algorithms are designed by the researcher using this idea. All of these algorithms embed the data into an image and transfer the stego image from one end to another end with stegokey at the receiver end with the help of a stego-key, they do the reverse engineering process in the stego image to get the original data which embedded by the sender side. In most of the algorithms, transfer the stego image and key is a very big concern. Since during the transmission time of stego image and key, anyone can make changes into that; like resize the image or cropping the same. If the receiver gets the damaged version of stego image, they can’t get the original message back. In that circumstances transmission of stego-image with stego-key, it needs more space and time to reach the destination as well as need to pay attention to the security. To overcome these problems, the proposed method does not transfer the stego image, due to which it is not required to compare the image before and after data insertion and no need to calculate the peak signal-noise ratio (PSNR). It shares stego key with the proper security key to recognize if any intruder made an attack on that. This method provides good security for the data

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Section: Multi Base Notation System (Mbns)mentioning

confidence: 99%

Pearl Pixel Steganographic Method for Grayscale Images using Location-Array Method

Raja¹,

Rajalakshmi²

2019

IJEAT

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“…Wu [16] proposed the general pixel-value differencing (GPVD) method based on the PVD [8] method, but the quantization range used is not limited to the power of two. Wu [16] adopted the mechanism of multiple-based number conversion [17][18][19][20][21] to embed secret messages into pixel pairs of images.…”

Section: Introductionmentioning

confidence: 99%

Modified Multiway Pixel-Value Differencing Methods Based on General Quantization Ranges for Image Steganography

Shih

2022

IEEE Access

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A new modified multiway pixel-value differencing steganographic methods using general quantization ranges of pixel pairs' difference values is proposed (where the quantization range is not limited to the power of two), which can be employed to embed secret messages into cover images with a higher embedding rate. The cover images are partitioned into non-overlapping complete or incomplete blocks by using 1×3, 2×2, 2×3, or 3×3 block templates. All the resulting blocks can be embedded with a bitstream of messages whose bit positions are randomized by a random numbering mechanism. Firstly, one pixel in each block is set as a shared pixel; the least significant bits substitution method with the optimal pixel adjustment process is used to embed message bits into the shared pixel. Then, the shared pixel forms multiple pixel pairs with the remaining pixels in the block. Each pixel pair is used to embed message bits by the pixel-value differencing method. A multiple-based number conversion mechanism is employed to implement the use of non-power-of-two range widths, with each shared pixel and each pixel pair being used to embed a digit in the multiple-based number. Accordingly, all the message bits are transformed into a big multiple-based number whose digits are then embedded into the shared pixels and the pixel pairs with a higher embedding rate. Pixel pairs are checked for falling-off-boundary before embedding messages, and the pairs that are likely to be out of bounds are not embedded. The experimental results show that the embedding rates of the proposed data hiding method are higher than that of the existing multiway pixel-value differencing method. The stegoimages after embedding secret messages retain good image quality, and the RS steganalysis cannot detect the presence of the secret messages.INDEX TERMS general quantization ranges, multiway pixel-value differencing, steganography, data hiding, multiple-based number conversion.

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“…Firstly; steganography technology can be used to improve the confidentiality payload and robustness integrity of stenographic transactions in distributed hospitals. This technique can provide the confidentiality that represents high image quality, high embedding capacity and attack resistance [ 8 , 20 – 22 , 33 , 36 , 38 – 40 , 47 , 51 , 56 , 57 , 62 , 63 , 71 , 73 – 79 , 81 , 89 , 90 , 96 , 100 – 111 ]. However, no study has yet focused on the integrity of secret data after retrieval from stego images and discovered tampering during data storage in the database.…”

Section: Introductionmentioning

confidence: 99%

PSO–Blockchain-based image steganography: towards a new method to secure updating and sharing COVID-19 data in decentralised hospitals intelligence architecture

Mohsin

Zaidan

et al. 2021

Multimed Tools Appl

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Secure updating and sharing for large amounts of healthcare information (such as medical data on coronavirus disease 2019 ) in efficient and secure transmission are important but challenging in communication channels amongst hospitals. In particular, in addressing the above challenges, two issues are faced, namely, those related to confidentiality and integrity of their health data and to network failure that may cause concerns about data availability. To the authors' knowledge, no study provides secure updating and sharing solution for large amounts of healthcare information in communication channels amongst hospitals. Therefore, this study proposes and discusses a novel steganographybased blockchain method in the spatial domain as a solution. The novelty of the proposed method is the removal and addition of new particles in the particle swarm optimisation (PSO) algorithm. In addition, hash function can hide secret medical COVID-19 data in hospital databases whilst providing confidentiality with high embedding capacity and high image quality. Moreover, stego images with hash data and blockchain technology are used in updating and sharing medical COVID-19 data between hospitals in the network to improve the level of confidentiality and protect the integrity of medical COVID-19 data in grey-scale images, achieve data availability if any connection failure occurs in a single point of the network and eliminate the central point (third party) in the network during transmission. The proposed method is discussed in three stages. Firstly, the pre-hiding stage estimates the embedding capacity of each host image. Secondly, the secret COVID-19 data hiding stage uses PSO algorithm and hash function. Thirdly, the transmission stage transfers the stego images based on blockchain technology and updates all nodes (hospitals) in the network. As proof of concept for the case study, the authors adopted the latest COVID-19 research published in the Computer Methods and Programs in Biomedicine journal, which presents a rescue framework within hospitals for the storage and transfusion of the best convalescent plasma to the most critical patients with COVID-19 on the basis of biological requirements. The validation and evaluation of the proposed method are discussed.

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An image information hiding using adaptation and radix

Cited by 17 publications

References 13 publications

Pearl Pixel Steganographic Method for Grayscale Images using Location-Array Method

Pearl Pixel Steganographic Method for Grayscale Images using Location-Array Method

Modified Multiway Pixel-Value Differencing Methods Based on General Quantization Ranges for Image Steganography

PSO–Blockchain-based image steganography: towards a new method to secure updating and sharing COVID-19 data in decentralised hospitals intelligence architecture

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