A reversible data hiding scheme based on (5, 3) Hamming code using extra information on overlapped pixel blocks of grayscale images

Nguyen, Tan Van; Le, Huu Dung

doi:10.1007/s11042-020-10347-0

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…But the hiding capacity (HC) is only 1 bpp. Nguyen and Le 23 used (5, 3) Hamming code in 5-pixel groups for achieving reversible data hiding. Reversibility was achieved, but HC is only 1.2 bpp.…”

Section: Related Work and Research Contributionmentioning

confidence: 99%

Image watermarking based on remainder value differencing and extended Hamming code

Gottimukkala,

Kumar,

Dash

et al. 2023

J. Electron. Imag.

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Due to the availability of various photo editing tools, intruders can tamper with an image very easily. So, various watermarking and tamper detection approaches have been proposed by researchers. Basically, tamper detection techniques focus on embedding the watermark, extracting the water mark, and identifying the tampered regions. But it is very important that the tampered pixels should also be corrected. We bring forward an image watermarking technique for tamper detection and correction using remainder value differencing (RVD) and extended Hamming code (EHC). It operates on a pixel group of size 2 × 2. Watermark bits (WBs) are generated from four most significant bits of the pixels in a pixel group by EHC and concealed in four lower bit planes by the principle of RVD. The WBs are extracted at the receiver along with the identification of tampered pixels. The tampered pixels are corrected by the developed correction logic. As the principle of RVD is used, precautions are taken to avoid the fall-off boundary problem. The efficacy of this technique is accessed through various quality metrics. It is noted that it performs better than the existing techniques. The recorded peak signal-to-noise ratio value is 45.49 dB with structural similarity value 0.9889. The tampered pixels are identified and corrected.

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Section: Related Work and Research Contributionmentioning

confidence: 99%

Image watermarking based on remainder value differencing and extended Hamming code

Gottimukkala,

Kumar,

Dash

et al. 2023

J. Electron. Imag.

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“…Therefore, the emergence of reversible information hiding technology satisfies the dual recovery of the carrier and secret message [10][11][12]. Reversible information hiding means that when the receiver receives the watermarked image not only the secret message can be extracted but also the original image can be recovered according to the embedding and extraction rules [13,14]. It not only satisfies the confidentiality of secrets but also does not permanently destroy the image, which can be widely applied to medical image transmission.…”

Section: Muhuri Et Al Implement Image Steganography On Integer Wavele...mentioning

confidence: 99%

Information Hiding Based on Two-Level Mechanism and Look-Up Table Approach

et al. 2022

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Information hiding can be seen everywhere in our daily life, and this technology improves the security of information. The requirements for information security are becoming higher and higher. The coverless information hiding with the help of mapping relationship has high capacity, but there is still a problem in which the secret message cannot find the mapping relationship and the process requires extra storage burden during the transmission. Therefore, on the basis of symmetric reversible watermarking, the paper introduces the two-level mechanism and novel arrangements to solve the problem of sufficient diversity of features and has better capacity and image quality as a whole. Besides, for the security of secret message, this paper designs a new encryption model based on Logistic mapping. This method only employs coverless information hiding of one carrier image to transmit secret message with the help of the two-level mechanism and look-up table. Reversible information hiding is applied to embed the generated location table on the original image so that ensures storage and security. The experiment certifies that the diversity of hash code is increased by using the two-level image mechanism and the quality of the image is excellent, which proves the advantages of the proposed symmetric method over the previous algorithm.

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“…Mittal first preprocessed the images by vector flow map cutting and, finally, obtained good segmentation results [15]. A segmentation method based on the Hessian matrix and the threshold entropy is proposed by Nguyen et al ey used morphological feature-based spectral clustering techniques to enhance blood vessels, by eventually combining the results with binary images, based on entropy maximization of threshold segmentation [16].…”

Section: State Of the Artmentioning

confidence: 99%

Feature Extraction of Fused Residual Network and Single Target-Assisted Vessel Image Recognition of MRF Grayscale Information

Zhang

Cai

Shi³

et al. 2022

Mobile Information Systems

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At present, the segmentation method for the heart is one of the most difficult problems and a hot topic in medical imaging segmentation and image analysis. In order to achieve high-precision recognition of heart and blood vessel image, a single target auxiliary blood vessel image recognition algorithm, based on Markov Random Field (MRF) and ash fusion residual network information, is proposed in this paper. The corresponding features are extracted by the residual network depth, and the heart and blood vessels are segmented with high precision through MRF gray information to assist blood vessel image recognition. Thus, an algorithm combining Markov Random Field and Grayscale Information can complete such auxiliary recognition for single target ship image. The paper will also discuss auxiliary recognition techniques for identifying images of the heart and blood vessels. Added to that, it presents current medical image algorithms with a particular emphasis on cardiac image algorithms. Through a series of analysis, better algorithms are proposed. Thus, the research contents of a single target present the vascular image recognition algorithm. Therefore, a blood vessel image recognition algorithm is proposed for the research content of a single target. A series of validation experiments are conducted on the DRIVE and star datasets. The experimental results show that the method proposed in this paper has high segmentation accuracy, can achieve high-precision segmentation, and can achieve high-precision single target blood vessel image recognition. By observing and analyzing two-dimensional cardiac images, such as diabetic arterioles, hemorrhages, hard exudates, and so on, the difficulty of diagnosing cardiovascular disease characteristics can be reduced, and the diagnosis and treatment can be facilitated.

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A reversible data hiding scheme based on (5, 3) Hamming code using extra information on overlapped pixel blocks of grayscale images

Cited by 7 publications

References 30 publications

Image watermarking based on remainder value differencing and extended Hamming code

Image watermarking based on remainder value differencing and extended Hamming code

Information Hiding Based on Two-Level Mechanism and Look-Up Table Approach

Feature Extraction of Fused Residual Network and Single Target-Assisted Vessel Image Recognition of MRF Grayscale Information

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