Image interpolating based data hiding in conjunction with pixel-shifting of histogram

Chang, Yao-Tang; Huang, Cheng-Ta; Lee, Chin‐Feng; Wang, Shiuh-Jeng

doi:10.1007/s11227-013-1016-6

Cited by 63 publications

(57 citation statements)

References 12 publications

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“…In the above equation (2), only the value of secret message (SM ∈ Z∕lZ with l prime ) in ASCII is a 0 and the value of a 1 , … , a (h−1) are at random in Z / lZ. After that, we computed the f(x) value which is converted into binary bit stream.…”

Section: Initialization Stagementioning

confidence: 99%

“…Consider pixel values of sub-sampled image block (SI 1 ) is in first row (100, 102) and second row (107, 104). The first interpolate value is fix ((100 + 102)∕2) = 101 because of two neighbor pixel and for position (2,2) there are four neighbor, so modified value will be fix ((100 + 102 + 107 + 104)∕4) = 103.…”

Section: Numerical Illustration Of Embedding Processmentioning

confidence: 99%

“…This 12 bit binary data is partitioned into four fragment. Embed these four fragment by adding its corresponding value at position (1, 2), (2, 1), (2,3) and (3,2) of size (3 × 3) interpolated image block. The value of position (2,2) in interpolated image block is added with the value of x in f(x).…”

Section: Introductionmentioning

confidence: 99%

“…For Lena image, the payload and quality through Jana [6] scheme were 130,000 bits and 50.60 PSNR dB respectively. Some researcher has been focused on interpolation based data hiding techniques [2,10,12,15,16,18,29,[32][33][34][35][36][37]. [23] and [3] are focused on watermarking and image authentication based data hiding techniques respectively.…”

Section: Introductionmentioning

confidence: 99%

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Directional pixel value ordering based secret sharing using sub-sampled image exploiting Lagrange polynomial

Meikap¹,

Jana

2019

SN Appl. Sci.

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In this paper, we proposed secret sharing within the sub-sampled image using Directional Pixel Value Ordering (DPVO) for Reversible Data Hiding scheme. The original cover is partitioned into sub-sampled images which extend its size through image interpolation technique. The secret information has been converted through Lagrange interpolation polynomial techniques. These new secret data is embedded within pixels of each interpolated sub-sampled images using DPVO. In the decoder side, the secret data is taken out from the pixels of every stego image using reverse DPVO. After that, Lagrange's interpolation is applied to generate the original secret message. The proposed scheme enhanced the security for the sharable characteristics of secrets amongst several images. It raises the data embedding capacity and improves the visual quality that is determined by peak signal to noise ratio (PSNR). The average PSNR value is over 60 dB. It shows the superiority of the new algorithm over the other existing data hiding methods regarding payload of data, quality, and security of the image. Also, the stego images have evaluated through standard deviation, regular singular analysis, correlation coefficient, normalized cross-correlation and structural similarity index in between original cover and marked (stego) image to display the robustness of our result among the several steganographic strikes.

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Section: Initialization Stagementioning

confidence: 99%

Section: Numerical Illustration Of Embedding Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Directional pixel value ordering based secret sharing using sub-sampled image exploiting Lagrange polynomial

Meikap¹,

Jana

2019

SN Appl. Sci.

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“…Jan et al [9] recommended a new way of pixel calculation, which results in improved PSNR compared, to existing methods. Chang et al proposed the enhanced neighbor mean interpolation [10] (ENMI), which uses a multistage embedding method to achieve a higher payload with the embedding capacity of 2, 47,095 bits which is higher than Jung et al…”

Section: Introductionmentioning

confidence: 99%

Distortionless Data Hiding for Encrypted Images: A DCT Approach

Nemani¹,

Talari²,

Sumalatha³

2017

JESTR

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An enhanced image quality for Distortionless Data Hiding (DDH) by Discrete Cosine Transform in images encrypted was implemented in this work. The DDH is commonly called reversible data hiding (RDH), referred as technique recovering input image with no distortion from stego image by separating the concealed information. Information can be implanted into the encrypted pictures by creating vacant space after encryption or before encryption. The former has the disadvantage of image degradation by data separation as well as on image recovery. To overcome the disadvantage, creating a vacant space before encryption such that it can attain real reversibility i.e. information can be separated from the cover picture with no degradation. Experiments result in a larger amount of data up to 1,31,072 bits embedded with better PSNR value in comparison to existing technique.

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An Enhanced Pixel Intensity Range-Based Reversible Data Hiding Scheme for Interpolated Images

Singh

Vaish

2022

Lecture Notes on Data Engineering and Communications Technologies

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Image interpolating based data hiding in conjunction with pixel-shifting of histogram

Cited by 63 publications

References 12 publications

Directional pixel value ordering based secret sharing using sub-sampled image exploiting Lagrange polynomial

Directional pixel value ordering based secret sharing using sub-sampled image exploiting Lagrange polynomial

Distortionless Data Hiding for Encrypted Images: A DCT Approach

An Enhanced Pixel Intensity Range-Based Reversible Data Hiding Scheme for Interpolated Images

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