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ADHAAR: A RELIABLE DATA HIDING TECHNIQUES
WITH (NNP2) ALGORITHMIC APPROACH USING X-RAY
IMAGES
R. Karthick
Assistant Professor, Electronics and Communication Engineering, Sethu Institute of Technology,
Virudhunagar, Tamil Nadu, (India).
E-mail: karthickkiwi@gmail.com
ORCID: https://orcid.org/0000-0002-3222-0185
Meenalochini Pandi
Assistant Professor, Electrical and Electronics Engineering, Sethu Institute of Technology,
Virudhunagar, Tamil Nadu, (India).
E-mail: meenalochinip@gmail.com
ORCID: https://orcid.org/0000-0003-3601-4773
M. Sheik Dawood
Professor, Electronics and Communication Engineering, Sethu Institute of Technology,
Virudhunagar, Tamil Nadu, (India).
E-mail: sheikdawood7@gmail.com
ORCID: https://orcid.org/0000-0002-8767-386X
A. Manoj Prabaharan
Assistant Professor, Electronics and Communication Engineering, Sethu Institute of Technology,
Virudhunagar, Tamil Nadu, (India).
E-mail: manojprabaharann@gmail.com
ORCID: https://orcid.org/0000-0003-0055-716X
P. Selvaprasanth
Assistant Professor, Electronics and Communication Engineering, Sethu Institute of Technology,
Virudhunagar, Tamil Nadu, (India).
E-mail: selvaprasanth9619@gmail.com
ORCID: https://orcid.org/0000-0003-3491-1243
Recepción: 29/11/2019 Aceptación: 05/03/2021 Publicación: 30/11/2021
Citación sugerida:
Karthick, R., Pandi, M., Dawood, M. S., Prabaharan, A., M., y Selvaprasanth, P. (2021). ADHAAR:
A reliable Data Hiding techniques with (NNP2) Algorithmic Approach using X-ray images. 3C
Tecnología. Glosas de innovación aplicadas a la pyme, Edición Especial, (noviembre, 2021), 597-609. https://
doi.org/10.17993/3ctecno.2021.specialissue8.597-609
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Noviembre 2021
ABSTRACT
The technique which is most important for the Reversible Data Hiding (RDH) is Prediction
Error Expansion (PEE) through this we can hide large messages into digital media with little
distortion. The Nearest Neighborhood Pixel Prediction (NNP2) algorithm which goes under
the process of the Rhombus prediction and Chinese Remainder Theorem (CRT). In which
these several methods are used for the prediction errors, variation of prediction and pixel
are used to embed data. The combined Chinese Remainder Theorem as well as Rhombus
prediction provides better quality output rather than traditional one. For embedding several
bits into one embeddable pixel the size have to be modied and adjusted using Chinese
Remainder Theorem (CRT). The distortion level is reduced by Histogram Shifting (HS).
The performance of proposed method is evaluated using PSNR for number of medical
images. The simulation results provide the best encoded capacity as well as good image
pattern than ancient techniques.
KEYWORDS
Prediction Error Expansion (PEE), Dierence Expansion (DE), Histogram Shifting (HS).
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1. INTRODUCTION
The renowned technique called Reversible data hiding (RDH) gives reversibility solution
for some sensitive media i.e., satellite images and medical images were taken into account
for calculating lossless embedded data (El Bey et al., 2016). The output data can be totally
recovered by means of adapting embed data into media, mostly hidden data are achieved
from Reversible data hiding (RDH) techniques. Based on all the studies there provides an
existing method (Pande & Varshney, 2017) i.e. DWT Discrete Wavelet Transform. This
method provides the same process of Reversible Data Hiding RDH with low accuracy,
higher distortion and security less, low data hiding capacity (Ayyappan & Lakshmi, 2018).
To overcome all the drawbacks, we have overcome with new method based on NNP2
algorithm and Rhombus prediction (Karthick et al., 2019).
In Suseela, Lakshmi and Jyothy (2017) has provided a useful study on A Novel approach for
digital image watermarking. The parameters and algorithm which uses in many techniques
is guided by Tiwari and Sardar (2017) and Tiwari and Sharmila (2017) by the Digital
Watermarking applications, Parameter Measures and techniques. In Shreekumar and
Salam (2014) provided the information for embedding as a medical information using
minimization of scaling factor from Firely algorithm. To optimize bottom level of image
distortion moves to combined histogram shifting and reversible data hiding techniques
(Karthick & Sundararajan, 2017a, 2017b, 2017c, 2018). By means of identifying peak
values and zero values proposed RDH based histogram shifting is implemented for lower
image distortion. Here, we have proposed Chinese remainder algorithm based NNP2
rhombus prediction techniques. The remaining sections are partitioned into ADHAAR
methodology and experimental results.
2. ADHAAR METHODOLOGY
ADHAAR methodology section shows brief information about Chinese remainder
theorem. The proposed techniques are given below.
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Watermark
‘A’ (text)
Histogram
Shifting
Extracted
Medical Image
Original Medical
Input Image
Nearest
Neighborhood
Pixel Prediction
(NNP2)
Chinese Theorem
(CRT) &
Rhombus
Prediction
ReversibleData
Hiding(RDH)
Technique
Figure 1. Proposed nearest neighborhood pixel prediction (NNP2) Algorithm.
Source: own elaboration.
2.1. CHINESE REMAINDER THEOREM
The basic concept of CRT shows that a congruence system. A well-known mod set {n1,
n2,….nm} where m is a positive integer, and GCD(ni, nj)=1 for i not equal to j, i, j€[0,m]
(Kaur & Shukla, 2014). For a positive integer X, there exist equation xi =X mod ni, where
i=1, 2,….m. The m-tuple x1, x2,…..xm is isolated for all X € {0, ni}. The description
of CRT is given:
(1)
(2)
(3)
These equations describe the isolated solution for CRT theorem, where C in this equation
manages the m-tuple {x1, x2,…………..xm}
2.2. NNP2 WITH RHOMBUS PREDICTION
In NNP2 provides the rhombus prediction to exhibits the nearly neighboring pixels with
largest correlation.
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Figure 2. Rhombus prediction.
Source: own elaboration.
The pixel can be predicted by pi,j, rhombus prediction considers 4 nearer pixels, Pi-1, j, Pi,
j+1, Pi, j-1, Pi+1, j. The central pixel pi,j, achieved by right, left, upper and bottom pixels,
Pi, j-1, Pi-1, j, Pi,j+1,Pi+1,j. There are two prime values taken into account for encoding the
white pixels called data embedding algorithm.
(4)
(5)
Step (i): calculation of prediction value
The prediction Pi,j is calculated by the equation,
Pi, j is the predicted value of pixel.
Step (ii): calculation of absolute value.
The absolute value is the dierence between original and predicted value of pixels D = |Ii,j
– pi,j|
Where,
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Ii,j is a value of original pixels
D is a dierence between original and predicted value of pixels.
Step (iii): calculation using CRT.
To calculate the integer C using Chinese remainder theorem, read N bits from secret le
(binary data), and its decimal value is x.
(6)
Where,
Step (iv): Embedding stage: The calculated value of C will be embedded into the original
value of pixels. The watermarked pixel value is given by,
If O D < p,
(7)
Hence,
Wi,j is the pixel(watermarked) value of Ii,j
If D ≥ p,
(8)
whereas,
Wi,j is the pixel (watermarked) value of Ii,j
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T is threshold.
The “black pixels” are remains constant and watermarking is adopted for “white pixels” are
shows the prediction value using embedding algorithm.
2.3. HISTOGRAM SHIFTING
Each and every variable intensity the number pixels showing an image in histogram graph.
The output image of RDH technique is subjected to histogram shifting to reduce the
distortion level. The secret bits are embedded with the prime values p and q.
However to prevent overow and underow the histogram is shifted. The histogram of
extracted image is shifted to L = p x q - p units. By using above condition, while p and q,
are an integer, the value of q is set as multiples of 2, such as 2n pixel can be embed 6 bits
at most.
Figure 3. Histogram of extracted image.
Source: own elaboration.
Figure 4. Histogram after shifting.
Source: own elaboration.
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3. RESULTS AND DISCUSSION
PSNR is computed between the two medical images. The original image and extracted
image from watermarking used measure the quality. The better quality attained from higher
value of PSNR with reconstructed or extracted image. The image quality can be achieved
by the known metrics MSE and PSNR. The PSNR gives the measurement of peak error
and MSE gives the ratio between the reconstructed image and original image. The error
can be minimized by means of optimize the MSE value.
The formula for PSNR is given in the Equation respectively.
(9)
R is max uctuations in an input image data.
The acquired medical images are shown in:
Figure 5.1. Sample input image 1.
Source: own elaboration.
Figure 5.2. Sample input image 2.
Source: own elaboration.
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Figure 5.3. Sample input image 3.
Source: own elaboration.
Figure 5.4. Sample input image 4.
Source: own elaboration.
Figure 5.5. Sample input image 5.
Source: own elaboration.
Table 1. Calculation of PSNR reading.
INPUT IMAGE DWT RHOMBUS
PREDICTION
Sample input 1 6.59 38.02
Sample input 2 10.23 37.49
Sample input 3 22.16 39.18
Sample input 4 26.83 41
Sample input 5 31.21 41.49
Source: own elaboration.
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PSNR value graphical representation for the existing and the proposed method.
0
5
10
15
20
25
30
35
40
45
Sample
input 1
Sample
input 2
Sample
input 3
Sample
input 4
Sample
input 5
PSNR in decibels
DWT
Rhombus Prediction
Figure 6. Sample tested Image DWT vs Rhombus prediction.
Source: own elaboration.
4. CONCLUSIONS
In this work, secured medical image transmission is proposed using image processing
techniques. Medical images are taken as original images. Watermark is embedded into
original image to be transmitted by using NNP2 algorithm. Clearly the proposed NNP2
with rhombus prediction provides lower distortion with larger embedding capacity than
conventional method. As a result, the graphical representation of DWT Vs rhombus
prediction (NNP2) achieved good performance in reversible data hiding schemes.in
addition; we use histogram shifting to prevent distortion.
ACKNOWLEDGEMENT
I would like to thank Professor Dr. M. Sundararajan for his expert advice and encouragement
throughout this research work.
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