Protection of Multispectral Images using Watermarking and Encryption

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 06 | June -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 2996
PROTECTION OF MULTISPECTRAL IMAGES USING WATERMARKING
AND ENCRYPTION
HARSHA PUSHPAN1, PRACHOD P PILLAI2
1PG SCHOLAR, Dept. of Computer Science & Engineering, Musaliar College of Engineering & Technology,
Pathanamthitta, Kerala
2Associate Professor, Dept. of Computer Science & Engineering, Musaliar College of Engineering & Technology,
Pathanamthitta, Kerala
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Abstract – The key problem in spatial information
service is the copyright protection of multispectral images.
This paper defines a scheme that combines watermarking and
encryption for the protection of multispectral images. This
paper proposes wavelet based watermarking and a secured
multiplicative-transposition-based cipher for encryption.
Experimental results show that the proposed algorithm
provides good robustness against encryption attacks
,transparency, strength, large data hiding capacity, correct
extraction of watermark, and strong robustness against JPEG
lossy compression, filtering, and noise.
Key Words: Multispectral image, Watermarking,
Waveletbasedwatermarking,Encryption,Multiplicative
and Transposition based cipher.
1. INTRODUCTION
Digitising texts, images, videos are very easy in this era due
to the advancement of technology. Digital data can be easily
accessed and shared with the help of Internet. But thisleads
to misuse of digital data. The unauthorized acquisition of
multispectral images and its preprocessing are cost- and
manpower-intensive tasks. Hence, it is important to protect
the ownership rights of the data owner and data processing.
Digital watermarking serves as a good and efficient solution
for the above said problem. Multispectral imagesareusedin
various applications including defense, which are related to
national security, so need high level of confidentiality. This
makes multispectral images highly sensitive and its
confidentiality needs urgent attention. Therefore, certain
precautions have to be adopted in handling the sharing of
such sensitive and confidential images over the Internet.
Although standard and asymmetric encryption algorithms
like AES, DES, and RSA are available, they cannot be
implemented for multispectral images as the data volumeis
high and nature of multispectral images.Asinglekeycannot
be used for encrypting multispectral images. For multiple
keys, key storage and implementation for such a high-
volume data is impractical. Moreover, these algorithms are
time consuming for encryption as well as decryption. The
combination of encryption and watermarking could be the
best solution to provide security and confidentiality for
multispectral images.
2. LITERATURE SURVEY
The paper “Content security protection for remote sensing
images integrating selective content encryption and digital
fingerprint” by Y. Xu, Z. Xu, and Y. Zhang ,proposed a method
by combining watermarking and encryption. They tried to
embed watermark at decryption. This scheme does not
provide total security and confidentiality as the encrypted
image does not carry watermark.
L. Jiang and Z. Xu proposed “Commutative encryption and
watermarking for remote sensing image” that implement
commutative encryption and watermarking solution for
remote sensing images. They used spatial scrambling based
on Arnold scrambling for encryption using a symmetric
scheme for whole remote sensing image. If the key is
compromised, the whole encryption procedure would fail.
L. Jiang, Z. Xu, and Y. Xu proposed “A new comprehensive
security protection for remote sensing image based on the
integration of encryption and watermarking” to integrate
encryption and watermarking using orthogonal
decomposition of remote sensing images. This method has
important effects on edges of remote sensing images and
need to perform some preprocessing at watermarking to
retainand recover edge information.
3. DISCRETE WAVELET TRANSFORM
The wavelet transform is based on function representing
wavelets. Wavelets are mathematical function that
represents both the scaled and translated copies of a finite-
length waveform i.e, mother wavelet. It helps to calculate
different frequency components of the given multispectral
image at different resolution levels. Discrete wavelet
transform (DWT) is a multi resolution description of an
image that represents the mathematical function. DWT
separates the signal into high- and low-frequency
coefficients.
The high-frequency coefficients contain information about
the edge components, while the low-frequency coefficient is

split again into high- and low-frequencycoefficients.The2-D
wavelet transform decomposes an image into lower
resolution approximation coefficients (LL) and detail
coefficients such as horizontal (HL), vertical (LH), and
diagonal (HH) coefficients. Watermark embedding in low
frequency (LL) increases robustness against compression,
Gaussian noise, scaling, and cropping whilewatermarking in
high frequency (HH) is robust to histogram equalizationand
intensity adjustments.
4. PROPOSED METHOD
Multispectral image mostly includes satellite images and
aerial photographs. The demandforthesedata hasincreased
dramatically due to the large numberofapplicationscapable
to use them. Digital watermarking and encryption are used
to achieve copyright protection andsecurityofmultispectral
images at dissemination level. A wavelet-based algorithm is
developed for copyright protection. Simple and strongly
secure encryption based on multiplicative and two-stage
transposition cipher is used to provide security at the
transmission level.
Fig 1 : Proposed System
4.1 Discrete Wavelet Transform
Wavelets are a special kind of mathematical functions, in
DWT (Discrete Wavelet Transform) there is a need to deal
with two sets of functions scaling functions and wavelet
functions . Most of the scaling and wavelet functions are
fractal. The commonly used wavelet functions are ‘Haar,
Daubechies, Symlets, Coiflets, Biorthogonal, Discrete Meyer
etc. Filter cofficients of these wavelet functions are discrete.
The steps in watermark preprocessing are:
1) Watermark preprocessing: To enhance the robustness
and security of the watermarking scheme, it is always
desirable toapplybasic transformationonwatermark before
embedding it into host data. Matrix interleaving is used in
this method.
2) Watermark Embedding:
The embedding algorithm uses a binaryimageaswatermark
and color multispectral image as a host image. Host
multispectral image is decomposed up to third level for the
process of watermark embedding.
Algorithm 1. Watermark Embedding
Input: Host image (R), Binary Watermark (W ), Matrix
interleaved position (M)
Process:
(1) Scramble the watermark (length = N ) using M
(2) Apply 2-D DWT on each channel of host multispec-tral
image up to 3 levels (LL3, HL3, LH3, and HH3). Select LL3 and
HH3 coefficients for watermark embedding.
(3) At third level, calculate watermark strength (alpha) as a
function of wavelet coefficients.
alpha = mean mean Ck
ch(i, j)
where i, j = 1, .., n ch = R, G, B k = LL3 , HH3
(4) Watermark is embedded in selected waveletcoefficients
as:
Ck(i, j) +alpha × W (l, m)
Ck (i, j) = Ck
where i, j = 1..n l, m = 1 . . . N ch = R, G, B
k = LL3 , HH3
(5)Apply inverse DWT to obtain watermarked
multispectral image.
˜
Output: Watermarked image (R)
Watermark Extraction
˜
Input: Watermarked image (R), Host image (R), Arnold
Key (Ak)
Process:
1) Using 2-D DWT, perform a third level decomposition of
the watermarked host image.
2) Calculate alpha from selected coefficients (LL3, HH3) of
each band of host multispectral image (R).
3) Extract the watermarks from LL3 andHH3 coefficients and
perform averaging to get scrambled watermark.
Embedding
Decryption
Encryption
Extraction
Watermark

4) Obtain the binary watermark (W ) by applying inverse of
matrix interleaving.
Multiplicative and Transposition Cipher-Based
Encryption
For encryption and decryption of multispectral images, we
are proposing multiplicative andtranspositioncipher(MTC)
based on symmetric key multiplicative affine cipher and
two-stage transposition cipher. Individually, they are
vulnerable to brute force, statistical, and cipher text-only
attacks. However, combination and proper cascading of
these ciphers provide more secure and strong cipher than
the individual.
We are applying MTC on multispectral image (red, green,
and blue channel) of size M × N and gray levels G. Even and
odd row elements are multiplied by EKER and EKOR.Eachrow
and column are shifted by EKSR and EKSC . Even and odd
column elements are multiplied by EKEC andEKOC .Multiplier
parameters EKER, EKOR, EKEC , and EKOC are relativelyprimeto
G, whereas 0 < EKSR < M and 0 < EKSC < N.
For RGB multispectral image, we have (3 × 6)! options for
key arrangement. At decoder side, boththecorrectsequence
of operations and correct keys should be available to get the
correct decryption; otherwise decoder cannot recover the
original image.
5. ANALYSIS
We have tested the performance of the proposed system on
different multispectral images of varying sizes on MATLAB
on 2.27 GHz Core Duo Processor.
The term peak signal-to-noise ratio (PSNR) is an expression
for the ratio between the maximum possible value (power) of
a signal and the power of distorting noise that affects the
quality of its representation. Because many signals have a
very wide dynamic range, (ratio between the largest and
smallest possible values of a changeable quantity)the PSNR is
usually expressed in terms of the logarithmic decibel scale.
Entropy converts any class other than logical to uint8 for the
histogram count calculation so that the pixel values are
discrete and directly correspond to a bin value.
Rough entropy measures calculation of the cluster centers
which is based on lower and upper approximation generated
by assignment of data objects to the cluster centers.
Roughness of the cluster center is calculated from lower and
upper approximations of each cluster center. In the next step,
rough entropy is calculated as the sum of all entropies of
cluster center roughness values.
A good encryption algorithm shouldrobustagainstall kindsof
brute force, cryptanalytic, and statistical attacks. The
histogram of encrypted image should be uniform to avoid
statistical attacks. Similarly,keyspacemustbelargeenoughto
avoid brute force attacks. In this section, the robustnessof the
proposed encryption algorithm is presented. It has been
observed that the proposed algorithm is robust against all the
mentioned attacks.
Key Sensitivity Analysis: In MTC, correct encryption and
decryption are highly sensitive to the utilized key space.
Secure image cryptosystems need high key sensitivity so that
the image cannot be decrypted correctly even if thereisa very
small change in correct keys. Key used in multiplicativecipher
are more sensitive as inverse of these keys only exists if it is
relatively prime to gray levels of an image. Original image
cannot be obtained even if a very small change occurs inthese
keys. The sequence of operations in MTC is also sensitive, as a
small sequence change can result into an incorrect decrypted
multispectral image.
Histogram Analysis: The encryption algorithm is strong if it
possesses good confusion and diffusionproperties.Histogram
analysis is used to demonstrate confusion and diffusion ofthe
proposed scheme. The color variations in RGB channels ofthe
original and encrypted image are represented in terms of
histogram. It is observed that the histogram of the encrypted
image is nearly uniformly distributed, and signiﬁcantly
different from the respective histogramsoftheoriginal image.
So the encrypted image does not provide clues to employ any
statistical attack on the proposed scheme.
Table 1 : Entropy Analysis
6. CONCLUSION
It is important to protect the ownership rights of the data
owner. Digital watermarking serves as a solution over the
above said problem.Multispectral imagesareusedinvarious
applications including defense, which are relatedtonational
security. This makes multispectral images highly sensitive
and its security needs urgent attention. In this paper crypto
watermarking, a combination of watermarking and
encryption to provide copyright protection form

multispectral images and secure delivery of copy righted
multispectral images is proposed. During transmission,
encryption can be used to prevent information leakage and
protocol attacks.
Ownership can be proved later as invisible water mark is
retained in the multispectral image. Ownership cannot be
proved until and unless the original multispectral image is
made available. It is observed that the proposed crypto-
watermarking approach satisfies the security of encryption,
the invisibility, robustness, and classification accuracy
retention of watermarking. Moreover, this algorithm
survives all the attacks havinglow-frequencyaswell ashigh-
frequency characteristics as we have utilized both low- and
high-frequencybandsforwatermarking. Thesamealgorithm
can further be used for hyperspectral images security at
storage as well as the dissemination.
REFERENCES
[1] T. Hemalatha, V. Joevivek, K. Sukumar, and K. Soman,
“Robust watermarking of remote sensing images without
the loss of spatial information,” in Proc. 10th ESRI India
User Conf., 2009, vol. 1, no. 2, pp. 1–8.
[2] B. Kumari and V. Rallabandi,“Modifiedpatchwork-based
watermarking scheme for satellite imagery,”Signal Process.,
vol. 88, no. 4, pp. 891– 904, 2008.
[3] P. Zhu and C. Chen,“Acopyrightprotection watermarking
algorithm for remote sensing image based on binary image
watermark,” Int. J. Light Electron Opt., vol. 124, no. 20, pp.
4177–4181, 2013.
[4] Y. Xu, Z. Xu, and Y. Zhang, “Content securityprotection for
remote sensing images integrating selective content
encryption and digital fingerprint,”J.Appl.RemoteSens., vol.
6, no. 1, p. 063505, 2012.
[5] L. Jiang and Z. Xu, “Commutative encryption and
watermarking for remote sensing image,” Int. J. Digital
Content Technol. Appl., vol. 6, no. 4, pp. 197–205, 2012.
[6] L. Jiang, Z. Xu, and Y. Xu, “A new comprehensive security
protection for remote sensing image based on the
integration of encryption and watermarking,” in Proc. IEEE
Int. Geosci. Remote Sens. Symp., 2013, pp. 2577–2580.
[7] S. Mallat, “The theory for multiresolution signal
decomposition: The wavelet representation,” IEEE Trans.
Pattern Anal. Mach. Intell., vol. 11, no. 7, pp. 654–693, 1989.

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