Over the past decades, research in security has concentrated on the development of algorithms and protocols for authentication, encryption and integrity of data. Despite tremendous advances, several security problems still afflict system’s. In this android app watermarking and encryption is being applied on images and data. Because of the human visual system’s low sensitivity to small changes and the high flexibility of digital media, anyone can easily make small changes in digital data with low perceptibility. Here watermarking and encryption are being performed in wavelet domain. Here in watermarking, the coefficients of watermarks are being embedded with the coefficients of the original image. Encryption is being done in wavelet domain so that the probability of an intruder trying to access the contents is very much minimized. Thus, this model provides a high level of security.

1. INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY
VOLUME 5 ISSUE 1 – MAY 2015 - ISSN: 2349 - 9303
1
Comparative Study on Watermarking & Image
Encryption for Secure Communication
Ajna Madanan1
1
AnnaUniversity, ECE,
ajna.madanan@gmail.com
Dr. S. Poornachandra2
2
Anna University, ECE
dean.iqac.snsce@gmail.com
Abstract—Over the past decades, research in security has concentrated on the development of algorithms and protocols for
authentication, encryption and integrity of data. Despite tremendous advances, several security problems still afflict system’s. In
this android app watermarking and encryption is being applied on images and data. Because of the human visual system’s low
sensitivity to small changes and the high flexibility of digital media, anyone can easily make small changes in digital data with
low perceptibility. Here watermarking and encryption are being performed in wavelet domain. Here in watermarking, the
coefficients of watermarks are being embedded with the coefficients of the original image. Encryption is being done in wavelet
domain so that the probability of an intruder trying to access the contents is very much minimized. Thus, this model provides a
high level of security.
Index Terms— Security, wavelets, watermarking, encryption, intruder.
——————————  ——————————
1 INTRODUCTION
xplosive growth in multimedia technology and its applications
has escalated the necessity to build secure methods for legal
distribution of the digital content. Presently need for protection
against unauthorized copying and distribution has increased. Thus,
there aroused apprehensions over copyright protection of digital
contents. The solution to this problem is digital watermarking, which
is the most common and possibly the strongest technique for
protecting digital data. Cryptography ensures that the messages are
secure from possible ―attacks‖, which are impersonation,
eavesdropping etc.
Watermarking is a method of data embedding and information
hiding. In digital watermarking, patterns of bits are inserted into a
digital image, video or audio file that helps in identifying the
copyright information, i.e. author, rights etc. It is a concept closely
related to steganography. Watermarking is considered as a special
technique of steganography where one message is embedded in
another and the two messages are related to each other in some way.
In digital watermarking a low energy signal is imperceptibly
embedded into another signal. The low-energy signal is known as
watermark. The main signal in which the watermark is embedded is
referred to as cover signal since it covers the watermark. The entity
known as watermark key is used for embedding and detecting
watermark signal.
In image watermarking, information is embedded into cover
media for proving ownership identification. Various watermarking
techniques have been proposed by many authors in the last several
years, which include spatial domain and transform domain
watermarking.
To provide security of the images in the multimedia environment,
encryption plays an important role. Image encryption ensures
confidential transmission and storage of image over internet. Image
encryption has applications in multimedia systems, internet
communication, telemedicine, medical imaging, and military
communication to name a few.
Steganography and cryptographic methods are used together with
wavelets to increase the security of the data while transmitting
through networks. A combination of both encryption with
steganography allows better private communication. The goal of
steganography is to avoid drawing suspicion to the transmission of
the secret message.
In this paper RSA algorithm is being used for encryption. The
RSA encryption technique is being named after the inventors Ron
Rivest, Adi Shamir, and Len Adleman in 1977. RSA is a public-key
cryptography system. This algorithm is based on the difficulty of
factorizing large numbers that have two and only two factors (prime
numbers). It works on a public key system. Everyone would know
the public key. Using public key a user can encrypt data, but cannot
decrypt it. The person who possesses the private key can only
decrypt it. Theoretically, it may be possible, but it is extremely
difficult to generate the private key from the public key; this makes
the RSA algorithm a very popular choice in data encryption.
E
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 Ajna Madanan is currently pursuing masters degree program in electronics
and communication engineering in Anna University, India, PH-
8220829679. E-mail: ajna.madanan@gmail.com
 V.J. Subashini is currently Associate Professor in Computer Applications in
AnnaUniversity, India, PH-9444057965. E-mail: vjsubashini@yahoo.com
 Dr.S. Poornachandra is currently the dean /IQAC in electronics and
communication engineering in Anna University, India, PH-8643844943. E-
mail: dean.iqac.snsce@gmail.com

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2 RELATED WORK
Wavelet based image watermarking is gaining more popularity
because of its resemblance to the human visual system. In the
wavelet-based perceptual watermark masking proposed by Khalil
Zebbiche and FouadKhelifi [1], the perceptual masking model takes
into consideration local brightness, band sensitivity and texture
masking to compute a weight for each DWT coefficient of the
fingerprint image. Thus, the perceptual model consists of a spatial
frequency sensitivity function and two masking components based
on local brightness and texture masking. The watermarking scheme
yields a better robustness (i.e. lower BER). The bit error ratio or bit
error rate (BER) is the number of bit errors divided by the total
number of transferring bits during a time interval.
M. Barni [2] have developed an improved wavelet-based
watermarking through pixel-wise masking. It is mainly based on
masking the watermark according to characteristics of Human Visual
System (HVS). The watermark is being adaptively added to the
largest detail bands. The watermark weighing function is calculated
as a simple product of data extracted from HVS model. The
watermark is being detected by correlation. Victor [3] have
developed an algorithm that relies upon adaptive image
watermarking in high resolution sub-bands of DWT.
N. Kaewkamnerd and K.R. Rao [4] developed a wavelet based
image adaptive watermarking scheme. Embedding is performed at
higher level sub-bands of wavelet transform, even though this may
clearly change the image fidelity. Bo Chen and Hang Shen [5] have
developed a new robust fragile double image watermarking
algorithm using improved pixel-wise masking model and a new bit
substitution based on pseudo-random sequence. The method embeds
robust and fragile watermark into the insensitive part and sensitive
part of wavelet coefficients making two watermarks non-interfering.
Peng Liu and Zhizhong Ding [6] proposed a blind image
watermarking scheme based on wavelet tree quantization. Here
largest two coefficients are selected as significant coefficients and
the difference between them is taken as significant difference.
Watermark bits are embedded by comparing the significant
difference with an average significant difference value and maximum
difference coefficients are quantized.
Mohammad. A. B. Younes [7] introduced a block-based
transformation algorithm based on the combination of image
transformation and a well-known encryption and decryption
algorithm called Blowfish. The original image is being divided into
blocks that are then rearranged into a transformed image using a
transformation algorithm, and then the transformed image is
encrypted using the Blowfish algorithm. The results show that the
correlation between image elements was significantly decreased by
using this technique. The results also showed that increasing the
number of blocks by using smaller block sizes resulted in a lower
correlation and higher entropy.
The researches and applications of applying digital watermarking
to Geo-information data are still very inadequate. Watermarking
techniques that are developed for multimedia data cannot be directly
applied to the satellite images because here the analytic integrity of
the data, rather than the perceptual quality, is of primary importance.
Also satellite images have higher requirements in content security. It
desires not only watermarking for copyright protection, but also
encryption during storage and transmission for preventing
information leakage. As the demand to protect the sensitive and
valuable data from satellites has increased, a new method for satellite
image security by combining DWT-DCT watermarking and AES
encryption has been implemented [8].
To improve performance, discrete wavelet transform (DWT) has
been combined with another equally powerful transform which is
discrete cosine transform (DCT). The combined DWT-DCT
watermarking algorithm’s imperceptibility was better than the
performance of the DWT approach. For watermarking, the preferred
color model must be HSV (Hue, Saturation and Value) rather than
RGB because it is the most closely related color model with Human
Visual System [9]. Salt and pepper noise of input color satellite
image can be removed by using Modified decision based
unsymmetrical trimmed median filter (MDBUTMF) algorithm.
Prashant Shinde and Chetan Mohol [10] have proposed
application for Android OS that permits to add an invisible or visible
watermarking into an image of the current designations.
3 PROPOSED SYSTEM
Here encryption is performed in wavelet domain so that the
probability of an intruder trying to access the contents is very much
minimized. Encryption is being provided after watermarking so that
security level can be increased.
3.1 Transmission Section
Fig. 1. Extracted watermark using Haar wavelet
Here the original image and watermark are being converted to
wavelet coefficients using Haar transform. The coefficients of the
watermark are added with the coefficients of the original image to
get the watermarked image. During the embedding process, the
watermark coefficients are being embedded into the highest value
wavelet coefficient of the original image. Here for the encryption,
RSA algorithm is being used. While encrypting, keys are also used
along with user-typed message which is being converted to wavelet
coefficients. After encryption they are being transmitted. This is
being shown in Fig. 1. Encryption is being provided after
watermarking so that security level can be increased.

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3.2 Receiver Section
Fig. 2. Extracted watermark using Haar wavelet
After the reception, the user-typed message would be converted
to wavelet coefficients and then it would be used for decryption
along with the keys. By applying inverse DWT, the image and
watermark image would be returned. The watermark is removed at
the receiving end with the help of the key and the original image.
This process is called watermark extraction. In the present model,
only threshold is being considered. This is being shown in Fig. 2.
4 RESULT ANALYSIS
Performance metrics used here are Mean Square Error and Peak
Signal-to-Noise Ratio and visual comparison which are done through
correlation. Mean Square Error (MSE) allows comparing the ―true‖
pixel values of the original image to the degraded image. MSE
represents the average of the squares of the "errors" between actual
image and noisy image. Errors are the amount by which the values of
the original image differ from the degraded image. MSE can be
found using the equation:
(1)
The notations used are listed below.
𝑋 (𝑖, 𝑗): Original image,
𝑋′ (𝑖, 𝑗): Watermarked image, and
Nt : Size of image
Peak Signal-to-Noise Ratio(PSNR) is an expression for finding
the ratio between the maximum possible value (power) of a signal
and the power of distorting noise that affects the quality of its
representation. As many signals have a very wide dynamic range
(ratio between the largest and smallest possible values of a
changeable quantity), PSNR is frequently expressed in terms of the
logarithmic decibel scale. PSNR can be found using the equation:
(2)
Here Matlab is being used as the simulation software. On applying
different wavelets such as Haar, Daubechies, Coiflet, Symlet on Fig.
3, various values of MSE and PSNR are as follows:
TABLE I
MSE AND PSNR VALUES USING HAAR WAVELET
Haar Wavelet
MSE PSNR
124.2513 27.1878
Fig. 3. Extracted watermark using Haar wavelet
In Table I and II, it can be seen that Haar wavelet and db 1
(Daubechies wavelet) produces the same PSNR and MSE values.
From Fig. 3 and 4(a), it is seen that watermark extracted are as same
as the original one. In Daubechies daughter wavelets, i.e. from DB 2
to DB 10 in Fig. 4, it is seen that spots are being introduced in the
extracted watermark and also the left side is being distorted.
TABLE II
MSE AND PSNR VALUES USING DAUBECHIES WAVELET
Daubechies Wavelet
Daughter Wavelets MSE PSNR
DB 1 124.2513 27.1878
DB 2 123.0029 27.2316
DB 3 121.3821 27.2893
DB 4 119.5124 27.3567
DB 5 117.6615 27.4245
DB 6 116.3122 27.4745
DB 7 114.9985 27.5239
DB 8 113.5129 27.5803
DB 9 111.6682 27.6515
DB 10 110.1306 27.7117
(a) (b) (c) (d) (e)
(f) (g) (h) (i) (j)
Fig. 4. Extracted watermark using Daubechies daughter wavelet i.e. from
DB 1 to DB 10.

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Table III
MSE and PSNR values using Symlet wavelet
Symlet Wavelet
Daughter Wavelets MSE PSNR
SYM 2 123.0029 27.2316
SYM 3 121.3821 27.2893
SYM 4 111.4139 27.6614
SYM 5 108.2783 27.7854
SYM 6 104.9880 27.9194
SYM 7 105.9100 27.8814
SYM 8 098.0600 28.2159
(a) (b) (c) (d) (e)
(f) (g)
Fig. 5. Extracted watermark using Symlet daughter wavelet i.e. from SYM
2 to SYM 8.
Similarly, in Symlet daughter wavelets, i.e. from SYM 2 to SYM
8, it is seen that spots are being introduced and its intensity increases
in the increasing order of Symlet daughter wavelets. Also the left
side is being distorted and it increases more while reaching SYM 8.
This can be seen in Fig. 5.
Table IV
MSE and PSNR values using Coiflet wavelet
Coiflet Wavelet
Daughter Wavelets MSE PSNR
COIF 1 117.6857 27.4236
COIF 2 109.5243 27.7357
COIF 3 102.0552 28.0425
COIF 4 095.2216 28.3434
COIF 5 087.2121 28.7250
(a) (b) (c) (d) (e)
Fig. 6. Extracted watermark using Coiflet daughter wavelet i.e. from COIF
1 to COIF 5
Similarly from Table III and IV it is seen that degradation of
image quality increases as one goes down. Degradation in Coiflet is
more when compared to Symlet wavelet as seen in Fig. 6. Therefore,
from the above analysis, it is being concluded that Haar wavelet and
DB 1 of Daubechies wavelet produces better results by comparing
extracted watermark with the original image.
5 CONCLUSION
This paper introduces a model that provides a high level of
security by using both watermarking and encryption in the wavelet
domain. Due to this the probability of an intruder’s access to the
contents is very much minimized.
Fig. 7. Images a) Original Image b) Watermark Image c) Watermarked
Image d) Extracted watermark
Here watermarking has been implemented using both grayscale
and color images and watermark was extracted successfully. Fig. 7
shows the original image, watermark, watermarked image and the
extracted watermark. By comparing the original watermark image
and the extracted watermark images, it is seen that Haar wavelet
produced better results.
ACKNOWLEDGMENT
I extend my heart-felt thanks to my Project Guide, Dr. S.
Poornachandra, Dean, IQAC, for his remarkable guidance,
encouragement, valuable suggestions and support for the completion
of this Project. I take immense pleasure in expressing my humble
note of gratitude to, Dr. Karthikeyan V.V., Head of Department for
giving the permission to carry my project in the department. I am
highly grateful to our PG Project Coordinator Dr. G.K.D.
PrasannaVenkatesan, for his valuable suggestions throughout the
course of this project. I record my indebtedness to our Principal Dr.
N. Gunasekaran, for his guidance and sustained encouragement for
the completion of the project. I also extend my thanks to Mrs. V.J.
Subashini, Senior Assistant Professor, Jerusalem College of
Engineering, Chennai, for her remarkable guidance, encouragement,
valuable suggestions and support for the completion of this Project. I
also extend my thanks to Faculty members, my parents, and friends
for providing their guidance and moral support in successfully
completing the Project.

5. INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY
VOLUME 5 ISSUE 1 – MAY 2015 - ISSN: 2349 - 9303
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