A Design of Novel Algorithm for Image Steganography Using Discrete Wavelet Transformation on Beagle Board-XM

IOSR Journal of Electronics and Communication Engineering (IOSR-JECE)

e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 10, Issue 3, Ver. I (May - Jun.2015), PP 22-30 www.iosrjournals.org

DOI: 10.9790/2834-10312230 www.iosrjournals.org 22 | Page

A Design of Novel Algorithm for Image Steganography Using

Discrete Wavelet Transformation on Beagle Board-XM

Nagarjun B1, Dr. Manjunath H V

2

1M.Tech Student, Department of Electronics & Communication, Dayananda Sagar Institutions, India 2Professor, Department of Electronics & Communication, Dayananda Sagar Institutions, India

Abstract :In today’s modern internet era, Security is one of the important issue in the communication and

storage of images. In order to provide security for these images, a wide variety of techniques and proposals

have been developed all across the world. The most prominent technique among all those security schemes is

the Image steganography. It is simply defined as the art or science in which the image sender embeds some

secret information into the image and sends it across a channel to the receiver. On receiving the image at the

receiver end the information that is hidden inside the image is retrieved back. In this research, we have used

discrete wavelength transformation and modified AES techniques for the process of steganography. The wavelet

is applied to the cover image in order to produce four different sub bands of different frequencies like LL, LH, HL and HH. The secret information is encrypted with the use of modified AES technique and it is hidden inside

the LH, HL and HH sub bands of the cover image. This novel algorithm for image steganography gives better

quality images and more security compared to other conventional methods that are present today.

Implementation of this steganography process is done using Beagle Board-XM with Open CV platform. Since

we are using Open CV and Beagle Board-XM for the purpose of implementation, the delay in processing of the

image, cost and resources required will be greatly reduced.

Keywords - Image Steganography, Discrete wavelet transformation, Biorthogonal DWT, AES, Encryption,

Beagle Board-XM, Open CV, PSNR

I. Introduction Since the inception of internet the security of information is the most vital factor in information

technology and communication. Many methods like cryptography, watermarking and encryption and decryption

techniques were developed in order to secure the information during communication. Unfortunately it was not

enough to protect the contents of the secret message from outside phishers and hackers. There was a need of a

new technique which can keep the existence of the message secret. The technique used to implement this is

called as Steganography.Coming to the history of Steganography, the Greek historian Herodotus writes in his

literary work “Histories” about a nobleman, Histaeus, who wanted to communicate with his son in law in

Greece. To communicate secretly with his son in law, he shaves one of his trusted slaves head and tattooed the

message on his scalp. After few months when the hair was grew on his scalp the slave was sent to Greece to

dispatch the secret message on his scalp [2]. As evidence, during world war the Germans developed a special

technique called as “Microdot”. Information such as important images and photographs were reduced in size

until it was a size of a sized period and were sent with a normal cover message over an insecure channel [3].

The major difference between Steganography and Cryptography is, Cryptography focuses only on

keeping the secret message or information and it is practically detectable. But in Steganography, it focuses on

keeping theexistence of the hidden information secret [4]. Compared to Cryptography, the steganography is

practically undetectable and more secure from external attackers. Both the techniques are unique in its own way

and have their own limitations and advantages. Once the presence of hidden secret is revealed or suspected the

security of the steganography fails. In few practical applications, the strength of steganography can be enhanced

by combining it with cryptography.

The other two techniques that are similar to steganography are watermarking and fingerprinting [5].

These two techniques mainly concentrate on protecting the intellectual property and have different algorithms

and requirements compared to steganography. In watermarking the instances that are present in the image are

“marked” in a uniform manner. A signature or sign are hidden in objects in a uniform manner in order to specify

the origin or signify the ownership and protection of copyright [6]. In fingerprinting, unique marks are

embedded well defined copies of the carrier object and then they are supplied to different customers. This

enables the intellectual property owner to identify if there is any break in licensing by the customers and supply

A Design Of Novel Algorithm For Image Steganography Using Discrete Wavelet…


to other third party people [5]. In both watermarking and fingerprinting the fact that the secret message hidden

inside the object may be publicly visible and known fact [4]. But in steganography the fact that there is a secret

message hidden inside a file itself will be a secret. Depending on the type of cover object that is used in

steganography, it can be classified into text, audio, video, image and protocol steganographic techniques in

digital mediums as shown in Fig. 1.

Fig. 1. Types of Steganography in digital medium.

A. Organization of the paper

This paper is structured as follows. In section II we have given an overview of the related work that has

been done on steganography techniques. The definitions that are commonly used and the general block diagram

of Image Steganography are presented in section III. The techniques that are used are given descriptively in

section IV. A brief introduction about the algorithms and hardware/software used for the implementation is

given in section V. Finally the performance analysis is discussed and concluded in section VI and VII

respectively.

II. Related Work Po-Yueh Chen and Hung-Ju Lin., [7] have effectively worked on a DWT based methodology for image

steganography. They have developed a new technique in which the secret message can be embedded in a

frequency domain. The new algorithm has been divided into two modes and five cases in their novel approach

for steganography. In this approach they have the secret information are embedded in the high frequency

coefficients which are resulted from the discrete wavelet transformation. Image quality is improved by

preserving the low frequency coefficients without making any modifications.

Mohammad Ali Mahrabi et al., [8] has done a remarkable work on steganalysis which are based on

statistical moments of wavelet sub band histograms. The wavelet sub bands which are derived from an image

consist of both least and most significant bits. Some of the least significant bits from grey level and some most

significant bit planes are removed and the image is decomposed using 3 levels Haar DWT. This decomposition

of image by 3 levels Haar DWT gives 13 sub bands in which the image itself is considered as LL band and

Fourier transform of each histogram sub band is calculated separately. This work gives improved detection rate

for LSB steganography compared to other techniques that are present today.

Prof U.L.Kulkarni et al., [9] has done a significant work on steganography using biometrics. Skin tone

region of images has been considered in their work to carry out the steganography using biometrics. They have

hidden the secret information in the skin tone region which is statistically undetectable compared to other

regions. The skin tone detection is performed by HSV color space. The secret information is hidden using DWT

approach which is more efficient that other frequency domain approaches. This work gives an insight into object

oriented steganography which illustrates higher security compared to other conventional methodologies.

Wang Yan and Ling-Di Ping., [10] has done an in depth study on steganography techniques and has

found a new algorithm which is based on spatial domain. They have formulated a new algorithm from which we

can hide a large amount of secret information in BMP image. They have used a methodology called as fixed

LSB’s substitute method which will compensate for distortion. This proposed method gives high capacity and

good quality of images compared to other steganography techniques that are in the present day.

K. Kanimozhi et al., [11] has performed a steganography based on dual transform using wavelets by

statistical methodology. This method extracts either DWT or IWT coefficients of both cover and secret image.

Fusion processing techniques is used on the coefficients that are extracted and stego image is obtained by the

application of various combinations of DWT and IWT on cover and secret images. Visual effect and robustness



of the stego image is corrected and performance analysis is done. This method has resulted in good

imperceptibility and robustness.

Neha Gupta et al., [12] have made a thorough study on steganography techniques and has proposed an

effective audio steganography by the use of DWT technique in this research work. This work intends to perform

the robustness and security of the audio steganography by the use of discrete wavelet transformation and LSB

techniques. The cover media they have used is an audio and can be easily used to communicate in the digital

media.

Prabakaran G and Bhavani R., [13] has done a unique digital image steganography based on the DWT

approach. They have worked on a modified approach for steganography where they have used a large size secret

image to hide in small size cover image. The secret image is scrabled by a technique called as Arnold

transformation. The DWT is performed on both cover image and secret image followed by Alpha blending

operation. Inverse DWT is applied to get the original stego image.

III. Definitions And Block Diagram The commonly used definitions and general block diagram of Image steganography are discussed here.

B. Definitions

Cover Image: It is an object which contains stream of data or signals which is used as a carrier of the

embedded information. The important factor of this cover image is the amount of secret information that

can be embedded into it.

Payload Image: It is the image that is used to embed into the cover image as secret information.

Stego Image: It is image that is obtained after the unification of cover image and payload image.

Capacity: It is defined as the amount of secret information that can be hidden inside the cover image

without harming the properties of the cover image.

Security: It is the measure of protection of payload image that is hidden inside the cover image.

C. General Block Diagram of Image Steganography

Fig. 2. General block diagram of Image Steganography

The general block diagram of image steganography is shown in Fig. 2. Image Steganography consists

of two individual modules namely the embedding module and the retrieval module. The embedding module is

used at the sender end where the payload image is embedded into cover image. After the successful embedding,

the stego image is derived at the end by the use of any steganographic techniques. The retrieval module is used

at the receiver end in order to extract the payload image which is embedded in the cover image by a method

called as inverse steganographic process. At the sender end encoder is used for the embedding of payload image

into cover image. Similarly, at the receiver end decoder is used to extract the payload image from stego image.



IV. DWT And Modified AES

D. Discrete Wavelet Transformation

Discrete Wavelet transform is one of the popular methods used in Image Processing domain. It has the

capacity to capture both time-frequency information of the image. The image data can be decomposed by the

method of Discrete Wavelet Transformation into different frequency ranges. This decomposition helps us to

separate frequency components that are induced by internal deformations or external factors. These decomposed

frequency components are called as sub bands. Wavelet transformation method ignores all those unwanted sub

bands and focus only on sub bands which contain maximum image information. 1 level DWT is computed by

performing successive low pass and high pass filtering of the image coefficient either in row by row or column

by column as shown in Fig. 3.

Fig. 3. Block Diagram of 1 Level DWT

Whereas in 2D DWT, the successive low pass and high pass filtering of image coefficients is done in

both row by row and column by column. This results in the decomposition of image into four sub bands namely

LL, LH,HL HH sub bands which corresponds to horizontal, vertical and diagonal features respectively. The LL

sub band contains the maximum amount of image information since it is approximately at half the original

image. The LH and HL sub bands contains the changes of images. The last sub band HH contains the detail

when the image is in high frequency. The 2D DWT and its sub bands is as shown in the Fig. 4.

Mathematically, Ψ(t) is considered as the mother wavelet and the parameters s and τ are the scaling and

shift parameters respectively. Then the 2D DWT of m x n image is given by the expression (1)

DWT (j,k) =𝟏

𝟐𝐣 𝐟 𝐱 ∞

−∞𝛗

𝐱

𝟐 − 𝐤 𝐝𝐱 (1)

Where k is the constant of the filter and j is the power of binary.

In this research work, we have applied 1 level DWT on the cover image by using Biorthogonal DWT

as the mother wavelet. Approximation band that is LL sub band contains the major information of the cover

image. Thus, the payload image is embedded in the remaining detailed bands of the image. The wavelet

computations can be explained in three easy steps as explained below.

Splitting: In this step, the total numbers of elements present are grouped into even and odd components that

is as shown in equation (2).

Xe = S1 = {S1, S2, S3 …….SN/2} and

XO = D1 = {d1, d2, d3……..dN/2} (2)

Prediction: In this step, the predicted values are obtained using the equation (3).

dN/2 = dN/2 – [0.5(Sm+Sm+1) + 0.5] (3)

Where, m varies from 1 to N/2 and D1 is given by

D1 = {d1, d2, d3……..dN/2}

Updation: In this step, the updated values are obtained by the equation (4).

SN/2 = SN/2 – [0.25(dp+dp+1) + 0.5] (4)

Where, p varies from 1 to (N/2+1) and S1 is given by

S1 = {S1, S2, S3 …….SN/2}



Fig. 4. Four Sub bands obtained after 2D DWT

E. Modified AES The Advanced Encryption Standard (AES) with few modifications are used to encrypt the payload

image which is used as secret information and hidden into the three sub bands LH, HL and HH obtained by the

process of DWT. The modified algorithm generally consists of 11 rounds in which the first round is termed as

Add round key stage followed by nine different rounds going through six stages of operation known as iteration.

The six stages of the each iteration are as listed below.

Sub Bytes

Shift Rows

Mix Columns

Add Round Key

Transpose of a matrix

BIT Manipulation

The final tenth round which gives the iteration of 3 rounds is

Sub Bytes

Shift Rows

Add Round Key

The only modifications that are done in modified AES algorithm is adding two more additional steps

that is transpose of a matrix and BIT manipulation. Transpose of a matrix is done after the mix row columns

where each 4*4 matrix is transposed with a intention to improve the security. Inverse transpose is applied during

the decryption of the same 4*4 matrix. In BIT manipulation the position of BITS of the transpose matrix are

interchanged. The pictorial representation of the modified AES technique is as shown in the Fig. 5. This

modification in AES algorithm improves the efficiency of the encryption and makes it more safe and secure.

The operation of each stage can be understood by the analysis of Advanced Encryption Standard by Joan

Daemen and Vincent Rijmen [14].

V. Proposed Algorithm and Resources Used The stego image is obtained by embedding the payload image into the cover image by the

steganographic technique. We make a few assumptions like the cover and payload images are color images with

different dimensions and there is a proper channel for the transmission of stego image. The objectives of this

algorithm include improving the PSNR value and increasing security. The embedding algorithm is shown in

Fig.6 and Table I respectively and reconstruction algorithm is shown in Fig.7 and Table II. Implementation of

this image steganography is done using Beagle Board-XM with OpenCV-2.4.2 platform. Beagle board is a low

cost and low power hardware which has several facilities like 512MB RAM, USB ports, Ethernet ports and

memory card slots to store images. Implementation of steganography on Beagle Board helps it in the usage of

real time applications. Since we are using OpenCV platform to implement this, the cost of the resources are

greatly reduced and the speed of operation is improved drastically. Microsoft Visual studio Express 2012 for

Windows Desktop is used to cross check the results of PSNR and capacity values that are obtained in the

steganographic process.



Fig. 5. Modified AES Algorithm

Fig. 6. Embedding Algorithm Fig. 7. Reconstruction AES Algorithm

TABLE I. Steps for Embedding Algorithm



TABLE II. Steps for Reconstruction Algorithm

VI. Performance Analysis The performance parameters that are considered here are PSNR value and capacity of the image. The PSNR

value of any image can be found by using the expression (5).

PSNR = 10*(log 10 ((255*255)/MSE)) (5)

Where MSE = 𝐝𝐢𝐟𝐟/(𝐡𝐞𝐢𝐠𝐡𝐭∗ 𝐰𝐢𝐝𝐭𝐡)

The cover images and the payload image that are used for the performance analysis is as shown in Fig.

8, Fig. 9, Fig. 10, Fig. 11, Fig.12 and Fig. 13. At first, we have used a constant cover image of 512 x 512

dimensions and payload image of different dimensions in order to analyze the results. The PSNR values for

different capacity using Biorthogonal DWT and Modified AES are tabulated in the Table III.

Similarly in the next stage we have used constant payload image of 256 x 256 dimensions and different

cover images of 512 x 512 dimensions to see the variations in PSNR values. It is noted that the PSNR values

slightly changes with the use of different cover images. The PSNR values are tabulated in Table IV. The PSNR

values that are obtained from the proposed algorithm are compared with other existing steganographic

techniques presented by Mohammad Reza Dastjani [15], Wang Yan [11], Kannimozhi [12], and Neha Gupta

[13]. It is observed that the PSNR value obtained in our proposed algorithm is much better compared to the

values obtained in other technologies. The comparison table of PSNR values is shown in Table V and the

screenshot obtained in Microsoft Visual studio Express 2012 during steganography process is given in Fig. 10.

TABLE III. PSNR Values for different capacity

Payload Image Size Capacity PSNR

256*256 0.2500 49.1819

400*400 0.6103 49.1704

450*450 0.7724 49.1758

470*470 0.8426 49.1623

From the above table consisting of variations in PSNR values corresponding to different capacity it can be

observed that as the capacity is increased the value of PSNR decreases.

Fig. 8. Cover Image: asheyes.jpgFig. 9. Cover Image: hfingers.jpg Fig. 10. Cover Image: dhands.jpg

(512 x 512) (512 x 512) (512 x 512)



Fig. 11. Cover Image: sfeet.jpgFig. 12. Cover Image: jears.jpg Fig. 13. Payload: navi4.jpg

(512 x 512)(512 x 512) (Different Sizes)

TABLE IV. PSNR values for different Cover Images

Cover Images Capacity PSNR value

hfingers.jpg (512 x 512) 0.250000 49.1956

dhands.jpg (512 x 512) 0.250000 49.1616

sfeet.jpg (512 x 512) 0.250000 49.1659

jears.jpg (512 x 512) 0.250000 49.1527

TABLE V. Comparison of PSNR values

Author Proposed Technique PSNR value

Mohammad Reza 2D Haar DWT 25.176

Wang Yan Spatial Domain 41.411

Kannimozhi Coefficients 40.850

Neha Gupta 1D Haar DWT 41.220

Proposed Method Biorthogonal+MAES 49.181

Fig. 10. Screenshot of steganography process in Ubuntu using OpenCV and Beagle Board-XM

VII. Conclusion A secret data that has to be hidden and sent from the sender to receiver end is done by carefully hiding

the data inside a stego image. A secure channel is used for the transformation of this stego image from one end

to the other. The embedded data inside the stego image is highly secure because of the encryption done by the

method of modified advanced encryption technique and Biorthogonal DWT for the creation of sub bands. The

implementation of this complete steganographic process is done by using Open CV platform and Beagle Board-

XM. Applications of this image steganography include major e-commerce industries and military fields. Future

work includes usage of other complex encryption techniques and wavelet transformations for better accuracy

and security.



Acknowledgments I express my deepest gratitude and sincere thanks to my guide Dr. Manjunath H V, Professor, Dept of

ECE, Dayananda Sagar Institutions, for his valuable time and guidance throughout this work. I am immensely

grateful to Ms. Poonam Sharma, Mr. Sathish S.B, Mr. Mahesh and Mr. Manjunath M and Dayananda Sagar

Institutions for their continuous support. I am highly indebted to my dad for his immense love and trust on me

throughout my journey of life. I also thank all my M.Tech classmates & beloved friends (JASHD) for their

continuous support and motivation throughout my career.

References [1]. T. Moerland, “Steganography and Steganalysis,” Leiden Institute of Advanced Computing Science, www.liacs.nl/home/tmoerl/privtech.pdf

[2]. J. Silman, “Steganography and Steganalysis: An overview,” Sans Institute, 2001.

[3]. T. Jamil, “Steganography: The art of hiding information is plain sight,” IEEE Potentials, 18:01, 1999.

[4]. H. Wang and S. Wang, “Cyber Warfare: Steganography vs Steganalysis,” Communications of the acm, 47:10, October 2004.

[5]. R.J. Anderson and F.A.P. Petitcolas, “On the limits of Steganography,” IEEE Journal of selected areas in communications, May 1998.

[6]. L.M. Marvel, C.G. Jr Boncelet and C. Retter, “Spread Spectrum Steganography,” IEEE Transactions on image processing, 8:08,1999.

[7]. Po-Yueh Chen and Hung-Ju Lin, “A DWT based approach for image steganography,” International Journal of Applied Science and

Engineering, 2009.

[8]. Mohammad Ali, Mehrabi, Hassan Aghaeinia and Mojtaba Abolghasemi, “Image Steganalysis based on statistical moments of wavelet

subband histogram of images with least significant bit planes,” Congress on Image and Signal Processing, 2008.

[9]. Professor U.L. Kulkarni and Andaljali A Shejul, “A DWT based approach for Steganography using Biometrics,” International Conference

on Data Storage and Data Engineering, June 2010.

[10]. Wangyan and Ling-Di Ping, “A new Steganography algorithm based on Spatial Domain,” International Journal of Symposium on Information Science and Engineering, Volume 3, Issue 1, pp. 408-411, January 2013.

[11]. K Kanimozhi, G. Prabakaran and Dr. R. Bhavani, “Dual Transform Based Steganography Using Wavelet Families and Statistical Methods”

International Conference on Pattern Recognition, Informatics and Mobile Engineering (PRIME), 2013.

[12]. Neha Gupta and Nidhi Sharma, “DWT and LSB based Audio Steganography,” International Conference on Reliability, Optimization a nd

Information Technology-ICROIT, February 2014.

[13]. Prabakaran. G and Bhavani.R, “A Modified Secure Digital Image Steganography Based on Discrete Wavelet Transform,” International

Conference on Computing, Electronics and Electrical Technologies [ICCEET] ,2012.

[14]. John Daemen and Vincent Rijmen, “The design of Rijndael: AES-The Advanced Encryption Standard,Springer 2002, ISBN: 3-54042580-2.

[15]. Mohammad Reza Dastjani and Farahani Andali Pourmohammad, “A DWT based perfect secure and high capacity Image Steganography

method,” International Conference on Parallel and Distributed Computing, Applications and Technologies, 2013.

Author Biographies

Dr. Manjunath H Vreceived his Bachelors of Engineering from Mysore University and completed his Masters

of Science (Engineering) and Doctor of Philosophy degrees from the reputed Indian

Institute of Science (IISc), Bangalore, in the field of Power Electronics and drives. He has

more than 32 years of experience in the field of Electrical Engineering and has worked in

numerous educational and research institutes globally. With his unique nature of discipline

and conduct he has contributed enormously in the field of teaching and remains as a

motivation for hundreds of students. He has worked in many topnotch research projects and

has published more than 20 innovative papers in International Conferences and has 5

globally accepted papers in reputed International Journals. Currently he is working as a

senior professor in the department of Electronics and Communication, Dayananda Sagar College of

Engineering, India. Contact: [email protected]

Nagarjun B received his Bachelors of Engineering in the field of Electronics and Communication in the year

2013 from Visvesvaraya Technological University. He started pursuing his Masters of

Technology in the field of VLSI and Embedded Systems from the year 2013-2015. He has

worked on several research projects and was among the first 51 participants from India to

complete the Semiconductor Manufacturing Course from Indian Institute of Technology

(IIT-Bombay) in the year 2012. His keen interest towards technology has driven him to

participate in 4 National/International Workshops and 3 International conferences across the

country. He has presented papers in 3 National conferences and possesses 2 globally

accepted papers in International Journals. He is also a member of International Association

of Engineers &Universal Association of Computers and Electronics Engineers. Currently he is working towards

his master’s degree in VLSI and Embedded systems from Dayananda Sagar College of Engineering, India.

Contact: [email protected]

mailto:[email protected]

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A Design of Novel Algorithm for Image Steganography Using Discrete Wavelet Transformation on Beagle Board-XM

Documents

keywords image steganography

cover image

image sender

security of information

secret information

process of steganography

steganography process

history of steganography