An efficient hash based steganography technique for text message using col

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-

6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME

150

AN EFFICIENT HASH BASED STEGANOGRAPHY TECHNIQUE FOR

TEXT MESSAGE USING COLOR IMAGES

Saurabh Singh1, Dr. Ashutosh Datar

2

1PG Student, Department of Electronics & Communication Engineering, S.A.T.I. Vidisha,

INDIA (M.P.) 2Head, Department of Bio-Medical Engineering, S.A.T.I. Vidisha, INDIA (M.P.)

ABSTRACT

In this paper, a hash based approach for color image Steganography using canny edge

detection method is proposed. One of the advantages of using edge detection technique is to secure

the data. For encoding the text data in an image Steganography procedure is followed. Edge

detection is done by canny method and then hash function is used to embed text data in the RGB

color image. The hash is a fast and secure approach for image Steganography. This found that canny

edge detection offers superior performance for detecting edges in an image. Large edge detected

image is preferred for secure Steganography. The proposed method provides a better security and

supports different types of file format like-jpg, jpeg, bmp, tiff etc.

Keywords: Edge detection, Steganography, Encoding & Decoding, Hash function.

I. INTRODUCTION

Steganography (Steganos-“Covered”, Graphie-“Writing”) is the art and science of writing

hidden messages in such a way that no one apart from the intended recipient knows of the existence

of the message [1]-[5]. The first step in embedding and hiding information is pass both the secret

message and the cover message into the encoder. Inside the encoder, one or several protocols may be

implemented to embed the secret information into the cover message. The type of protocol will

depend on the information in fig.1. contents to be embedded.

INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &

TECHNOLOGY (IJCET)

ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), pp. 150-162 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com

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Figure.1. Block diagram of Steganography

For example, an image protocol is used to embed information inside images. A key is often

needed in the embedding process. This can be in the form of public or private key to encode the

secret message with sender private key and the recipient can decode it using sender public key. In

embedding the information this way, the chance of a third, party attacker getting hold of the Stego-

object and decoding it to find out the secret information is greatly reduced.

Hence, the embedding process inserts a mark M in the object I. Random number generator is

generally used to produced a key K, which is embedded in I. Hence, the marked object I is generated

as: � � � ��

The output of the encoder will be a Stego object contains original cover image and embedded

secret information. The Steganography algorithm must ensure that the Stego object bear a very close

resemblance to original cover object. The Stego object then may be sent through usual

communication channels to intended recipients. The Stego object at receiver end must be decoded to

retrieve the secret information. In decoding process, Stego object is decoded with the help of the

public or private key. Depending upon the encoding technique, sometimes the original cover image

is also needed in decoding process. After successful decoding, the embedding secret information

may be extracted and viewed. There are many algorithms available for image Steganography.eg.

LSB (Least significant bit) method, masking etc. LSB method is simple and popular method but its

security aspect is poor. Hash function based approach is robust and convenient. A hash function

converts variable lengths data of fixed length. The hash function returns hash codes or checksum or

simple hashes. [Some more about hash function coding and decoding].

II. EDGE DETECTION

Edge in an image contains shape information. Edges represent abrupt transition of gray level in

an image. The edge detection is the process of finding such transition, which represents some

physical boundary in the scene [6]–[11]. The goal of edge detection is to obtain a line drawing of the

scene in an image, so as to further extract information regarding important features [Fig.2.]. This

information may then be used in pattern recognition algorithms. Below figure shows the edge

detection example-

Cover

Secret

Message

Image

Encoder

Stego

Cover

Secret

Message

Decoder

Key Key



152

Figure.2. Edge detection example

Basic steps in edge detection-

1) Smoothing: Noise removal/ suppression with preservation of edges.

2) Enhancement: Sharpening of edges by filtering.

3) Detection: Determination with certainty if pixel belongs to an edge or not.

4) Localization: Determining exact location of edge.

There are various operators which are used in edge detections. Some note worthy edge detectors are-

1) Robert Edge Detection

It is one of early detector, which is simply and efficient. It highlights region of high spatial

frequency which may usually corresponds to edges. It then assign pixels values in output images;

correspond to estimated complete magnitude of spatial gradient of the input image.

2) Sobel Edge Detection

It is central difference based operator giving higher weight to central pixel. It is an

approximation of first order derivative of Gaussian kernel. Sobel operators which are shown in the

masks below:

3) Prewitt Edge Detection

It is also based on central difference where operator measures two components. Vertical edge

components and horizontal edge components for current pixel.

Saurabh.jpeg



153

4) Kirsch Edge Detection

This operator or compass kernel is a non-linear edge detector. It finds maximum edge

strength in predetermine direction. It is represented by the mask:

E � ��3 �3 5�3 0 5�3 �3 5 NE � ��3 5 5�3 0 5�3 �3 �3

N � � 5 5 5�3 0 5�3 �3 �3 NW � � 5 5 �35 0 �3�3 �3 �3

W � �5 �3 �35 0 �35 �3 �3 SW � ��3 �3 �3 5 0 �3 5 5 �3

S � ��3 �3 �3�3 0 �35 5 5 SE � ��3 �3 5�3 0 5�3 5 5

5) LoG (Laplacian of Gaussian) Edge Detection

This operator has smoothing effects through convolution with Gaussian shape kernel. It is

followed by application of laplacian operator.

G� = � 0 �1 0�1 4 �1 0 �1 0 and G� = ��1 �1 �1�1 8 �1�1 �1 �1

6) Canny Edge Detection

Canny edge detector is an optional edge detector algorithm which has good detection, good

localization capability with minimal response. The problem with above edge detector is that the

detected edge may not be complete due to noise, breaks in edges from non-uniform illumination,

unwanted effects due to spurious intensity is discontinuity etc. Hough transform is one of the popular

approaches to link edge pixels into meaningful edges. The Hough transform [12]-[15] has two steps:

1. Peak detection- For each detected peak the location of non-zero pixels that contributes to

that peak is determined.

2. Line detection and linking: After the peaks are identified, there corresponding line

segments with starting and ending points are determined.

Figure.3. Hough transform based edge detection



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III. HASH FUNCTION

To create a digest of the message, hash function [16]-[18] is used. The hash function creates a

fixed digest from a variable –length message (fig.4).

Figure.4. Signing the digest

The two most common hash functions are called MD5 (Message Digest 5) and SHA-1

(Secure Hash Algorithm 1). The first one produces a 120-bit digest. The second produces a 160-bit

digest. Hash functions have must two properties to guarantee it success.

1. Hashing is one-way; the digest can only be created from the message, but not vice-versa.

2. Hashing is one-to-one function; there is little probability that two message will create the same

digest.

One practical use is a data structure called a hash table where the data is stored associatively.

Searching for a person's name in a list is slow, but the hashed value can be used to store a reference

to the original data and retrieve constant time (barring collisions). It is easy to generate hash values

from input data and easy to verify that the data matches the hash, but hard to 'fake' a hash value to

hide malicious data. Hash functions are also used to accelerate table lookup or data comparison tasks

such as finding items in a database, detecting duplicated or similar records in a large file, finding

similar stretches in DNA sequences, and so on.

Different types of hash function are available but most types of hashing function the choice

of the function depends strongly on the nature of the input data. Types of hash function-

1. Trivial hash function

2. Perfect hash function

3. Minimal perfect hash function

4. Cryptographic hash function

5. Hashing with checksum

6. Hashing variable length data

7. Modulus hash function etc.

In this paper, Modulus hash function has been used that is the mod of the division hash

function could be h = z mod n (the remainder of z divided by n). This is the combination of addition,

subtraction, multiplication and division function.

It is of two types:

1. Symmetric hash function-This type of hash function ensure that sender and receiver used the same

key for data encoding and decoding.

2. Asymmetric hash function- This type of hash function ensure that sender and receiver used the

different keys for data encoding and decoding.

In our methodology, symmetric modulus hash function has been used for text data hiding and

retrieval purpose in the Red, Green and Blue pixels of the image.

Message

Hash

function

Message

Digest

(Variable length) (Fixed Length)



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IV. APPROACH FOR STEGANOGRAPHY

This approach allows the user to embed their secret textual information in images in a way

that can be invisible and doesn't degrade or affect the quality of the original image [2]-[4]. Users

want to make their information secure or protect their work from piracy. This approach is able to

manipulate with different file formats e.g. bitmap, jpeg, jpg, gif and tiff etc.

The Steganography steps for the RGB image used in this work are (fig.5).

1. Input Image-An input interface is provided so that a user can input a (bmp, jpg, gif or tiff

etc.) image for hiding personal data for privacy purposes.

2. Input Textual Data- Input the text file containing the textual data which the user wants to

code in the image. The input text file is read by the system.

3. Coding Data in Image-For coding textual data in the image, a hash-based algorithm is used.

Basic purpose of using the hash-based algorithm is to pick pixels randomly to store text data.

The text data are stored in red, blue and green pixel of the employed RGB image. Once the

image is encoded, it is transmitted to intended user.

4. Decoding Data from Image-For decoding textual data in the image, the hash key is used that

was generated during coding. The pixels (red, green, blue byte) values of each position are

read one by one and generated characters concatenated to form a complete message string.

Figure.5. Basic block diagram of color image Steganography using hash algorithm

V. PROPOSED METHODOLOGY

The image and the message are encoded at the sender end. The flow chart for encodings and

decoding are given in fig (6) & fig (7) respectively [19]–[25].

1. Encoding Procedure:

The encoding procedure involves of the RGB image and message string. Application of

canny operator provides edge information, which is linked by using Hough transform. The edge

pixels are identified and are used as a hash key (K). The hash function uses hash key K, input image

and text data to generate pattern. This pattern contains the information regarding position of pixels

for hiding message data. The pixels are chosen from Red, Green and Blue plane of the image. The

output contains encoded image (Stego-object) which is sent to the intended user. The flow chart of

the encoding procedure shown in fig. (6).

Input/output text file and image file

Hash function

Encoded image with

Text data

Decoding Encoding

Decoding Encoding



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Figure.6. Flow chart of the encoding procedure

Check

Remainder

value, in Mod

function

Start

Read RGB

Edge identification and linking

by canny edge detection &

Hough transform

Pixel identification,

Hash key

Read

message

string L

Hash function &

hash digest, I

Is I � L

Modify green

pixel

Modify

blue

Modify

red pixel

Generate Stego-

object End

No Yes

= 1 = 0



157

2. Decoding Procedure: It is the reverse process of encoding. In this process received Stego-object

contains original image along with hidden message, which is to be extracted. The input hash key is

used to generate hash values, which contain information about location of pixels where message is

stored. The message string is extracted from Red, Green and Blue pixels of the image. The output

contains extracted text message. The flow chart of decoding the Stego-object is shown in fig. (7).

Figure.7. Flow chart of the decoding procedure

Start

Received Stego

object

Is I � L

Check

Remainder

value in

Mod

function

Read green

pixel data

Read blue

pixel data

Read red pixel

data

Output text

file contains End

Hash function &

hash digest, I

Retrieve

encoded byte

No Yes

= 1 = 0



158

VI. IMAGE QUALITY PARAMETER

Image quality is a characteristic of an image that measures the perceived image degradation

(typically, compared to an ideal or perfect image) [5]. Two parameters are there:

1. MSE (Mean square error)

It is defined as the squared difference between the original image and estimated image.

Where: X = original value, = stego value and N = number of pixels

2. PSNR (Peak signal to noise ratio):-

Peak Signal-to-Noise Ratio, often-abbreviated PSNR, is an engineering term for the ratio

between the maximum possible power of a signal and the power of corrupting noise that affects the

fidelity of its representation [10]. Because many signals have a very wide dynamic range, PSNR is

usually expressed in terms of the logarithmic decibel scale. PSNR is most easily defined via

the mean squared error (MSE):

Where: L = maximum value, MSE = Mean Square Error

VII. SIMULATION RESULTS

In the proposed methodology three different color image Lena, Peppers and Baboon of

standard size are used. Simulation results are performed in Matlab2010a version. A comparative of

test images under simulation and encoded images corresponding histograms, detected edges are

shown in fig (8) - fig (12).

The quantitative results are shown in Table-I. On making comparison between original

images and encoded images and their respective histograms. It is evident that no noticeable change is

observed. Hence, it is also observed that the Steganography process did not significantly altered the

image qualities. It is also observed from fig. (10) that Baboon.jpeg offers maximum edge pixels

while the Peppers.jpeg has the lower edge pixels. Hench the PSNR of Baboon.jpeg is higher that two

others except for first case when Lena.jpeg has high value in fig. (13). It may be due to small color

variation in Lena.jpeg. Further, a comparison of results of proposed approach is made with Kritika

et.al. [19]. The proposed method shown superior results in all cases under simulation.

Figure.8. Original images of size 512x512

Lena.jpeg Peppers.jpeg Baboon.jpeg



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Figure.9. Encoded Images with Text Data(2547 Bytes)

Figure.10.After Applying Canny Edge Detection Algorithm

Figure.11. Histogram of original image

Figure.12. Histogram of encoded image with

text data (2547 Bytes)







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Table I

PSNR Output for text encoding

S.No.

Message

length

Lena �512 � 512�

Peppers �512 � 512�

Baboon �512 � 512� Kritika

et.al.

[19]

Proposed Kritika

et.al.

[19]

Proposed Kritikaet.al.

[19]

Proposed

1.

849

Bytes

46.7704

47.5599

42.4704

43.7486

44.5141

46.5188

2.

1698

Bytes

43.1161

43.7728

39.8468

41.9565

41.4249

44.6801

3.

2547

Bytes

40.4854

41.5473

37.9358

40.3967

37.7590

43.2978

4.

3396

Bytes

39.5810

40.0503

36.7382

39.4238

36.3541

42.3406

5.

4287

Bytes

38.5342

39.6310

35.7352

38.6527

35.1693

41.5895

Graphical Results of Calculated PSNR

Figure.13. The graph shows that as the message length increases PSNR decreases, baboon image

gives better results than Lena and peppers image

38

39

40

41

42

43

44

45

46

47

48

849 1698 2547 3396 4287

Lena

Peppers

Baboon

P

S

N

R

in

(dB)

Message length (Bytes)



161

VIII. CONCLUSION

The work proposed an approach for Steganography using RGB image and text message. The

results, in comparison to [19] are superior. In this work, all RGB color planes of image are used for

hiding secret text message without loss of image quality. Hence, this approach offers high message

carrying capacity and high-encoded image quality. The Human Visual System (HSV) cannot detect

these fine changes in original image, made due to Steganography process. Moreover, use of hash

function improves its robustness. Hence, it may be adopted for Steganography using color images.

This Color image (RGB) Steganography technique can be extending to further audio, video etc.

ACKNOWLEDGMENT

The author wish to acknowledge Dr. S.N. SHARMA (Head, Department of E & C

Engineering) for his support.

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