International Journal for Science and Emerging ISSN No. (Online):2250-3641 Technologies with Latest Trends 6(1): 29-37 (2013) ISSN No. (Print): 2277-8136

Transform Domain Analysis of Image Steganography

Gurmeet Kaur* and Aarti Kochhar**

*Department of Electronics and Communication Engineering, CEM Kapurthala.

**Department of Electronics and Communication Engineering, DAVIET Jalandhar

(Received 10 April 2013 Accepted 13 April 2013)

Abstract: Steganography is a means of data hiding in images for covert transmission. In recent years, Steganography

and Steganalysis are two important areas of research that involve a number of applications. These two areas of

research are important especially when reliable and secure information exchange is required. Steganography is an

art of embedding information in a cover image without causing statistically significant variations to the cover image.

Steganalysis is the technology that attempts to defeat Steganography by detecting the hidden information and

extracting. In this paper we propose an image Steganography that can verify the reliability of the information being

transmitted to the receiver. This paper is based on the comparison of the DCT and DWT method. This paper presents

a novel technique for Image steganography based on DWT, where DWT is used to transform original image (cover

image) from spatial domain to frequency domain. The experimental results show that the algorithm has a high

capacity and a good invisibility as compare to DCT. Moreover PSNR of cover image with stego-image shows the

better results in comparison with existing Steganography approaches. Also DWT method is best when we have to

increase the pay load capacity.

Index TermsSteganography, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT).

1. INTRODUCTION The growing possibilities of modem

communications need the special means of

security especially on computer network. The

network security is becoming more important as

the number of data being exchanged on the

Internet increases. Therefore, the confidentiality

and data integrity are required to protect against

unauthorized access. This has resulted in an

explosive growth of the field of information hiding

area, which encompasses applications such as

copyright protection for digital media,

Watermarking, fingerprinting, and Steganography.

All these applications of information hiding are

quite diverse. In watermarking applications, the

message contains information such as, owner

identification and a digital time stamp, which is

usually applied for copyright protection. With

fingerprint, the owner of the data set embeds a

serial number that uniquely identifies him as the

owner. This adds to copyright information and

makes it possible to trace any unauthorized usage

of the data set. Steganography hides the secret

message within the host data set and its presence is

imperceptible and is to be reliably communicated

to a receiver [1].

The word steganography comes

from the Greek Steganos, which means covered or

secret and graphy means writing or drawing i.e.

Steganography means literally covered writing.

With the existing communication methods,

Steganography can be used to carry out hidden

messages. The main goal of Steganography is to

communicate securely in a completely

undetectable manner and to avoid drawing

suspicion to the transmission of a hidden data. If a

Steganography method causes someone to suspect

there is secret information in a carrier medium,

then the method become useless. Until recently,

information hiding techniques received very much

less attention from the research community and

from industry than cryptography. Cryptography

and steganography are widely used in the field of

data hiding and has received significant attention

from both industry and academia in the recent

past. Former conceals the original data but latter

conceals the very fact that data is hidden.

Steganography provides high level of secrecy and

security by combining with cryptography.

Throughout history Steganography has been used

to secretly communicate information between

people.

2. IMAGE STEGANOGRAPHY

TECHNIQUES

Based on the analyses of steganography tools algorithms, we partition these tools into two

categories:

Spatial Domain Based Steganography:

Spatial steganography mainly includes LSB (Least

Significant Bit) steganography Least significant

bit (LSB) insertion is a common, simple approach

to embedding information in a cover image. The

least significant bit (in other words, the 8th bit) of

some or all of the bytes inside an image is changed

to a bit of the secret message.

Transform Domain Based Steganography:

Basically there are many kinds of power level

transforms that exist to transfer an image to its

frequency domain, some of which are Discrete

Cosine Transform, KL Transform and Wavelet

Transform.

The Discrete Cosine Transform (DCT):

This method is used, but similar transforms are for

example the Discrete Fourier Transform (DFT).

These mathematical transforms convert the pixels

in such a way as to give the effect of spreading

the location of the pixel values over part of the

image [5]. The DCT transforms a signal from an

image representation into a frequency

representation, by grouping the pixels into 8 8

pixel blocks and transforming the pixel blocks into

64 DCT.

The Two-Dimensional DCT

The 2-D DCT is a direct extension of the 1-D case

and is given by

Discrete Wavelet Transform

Wavelets are special functions which (in a form

analogous to sins and cosines in Fourier analysis)

are used as basal functions for representing

signals. The discrete wavelet transform (DWT) we

applied here is Haar-DWT, the simplest DWT. In

Haar-DWT the low frequency wavelet coefficient

30 Kaur and Kochhar

are generated by averaging the two pixel values

and high frequency coefficients are generated by

taking half of the difference of the same two

pixels. A signal is passed through a series of filters

to calculate DWT. Procedure starts by passing this

signal sequence through a half band digital low

pass filter with impulse response h (n).Filtering of

a signal is numerically equal to convolution of the

tile signal with impulse response of the filter.

A half band low pass filter removes all frequencies

that are above half of the highest frequency in the

tile signal. Then the signal is passed through high

pass filter.The two filters are related to each other

as

For 2-D images, applying DWT (Discrete Wavelet

Transform) separates the image into a lower

resolution approximation image or band (LL) as

well as horizontal (HL), vertical (LH) and

diagonal (HH) detail components as shown in

figure.

Hao-tian Wu, Huang [9] in steganographic

algorithm is proposed for JPEG Image by

modifying the block DCT coefficients. Firstly, an

embedding algorithm called LSB+ matching is

generated to approximately preserve the marginal

distribution of DC coefficients. We further divide

the DCT coefficients into four frequency bands,

including the direct current (DC), low frequency,

middle-frequency, and high-frequency. Via matrix

encoding, low data hiding rate and high

embedding efficiency are achieved in high-

frequency band, while the hiding rate is increased

in the middle-frequency and DC bands, and

highest in the low-frequency band. In addition, a

coefficient selection strategy is employed to make

the hidden message less detectable. The proposed

algorithm is implemented on a set of 10000

images and tested with four steganalytic

algorithms.

Mamta Juneja, Parvinder Singh Sandhu [11]

represented Robust image steganography

technique based on LSB (Least Significant Bit)

insertion and RSA encryption technique.

Steganography is the term used to describe the

hiding of data in images to avoid detection by

attackers. Steganalysis is the method used by

attackers to determine if images have hidden data

and to recover that data. The application discussed

in this paper ranks images in a users library based

on their suitability as cover objects for some data.

By matching data to an image, there is less chance

Kaur and Kochhar 31

of an attacker being able to use steganalysis to

recover the data. Before hiding the data in an

image the application first encrypts it. The

steganography method proposed in this paper and

illustrated by the application is superior to that

used by current Steganography tools.

Yi-zhen Chen, Zhi Han, Shu-ping Li, Chun-hui

Lu, Xiao-Hui Yao [13] an improved adaptive

steganography algorithmSVBA algorithm,

which fully analyzes the statistical properties and

adopts HVS features. SVBA algorithm first

divides the image into 8*8 blocks and analyzes the

mean, variance and entropy value of grey by

block, then sets a sensitivity vector for each block

with considering HVS features and adjusts the

steganography schema dynamically according to

the block sensitivity vectors. Simulation

experiment results onMatlab7.0 shows this

algorithm has a balanced performance on

efficiency, capacity, imperceptibility and

robustness. Mohammad Javad Khosravi, Samaneh

Ghandali [20] novel steganography technique

based on the combination of a secret sharing

method and wavelet transform is presented. In this

method, a secret image is shared into some shares.

Then, the shares and Fletcher- 16checksum of

shares are hidden into cover images using an

integer wavelet based Steganography technique.

3. MODEL

A. Definitions

(i)Cover image: It is defined as the original

image into which the required information is

embedded. It is also termed as carrier image. The

information should be embedded in such a manner

that there are no significant changes in the

statistical properties of the cover image.

(ii)Stegoimage: It is a unified image obtained by

the combination of the payload and cover image.

(iii)Perceptibility: It describes the ability of a

third party (not the intended recipient) to visually

detect the presence of hidden information in the

stego image. The embedding algorithm is

imperceptible when used on a particular image if

an innocent third party, interested in the content of

the cover image, is unaware of the existence of the

payload. Essentially this requires that the

embedding process not degrade the visual quality

of the cover image.

(iv)Robustness: It characterizes the ability of the

payload to survive the embedding and extraction

process, even in the face of manipulations of the

stego image such as filtering, cropping, rotating

and compression.

(v)Security: It is inability of adversary to detect

hidden images accessible only to the authorized

user. The quality factor can enhance the security

of the image. A steganographic image is perfectly

secure when statistical data of the cover and stego

images are identical

B. Error Analysis:

(i) Bit Error Rate: For the successful recovery

of the hidden information the communication

channel must be ideal but for the real

communication channel, there will be error while

retrieving hidden information and this is measured

32 Kaur and Kochhar

by BER. The cover image is represented as cov

and stego image as steg in the given equation

Where i is the pixel position

(ii) Mean Square Error: It is defined as the square

of error between cover image and the stego image.

The distortion in the image can be measured using

MSE.

(iii) Peak Signal to Noise Ratio: It is the ratio of

the maximum signal to noise in the stego image.

4. ALGORITHMS OF STEGANOGRAPHY

A.DCT Based Steganography

Algorithm to embed text message:-

Step 1: Read cover image.

Step 2: Read secret message and convert it in

binary.

Step 3: The cover image is broken into 88 block

of pixels.

Step 4: Working from left to right, top to bottom

subtract 128 in each block of pixels.

Step 5: DCT is applied to each block.

Step 6:Each block is compressed through

quantization table.

Step 7: Calculate LSB of each DC coefficient and

replace with each bit of secret message.

Step 8: Write stego image.

Algorithm to retrieve text message:-

Step 1: Read stego image

Step 2: Stego image is broken into

88 block of pixels.

Step 3:Working from left to right, top to bottom

subtract 128 in each block of

pixels.

Step 4: DCT is applied to each block.

Step5:Each block is compressed through

quantization table.

Step6:Calculate LSB of each DCT coefficient.

Fig(a) Original Desert Image and Stego image

Kaur and Kochhar 33

Fig (b) Histogram Analysis of Original image

and Stego Image

Fig(c) Original Jellyfish Image and Stego

image

Fig (d) Histogram Analysis of Original image

and Stego Image

B. Wavelet Based Steganography

Algorithm to embed text message

Step 1: Read cover image.

Step 2: Read secret key and convert it in binary.

Step 3: Perform the DWT and convert the image

into four sub bands.

Step 4: Embed the secret key by changing the LSB

of the each sub band

Step 5: Generate the stego image.

Algorithm to retrieve text message:-

Step 1: Read stego image.

Step 2: Calculate the IDWT.

Step 3: Retrieve the message

34 Kaur and Kochhar

Fig(a) Original Jellyfish Image and Stego image

+

Fig (b) Histogram Analysis of Original image

and Stego Image

Fig(c) Original Jellyfish Image and Stego

image

Fig (d) Histogram Analysis of Original image

and Stego Image

Kaur and Kochhar 35

5 PERFORMANCE & RESULTS

The analysis of DCT based and DWT based

Steganography has been done on basis of

parameters like PSNR, MSE, Processing time,

capacity. PSNR computes the peak signal to noise

ratio, in decibels, between two images. This ratio

is used as a quality measurement between two

images. If PSNR ratio is high then images are best

of quality. Capacity is used to calculate the no of

bits that can be embedded in the cover image.

Processing time is the time taken by the MATLAB

Software to execute the program.

Method

Picture

name

PSN

R

MS

E

PROCES

SING

TIME

(seconds)

Capacity(

Bits)

DCT

(8X8)

DESER

T 37.

54

11.

45

1.95 24576

DCT(8

X8)

JELLT

FISH 39.

35

7.5

4

1.63 24576

DWT DESERT

49.

76

0.6

8

1.40 524288

DWT JELLYFISH

49.

85

0.6

7

1.25 524288

Table1: Simulation results for DCT & DWT

Method

6. CONCLUSION

The Steganography is covert communication to

protect confidential information. Here we

presented a comparative study of DCT and the

DWT Methods. Both come under transform

domain analysis. Both the methods have good

imperceptibility and also Robustness against

statistical attacks. But as we know the major aim

of the Steganography is to increase the robustness

against attacks and also to increase the payload

capacity. Our proposed method increases both the

PSNR and also the capacity which is less in DCT

Method.

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