ABSTRACT calculating PSNR to prove the advantages of the

ABSTRACT

In this
paper, Steganography is used for
covered writing .It is a art and science of writing hidden messages.
Steganography techniques can be utilized for images, a video file or an audio
file hiding. In this paper, performance analysis of image
steganography based on 2 level ,3 level and 4 level DWT associated to colored
images is done. It is a efficient and secure method of hiding secret
message-extracting embedded message into/from a color image will be proposed.
The proposed method will be tested, implemented and analyzed. Efficiency, quality, and
security issues will be done by calculating PSNR to prove the advantages of the
proposed method.

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General
Terms

Security, Wavelet,
Algorithm.

Keywords

Steganography,
DWT, Haar, PSNR,MSE.

 

1.     INTRODUCTION

Steganography
is the art and science of writing hidden messages in such a way that no one,
excluding the sender and deliberated recipient, suspects the message existence,
a form of security through obscurity. The data to be hided is called the secret
message and the medium in which the data is hided is called the covering media.
The covering media (which is in our paper is a color image) containing hidden
message is called stego-image (holding image). The algorithms employed for
hiding the message in the cover medium at the sender end and extracting the
hidden message from the stego-image at the receiver end is called stego system.

Fig 1: Steganography
Model

Figure1 shows the flow of processes that takes place
in image steganography. This paper presents DWT(2-level ,3level & 4-level
wavelet decomposition)based image steganography. Haar wavelets are used in
embedding process of the steganography techniques It has been observed that
wavelets based techniques are most robust as compared to Least Significant Bit
(LSB) based techniques. Also, good quality stego-image is obtained using
wavelet based techniques. For image quality measurement, Peak Signal to Noise
Ratio (PSNR) &Mean Square Error(MSE)is used.It analyze the Peak Signal
Noise Ratio (PSNR) of Haar wavelet. The PSNR block computes the peak
signal-to-noise ratio, in decibels, between two images. This ratio is often
used as a quality measurement between the original and a compressed image. The
higher the PSNR & lower the MSE, the better the quality of the
reconstructed image.

 

2.    
DISCRETE WAVELET TRANSFORM

In numerical analysis and functional analysis, a discrete wavelet transform(DWT) is any transform for which the wavelets are
discretely sampled. It captures both frequency and location information
(location in time). The wavelet transform has gained widespread
acceptance in signal processing and image compression. Recently the JPEG
committee has released its new image coding standard, JPEG-2000, which has been
based upon DWT. Wavelet transform decomposes a signal into a set of basic functions.
These basic functions are called wavelets. Wavelets are obtained from a single
prototype wavelet called mother wavelet by dilations and shifting. The DWT has
been introduced as a highly efficient and flexible method for sub band
decomposition of signals. DWT transform discrete time signal to discrete
wavelet representation. A discrete wavelet transform (DWT) is a sampled wavelet
function. The Discrete Wavelet Transform (DWT), which is based on sub-band
coding, is found to yield a fast computation of Wavelet Transform. It is easy
to implement and reduces the computation time and resources required. Rather
than calculate the wavelet coefficients at every point, the DWT uses only a
subset of positions and scales. This method results in a perfect and more efficient
manner of a wavelet transform. The DWT is similar but more adaptable than the
Fourier series. The discrete wavelet transform
has a huge number of applications in science, engineering, mathematics and
computer science. Most notably, it is used for signal
coding, to represent a discrete
signal in a more redundant form, often as a preconditioning for data compression.

Discrete Wavelet Transform (DWT)
performs repeated procedure at each step given as follows:

 

1)      
Filter the image by 2D-lowpass and high  pass filter

2)      
Sub sample the
result by factor 2

3)      
Decompose
the image into 4 sub-band (LL,LH,HL,HH)

·        
LL-Low to Low frequency coefficient

·        
LH-Low to High frequency coefficient