Digital Image Steganography Using Adsp Computer Science Essay

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Information hiding is a technique that inserts secret messages into a cover file, so that the existence of the messages is not apparent. Research in information hiding has tremendous increased during the past decade with commercial interests. Information hiding techniques that are used today include watermarking and Steganography. The major concern of watermarking is to protect the ownership of a digital content, while Steganography is to embed secret messages into digital content so that the secret messages are not detectable. Although many Steganography techniques have been developed for digital images, most of them are irreversible. That is, the original image cannot be recovered to its original state after the extraction of secret data. A lossless or reversible Steganography is defined as an original image can be completely recovered from the stego- image after the embedded data has been extracted. This technique has been focused on spatial uncompressed domain recently which include Least Significant Bit algorithm (LSB), and is considered more challenging to carry out in the compressed domain. In this, we propose a lossless, compressed domain Steganography technique for compressed images based on the Discrete Wavelet Transform (DWT). The location map which is often used in lossless Steganography is not required in our scheme. Therefore, the hiding capacity is independent of the compressed codes. Moreover, the stego-image preserves the same image quality as the original compressed images. The experimental results show that the proposed scheme is not only easy to implement but also provides an efficient mechanism for lossless data embedding.

Keywords- Steganography, discrete wavelet transform, spatial domain, Least Significant Bit (LSB) algorithm.

Introduction

Steganography is the art and science of hiding secret data in plain sight without being noticed within an innocent cover data so that it can be securely transmitted over a network. The word Steganography is originally composed of two Greek words steganos and graphia, which means "covered writing". The use of Steganography dates back to ancient times where it was used by romans and ancient Egyptians. Any digital file such as image, video, audio, text or IP packets can be used to hide secret message. Generally the file used to hide data is referred to as cover object, and the term stego-object is used for the file containing secret message.

Among all digital file formats available nowadays image files are the most popular cover objects because they are easy to find and have higher degree of distortion tolerance over other types of files with high hiding capacity due to the redundancy of digital information representation of an image data. There are a number of Steganographic schemes that hide secret message in an image file; these schemes can be classified according to the format of the cover image or the method of hiding. We have two popular types of hiding methods; spatial domain embedding and transform domain embedding.

The Least Significant Bit (LSB) substitution is an example of spatial domain techniques. The basic idea in LSB is the direct replacement of LSBs of noisy or unused bits of the cover image with the secret message bits.LSB is the most preferred technique used for data hiding because it is simple to implement offers high hiding capacity, and provides a very easy way to control stego-image quality [2]

The other type of hiding method is the transform domain techniques which appeared to overcome the robustness and imperceptibility problems found in the LSB substitution techniques. There are many transforms that can be used in data hiding, the most widely used transforms are; the discrete cosine transform (DCT) the discrete wavelet transform (DWT) and the discrete Fourier transform (DFT). Most recent researches are directed to the use of DWT since it is used in the new image compression format JPEG2000 and MPEG4, In [9] the secret message is embedded into the high frequency coefficients of the wavelet transform while leaving the low frequency coefficients sub band unaltered.

DISCRETE WAVELATE TRANSFORM(DWT)

It preserves the details in an image and removing noise efficiently. It uses multi-resolution technique by which different frequencies are analysed with different resolutions. Wavelets are localized waves & have their energy concentrated in time or space and are suited to analysis of transient signals. The Discrete Wavelet Transform (DWT) 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. [6]

Wavelet Family

There are a number of basic functions that can be used as the mother wavelet for Wavelet Transformation. Since the mother wavelet produces all wavelet functions used in the transformation through translation and scaling, it determines the characteristics of the resulting Wavelet Transform. Therefore, the details of the particular application should be taken into account and the appropriate mother wavelet should be chosen in order to use the Wavelet Transform effectively.

There are different types of wavelet families such as Haar, Daubechies4, Coiflet1, Symlet2, Meyer, Morlet, Mexican Hat etc.

Haar Wavelet Transform

In proposed system we are using Haar Wavelet type because it is the simplest of all wavelet transform. In this the low frequency wavelet coefficients are generated by averaging the two pixel values and high frequency coefficients that are generated by taking half of the difference of the same two pixels. The four bands obtained are LL, LH, HL, and HH which is shown in Fig 1.

The LL band is called as approximation band, which consists of low frequency wavelet coefficients, and contains significant part of the spatial domain image. The other bands are called as detail bands which consist of high frequency coefficients and contain the edge details of the spatial domain image.

Step1: Column wise processing to get H and L

H = (Co¯ Ce)

L = (Ce-[H /2])

Where Co and Ce is the odd column and even column wise pixel values.

Step 2: Row wise processing to get LL, LH, HL and HH,

Separate odd and even rows of H and L,

Namely, Hodd- odd row of H

Lodd- odd row of L

Heven - even row of H

Leven - even row of L

LH = Lodd - Leven

LL = Leven- LH/2

HL = Hodd- Heven

HH = Heven -HL/2

LEAST SIGNIFICANT BIT (LSB) ALGORITHM

The Least Significant Bit (LSB) substitution is an example of Spatial domain techniques. The basic idea in LSB is the direct replacement of LSBs of noisy or unused bits of the cover image with the secret message bits. Till now LSB is the most preferred technique used for data hiding because it is simple to implement offers high hiding capacity, and provides a very easy way to control stego-image quality [2] but it has low robustness to modifications made to the stego-image such as low pass filtering and compression [3] and also low imperceptibility. Algorithms using LSB in grayscale images can be found in [4, 5, 6].

The other type of hiding method is the transform domain techniques which appeared to overcome the robustness and imperceptibility problems found in the LSB substitution techniques. There are many transforms that can be used in data hiding, the most widely used transforms are; the discrete cosine transform (DCT) which is used in the common image compression format JPEG and MPEG, the discrete wavelet transform (DWT) and the discrete Fourier transform (DFT). Most recent researches are directed to the use of DWT since it is used in the new image compression format JPEG2000 and MPEG4, examples of using DWT can be found in [9, 10]. In [9] the secret message is embedded into the high frequency coefficients of the wavelet transform while leaving the low frequency coefficients sub band unaltered.

In the current endeavour, an image file with ".jpg" extension has been selected as host file. It is assumed that the least Significant bits of that file should be modified without degrading the image quality.

There are 3 types of this algorithm:

2 pixel per character

4 pixel per character

8 pixel per character

As move on from first type to last type the data hiding capacity decreases but quality of image increases.

PROPOSED SYSTEM

Figure 2 shows the hardware interface of our system. We are using BF532 processor. There are two parts of the process first part is embedding and other is extraction.

Figure 2. The Hardware interface diagram of proposed system

Embedding

From personal computer we are getting input image as cover image then giving that to processor It process that image. In that firstly it compress the image with the help of Discrete Wavelet transform (DWT).Then in that compressed image embed the secret data which we have to passed to get stego image. This embedding process is done with the help of Least Significant Bit (LSB) algorithm.

Extraction

After getting stego stego image at transmitter, at receiver exactly reverse process is done. From stego image extract the secret data then decompress the image with the help of Inverse Discrete Wavelet Transform (IDWT).Finally we get our original image.

FLOW DIAGRAM

Figure 3. The flow diagram of proposed system

Step 1. First we take input image as cover image in .jpg format.

Step 2. Then apply Discrete wavelet transform mechanism to compress the image.

Step 3. With the help of LSB algorithm, embed the secret data and compress image we get stego image at transmitter side.

Step 4. Exactly reverse process is done at the receiver side.

Step 5. We are extracting secret data from stego image.

Step 6.Then by decompressing the image we get the original cover image.

ERROR METRICS

A performance measure in the stego image is measured by

means of two parameters namely, Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).

The MSE is calculated by using the equation,

In the previous equation, M and N are the number of rows and columns in the input images, respectively.

Then the block computes the PSNR using the following equation:

In the previous equation, R is the maximum fluctuation in the input image data type. For example, if the input image has a double-precision floating-point data type, then R is 1. If it has an 8-bit unsigned integer data type, R is 255,etc.

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