International Journal of Engineering, Management & Sciences (IJEMS) ISSN-2348 –3733, Volume-2, Issue-6, June 2015
A Secure Steganography Technique using Discrete Wavelet Transformation Priyanka Mudgal, Neeraj Mangalani Abstract— Information security is the basic requirement of today digital communication. Everything is going digital, so the protection of information is much more important. The information security can be classified in to Steganography, Watermarking and Cryptography. steganography is the art and science of hiding secret information into digital media, so that no one can identified the presence inside the media. There are different types of carriers (ie. Image , video etc) for cover media. Steganalysis is type of attack against the steganography technique. To make security in communication system, new steganography method based on Discrete wavelet Transform is proposed. The proposed method hides secret message bits inside the intensity matrix of secret image. Then the Inverse Discrete Wavelet Transformation (IDWT) is applied to obtain the stego image. The performance of proposed design is investigated by comparing cover and stego image in terms of Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE). Index Terms— Information hiding, Cryptography, Steganography, Watermarking, PSNR, MSE.
I. INTRODUCTION The term steganography is indoors from Greek word which basic meaning is “Covered Writing”. Embedder can select any digital cover media (like: image, audio file) that grades in most secure stego file. Today, computers and internet have become the most powerful source of communication among the people in the different parts of the world. Long distances between the people are no longer an obstruction to exchange data between them. However, the safety and security of the exchanged data is of great fear, particularly in the case if it is confidential. There is need to develop good steganography algorithm, because steganography is more power full technique for secure communication. Future is always invisible in steganography that provide more resistance, steganalysis[1 ][2 ][3 ]. II. WAVELET TRANSFORM IN INFORMATION SECURITY Wavelet Transform is the related field in signal and image processing. Which provides the grater advantage in term of robustness and capacity for hiding the data or other types of information.The work described in this paper implements steganography in the Wavelet domain. In Wavelet transform, the original signal, which may be one dimensional, two -Dimensional, three dimensional, is transformed using
Manuscript received June 10, 2015. Priyanka Mudgal, M.Tech(scholar), Jagannath Chaksu,Jaipur. Neeraj Mangalani, Assistant Professor, Jagannath Chaksu,Jaipur
predefined wavelets. The wavelets are orthogonal, or biorthogonal [4][5][6]. A signal can be decomposed into two parts, high frequencies and low frequencies through a wavelet transformation. The low frequencies part is decomposed again into two parts of higher and lower frequencies. The number of decompositions in this process is usually determined by application and length of the original signal. The data obtained from the above decomposition are called the DWT coefficients. Moreover, these coefficients can be used to reconstruct the original signal. This reconstruction is known as the inverse DWT (IDWT)[4].fig 1 shows a 2-D DWT.
Fig1 . 2-Dimensnal Wavelet Transformation for an image file III. IMAGE PIXELS AND INTENSITY The original image and its 8 times magnified view with pixel grid is shown in Figures 2 (a) and 2 (b) respectively.
University, University,
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Please Enter Title Name of Your Paper IV. CONVERSION OF COLOR IMAGE INTO GREYSCALE IMAGE AND ITS PIXEL MATRIX Conversion of a color image to grayscale can be done using several approaches. Different weighting of the primary colors effectively represents the effect of obtaining black-and-white image from color images.
To convert a gray intensity value to RGB, MATLAB function rgb2gray( ); convert the color image into Grayscale image[7][ 8] . The color palette of RGB image is shown in the figure below. The 8 bit grayscale color palette is as shown in the figure 4.
It is also possible to convert grayscale image in to color image by some standard methods which is based on assigning pixel colors via a color palette .
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Consider the Hidden Message: “The true logic� The conversion of the above hidden message in binary string can be done as per the following scheme: Total characters: 14 Total Bits: 112 (=14X8) The complete binary string for the secret message is: 010101000110100001100101001000000111010001110010 011101010110010100100000011011000110111101100111 0110100101100011 THE PROPOSED SYSTEM The proposed Technique first divides the encrypted message bits into group of 8 bits and then hides it into carrier. The carrier media is known as stego media. At the receiver end the embedded message is decrypted, with the help of reverse process of encoding process. The encoding process of proposed technique work in three steps, which are as follow: Step 1. Encrypt hidden message into group of 8 bits. Step 2. Hide bits into secret image media. Step 3. Hide secret image with encrypted message bits into carrier ( cover image ), by using DWT. 2.Hide encrypted message into secret image and get secret image with encrypted message. In step 2 first convert the message in to bits and then embedded it into image. 3. Hide secret image with encrypted message into cover media. 1: Obtain the cover and secret image. 2: DWT is apply on the cover image and secret image 3: Apply mixing operation on the output of step 2, At last apply IDWT to obtain the stego image. Experimental results and analysis of Proposed Technique The performance of work presented in this paper is tested on different images, with the help of MATLAB. The picture quality of stego image is reduced, but these small digration will be acceptable. Performance analyzed, in terms of Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE).
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International Journal of Engineering, Management & Sciences (IJEMS) ISSN-2348 –3733, Volume-2, Issue-6, June 2015 [5]. Singh P, Mann P K; “Fast Fourier Transformation Based Audio PSNR measure the difference between the original and stego Watermarking Using Random Sample”; International Journal of Advanced image. It is defined as follow
Engineering Science and Technology; Vol-9; Issue 1; pp 66-68; 2011. [6]. Abu-Hajar Ahmed; “Imaging Watermarking Survey and Ongoing Current Research”; pp 1-28; San Jose, California. Improving the Performance of Spread Spectrum watermarking by minimizing the cost function 2012. [7]. Chawala G, Saini R, Yadav R, Kamaldeep; “Classification of Watermarking Based upon Various Parameters”; International Journal of Computer Application & Information Technology; Vol-1; Issue 2; pp 16-19; September 2012. [8]. Alomari R S, Al-Jaber Ahmed; “A Fragile Watermarking Algorithm for Content Authentication ”; International Journal of Computing & Information Sciences; Vol-2; Issue 1; pp 27-37; April 2004.
PSNR = 10 log where MSE MSE =
Cover image Paper.Tiff
Secret image Priyanka.Tiff
PSNR 37.5600
Goldhill.Tiff
Priyanka.Tiff 34.1068
Pirate.Tiff
Priyanka.Tiff 32.2742
Table: 1 Picture quality measurement for different image with the help of PSNR The image excellence measurements for some of the experienced images have been illustrated in Table. The results show high PSNR and low MSE values which indicate the effectiveness and accuracy of our proposed method.
REFERENCES [1]. S. Katzenbeisser and F. A. P. Petitcolas, “Hiding Techniques for Steganography and Digital Watermarking”, S. Katzenbeisser and F. A. P. Petitcolas, Eds. Boston, MA: Artech House, ISBN 1-58053-035-4 Volume 1, 2000. [2]. Transactions on K. Su, D. Kundur, and D. Hatzinakos, “Statistical Invisibility for Collusion-resistant Digital Video Watermarking,” IEEE Multimedia 2004 .IEEE Transactions On Multimedia,VOL.7,No.1 Feburary 2005. [3]. K. Su, D. Kundur, and D. Hatzinakos, “Spatially Localized Image-dependent Watermarking for Statistical Invisibility and Collusion Resistance,” IEEE Transactions on Multimedia 2004 . Volume 7 Issue 1, February 2005 . [4]. M. Sifuzzaman, M.R. Islam and M.Z. Ali, “Application of Wavelet Transform and its Advantages Compared to Fourier Transform”, Journal of Physical Sciences, Vol. 13, pp.121-134, 2009.
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