Reversible Steganographic Technique Based on IWT for Enhanced Security

Reversible Steganographic Technique Based on IWT for Enhanced Security V. Dinesh, M. R. Kiran & A. Nepolian A.R Engineering College, Villupuram E-mail : er.vdinesh@gmail.com, kira21213@gmail.com, dknepolianfrds@gmail.com Abstract Steganography is used to hide a secret message within a cover image, thereby yielding a stego image such that even the trace of the presence of secret information is wiped out. The purpose of steganography is to maintain secret communication between two users. Steganography has several technical challenges namely high hiding capacity and imperceptibility. Steganographic technique with Integer Wavelet transform (IWT) and reversible technique provides high hiding capacity, high security, low complexity and good visual quality. Reversible technique in the sense extraction of the original input image. Steganography technique is of different types such as image steganography where image file is used as cover file, audio steganography where audio file is used as cover file and video file where video is used as cover file. Image steganography is the best technique for hiding information with more security than the other techniques. Keywords IWT, Reversible Data Embedding, Reversible Data Extraction, PSNR and MSE. I. INTRODUCTION Steganography means to hide secret information into innocent data. Digital images are ideal for hiding secret information. An image containing a secret message is called a cover image. First, the difference of the cover image and the stego image should be visually unnoticeable. The embedding itself should draw no extra attention to the stego image so that no hackers would try to extract the hidden message illegally. Second, the message hiding method should be reliable. It is impossible for someone to extract the hidden message if she/he does not have a special extracting method and a proper secret key. Third, the maximum length of the secret message that can be hidden should be as long as possible. Steganography comes from Greek and means covered writing. The ancient Greeks wrote text on wax-covered tablets. To pass a hidden message, a person would scrape off the wax and write the message on the underlying wood. He/she would then once again cover the wood with wax so it appeared unused. Steganography is the art of secret communication. Its purpose is to hide the very presence of communication as opposed to cryptography whose goal is to make communication unintelligible to those who do not posses the right keys. Digital images, videos, sound files, and other computer files that contain perceptually irrelevant or redundant information can be used as covers or carriers to hide secret messages. After embedding a secret message into the cover-image, a so-called stego-image is obtained. It is important that the stego-image does not contain any easily detectable artifacts due to message embedding. Although computer-generated fractal images may seem as good covers because of their complexity and irregularity, they are generated by strict deterministic rules that may be easily violated by message embedding. The stegosystem is generally shown in Fig.1. Fig.1 : Stegosystem Emb: The message to be embedded. It is anything that can be represented as a bit stream (an image or text). Cover: Data/Medium in which emb will be embedded. Stego: Modified version of the cover that contains the embedded message, Emb. Key: Additional data that is needed for embedding & extracting. f E : Steganographic function that has cover, emb & key as parameters. 38

II. PROPOSED METHODOLOGY Reversible Steganography method consists of two sections. One is Embedding and another is Extraction. Phase I project includes the whole section of Embedding. Embedding process includes cover file, histogram modification of the cover file, integer wavelet transform applied on cover file, secret data, reversible data embedding technique and stego image. The overall block diagram of reversible steganography method is shown in Fig.2 Embedding Process The embedding process of reversible steganography method includes several steps such as histogram modification of cover image, integer wavelet transform, inverse S transform and data hiding process. Cover Image In steganography method cover image is nothing but an input image where the secret information is hided. In previous techniques greyscale image is used as cover image whereas in the proposed method digital image(colour image) is used as cover file. Any digital file such as image, video, audio, etc can be used as cover. Histogram Modification Histogram modification is done in all planes, because the secret data is to be embedded in all the planes, while embedding integer wavelet coefficients produce stegoimage pixel values greater than 255 or lesser than 0.So all the pixel values will be ranged from 15 to 240. The histogram modification avoids the overflow and underflow problems that occurs during the transformation of the input image. Histogram is applied to the image in order to find the number of pixels occurring in the image. Integer Wavelet Transform Wavelet Transform The basic idea of the wavelet transform is to represent any arbitrary function as a superposition of a set of such wavelets or basis functions. These basis functions or baby wavelets are obtained from a single prototype wavelet called the mother wavelet, by dilations or contractions (scaling) and translations (shifts). Many new wavelet applications such as image compression, turbulence, human vision, radar, and earthquake prediction are developed in recent years. The 1-D wavelet transform can be extended to a two-dimensional (2-D) wavelet transform using separable wavelet filters. With separable filters the 2-D transform can be computed by applying a 1-D transform to all the rows of the input, and then repeating on all of the columns. LL1 HL1 LH1 HH1 Fig. 3 : Subband Labeling Scheme for one level,2-d WaveletTransform. The original image of a one-level (K=1), 2-D wavelet transform, with corresponding notation is shown in Fig.3. The example is repeated for a threelevel (K =3) wavelet expansion in Fig.4. In all of the discussion K represents the highest level of the decomposition of the wavelet transform. Fig.2 : Reversible Steganography Block Diagram. 39

LL 1 HL 1 HL 2 LH 1 HH 1 LH 2 HH 2 HL 3 inverse transform. Due to the mentioned difference between integer wavelet transform (IWT) and discrete wavelet transform (DWT) the LL subband in the case of IWT appears to be a close copy with smaller scale of the original image while in the case of DWT the resulting LL subband is distorted. The Haar wavelet transform can be written as simple pairwise averages and differences LH 3 HH 3 Fig.4 : Subband labeling Scheme for Three Level, 2-D WaveletTransform The 2-D subband decomposition is just an extension of 1-D subband decomposition. The entire process is carried out by executing 1-D subband decomposition twice, first in one direction (horizontal), then in the orthogonal (vertical) direction. For example, the low-pass subbands (Li) resulting from the horizontal direction is further decomposed in the vertical direction, leading to LLi and LHi subbands. Similarly, the high pass subband (Hi) is further decomposed into HLi and HHi. After one level of transform, the image can be further decomposed by applying the 2-D subband decomposition to the existing LLi subband. This iterative process results in multiple transform levels. In Fig.4 the first level of transform results in LH1, HL1, and HH1, in addition to LL1, which is further decomposed into LH2, HL2, HH2, LL2 at the second level, and the information of LL2 is used for the third level transform. Integer Wavelet Transform Generally wavelet domain allows us to hide data in regions that the human visual system (HVS) is less sensitive to, such as the high resolution detail bands (HL, LH and HH), Hiding data in these regions allow us to increase the robustness while maintaining good visual quality. Integer wavelet transform maps an integer data set into another integer data set. In discrete wavelet transform, the used wavelet filters have floating point coefficients so that when we hide data in their coefficients any truncations of the floating point values of the pixels that should be integers may cause the loss of the hidden information which may lead to the failure of the data hiding system. To avoid problems of floating point precision of the wavelet filters when the input data is integer as in digital images, the output data will no longer be integer which doesn't allow perfect reconstruction of the input image and in this case there will be no loss of information through forward and S 1,n = (S 0,2n + S 0,2n+1 )/2 (1) d 1,n = S 0,2n+1 S 0, 2n (2) Where S i,1, d i,l are the nth low frequency and high frequency wavelet coefficients at the ith level respectively. It is obvious that the output of (\) is not integer, the Haar wavelet transform in (2.1) can be rewritten using lifting in two steps to be executed sequentially. d 1,n = S 0,2n+1 S 0, 2n (3) S 1,n = S 0,2n + d 1,n /2 (4) From (1) and (2) we can calculate the integer wavelet transform according to d 1,n = S 0,2n+1 S 0, 2n (5) S 1,n = S 0,2n + d 1,n /2 (6) Then the inverse transform can be calculated by S 0,2n = S 1,n d 1,n /2 (7) S 0,2n+1 = d 1,n + S 0,2n (8) Reversible Data Embedding Spatial Domain Reversible Steganography This type of reversible steganography directly modifies image pixels in the spatial domain to achieve reversibility. Since this technique is easy to implement, offer a relatively high hiding capacity, and the quality of the cover image can be easily controlled, it has become a popular method for reversible steganography. Reversible data embedding, which is also called lossless data embedding, embeds invisible data (which is called a payload) into a digital image in a reversible fashion. As a basic requirement, the quality degradation on the image after data embedding should be low. An intriguing feature of reversible data embedding is the reversibility, that is, one can remove the embedded data to restore the original image. From the information hiding point of view, reversible data embedding hides 40

some information in a digital image in such a way that an authorized party could decode the hidden information and also restore the image to its original, pristine state. The performance of a reversible dataembedding algorithm can be measured by the following. 1) Payload capacity limit: It is the maximal amount of information that can be embedded. 2) Visual quality: It is the visual quality on the embedded image. The motivation of reversible data embedding is distortion- free data embedding. Though imperceptible, embedding some data will inevitably change the original content. Even a very slight change in pixel values may not be desirable, especially in sensitive imagery, such as military data and medical data. In such a scenario, every bit of information is important.from the application point of view, reversible data embedding can be used as an information carrier. Since the difference between the embedded image and original image is almost imperceptible from human eyes, reversible data embedding could be thought as a covert communication channel. By embedding its message authentication code, reversible data embedding provides a true self authentication scheme, without the use of metadata. This technique presents a high-capacity, high visual quality, reversible data-embedding method for digital images. This method can be applied to digital audio and video as well. We calculate the differences of neighboring pixel values, and select some difference values for the difference expansion (DE). The original content restoration information, a message authentication code, and additional data (which could be any data, such as date/time information, auxiliary data, etc.) will all be embedded into the difference values. Reversible Data Hiding Data hiding is referred to as a process to hide data (representing some information) into cover media. That is, the data hiding process links two sets of data, a set of the embedded data and another set of the cover media data. The relationship between these two sets of data characterizes different applications. For instance, in covert communications, the hidden data may often be irrelevant to the cover media. In authentication, however, the embedded data are closely related to the cover media. In these two types of applications, invisibility of hidden data is an important requirement. In some applications, such as medical diagnosis and law enforcement, it is critical to reverse the marked media back to the original cover media after the hidden data are retrieved for some legal considerations. In other applications, such as remote sensing and highenergy particle physical experimental investigation, it is also desired that the original cover media can be recovered because of the required high-precision nature. The marking techniques satisfying this requirement are referred to as reversible, lossless, distortion-free, or invertible data hiding techniques. Inverse Integer Wavelet Transform It is the inverse process of the integer wavelet transform which on applying to each 8x8 block and combine R, G&B plane to produce stego image. The stego image formation is generally obtained by combining the cover file (image) and the text information. III. RESULT AND DISCUSSION 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, where M and N denote the total number of pixels in the horizontal and the vertical dimensions of the image Xi, j represents the pixels in the original image and Yi, j, represents the pixels of the stego-image. The Peak Signal to Noise Ratio (PSNR) is expressed as The extracted original image using reversible algorithm is shown in Fig.5. Fig. 5 : Extracted original image 41

The PSNR and MSE values obtained in red plane is shown in Table-I. Table-I MSE, PSNR values in Red plane Total No. Red Plane Cover of bits Image Size embedded PSNR(db) MSE Lena 466KB 872 49.9799 0.6532 Baboon 88KB 872 49.8272 0.6766 Desert 827KB 872 50.4548 0.5856 IV. CONCLUSION Data hiding using reversible steganography has three primary objectives firstly that steganography should provide the maximum possible payload, and the second, embedded data must be imperceptible to the observer and the original image should be extracted. This steganography method with integer wavelet transform gives high payload (capacity) in the cover image with very little error. This method s performance can be improved by achieving high PSNR and low MSE. [2] KokSheik Wong, Xiaojun Qi, Kiyoshi Tanaka A DCT-based Mod4 steganographic method, Science Direct-Signal Processing, 87 (2007) 1251 1263. [3] Dr. V. Vijayalakshmi, Dr. G. Zayaraz, and V. Nagaraj, A Modulo Based LSB Steganography Method, IEEE International conference on Control,Automation,Communication and Energy Conservation,(2009),1-4. [4] Rengarajan Amirtharajan and John Bosco Balaguru Rayappan, Tri- Layer Stego for Enhanced Security A Keyless Random Approach - IEEE Xplore, DOI, 10.1109/IMSAA.2009.5439438. [5] Saeed Sarreshtedari and Shahrokh Ghaemmaghami, High Capacity Image Steganography in Wavelet Domain, IEEE CCNC 2010 proceedings,(2010),1-5. [6] Abbas Cheddad, Joan Condell, Kevin Curran, Paul Mc Kevitt, Digital image steganography: Survey and analysis of methods Signal Processing, 90 (2010),727 752. [7] Thanikaiselvan.V, Arulmozhivarman.P, Rengarajan Amirtharajan, John Bosco Balaguru Rayappan, Wave(Let) Decide Choosy Pixel Embeddinng For Stego - ICCCET2011, 18th & 19th March, 2011. IV. REFERENCES [1] Po-Yueh Chen and Hung-Ju Lin, A DWT Based Approach for Image Steganography, International Journal of Applied Science and Engineering 4, (2006), 275-290. 42