AN IMPROVISED LOSSLESS DATA-HIDING MECHANISM FOR IMAGE AUTHENTICATION BASED HISTOGRAM MODIFICATION

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1 AN IMPROVISED LOSSLESS DATA-HIDING MECHANISM FOR IMAGE AUTHENTICATION BASED HISTOGRAM MODIFICATION Shaik Shaheena 1, B. L. Sirisha 2 VR Siddhartha Engineering College, Vijayawada, Krishna, Andhra Pradesh(520007), INDIA sss222shaik@gmail.com, suravarapuls@yahoo.co.in Abstract Digitalizationhasimplemented in all security based applications ranging from home based security to data hiding. Although tremendous progress has been made in past years but still achieving lossless data hiding without any loss of information is concerned area. An optimized lossless data hiding algorithm based on histogram modification technique is proposed in this paper for authentication and the proposed algorithm handles the embedding distortion in effective way. Embedding distortion removal from image is unresolved issue in data hiding and it is handled in efficient manner in this work by taking pixel differences (underflow and overflow) into consideration. Underflow and overflowis prevented by histogram shifting technique and experimental results shows better performance overconventional state-of-art methods. Keywords:Lossless data hiding, image authentication,self-authentication, fragile watermarks 1. INTRODUCTION The techonology gradually changes from analog to digital. Digital technology makes the multimedia data (image, video, and text) storage, replication, and communication become very convenient. Therefore, how to implement effective copyright protection and information security measures in the network environment has become an urgent realistic problem. Data hiding (also called information hiding) plays an important role in multimedia security. The main purpose is to conceal messages in the original medium to protect intellectual property rights, to share secret message, or for content authentication. Nevertheless, the original medium will be permanently altered and cannot be completely reconstructed after the secret message is extracted if the recovering information is not provided. In some applications, such as medical imaging, remote sensing, and military imaging, a slight distortion is not allowed. Therefore, reversible data hiding techniques have become an important research topic in recent years. To increase hiding ability, in this paper an efficient extension of the histogram modification technique by considering the pixel difference instead of simple pixel value is proposed along with the histogram shifting technique to prevent problems that are raised about overflow and underflow. As a result, characteristics of the pixel difference enable the proposed algorithm to obtain the higher peak point to embed a large amount of message. 2. OVER/UNDERFLOW PROBLEM

2 In some cases, the pixel values in a block are very close to the ends of histogram, such as 0 or 255 in the 8-bit case. The modification of the pixel values may lead to over/underflow problem, which means the modified pixel values are beyond the range of [0,255]. Instead of using modulo 256 addition, we propose a new technique to solve this issue. That is, if the pixel values only fall into one side of the histogram, we may shift the pixel value towards the other side to avoid the over/underflow problem. In the worst case, if there are some pixel values with the block, which are close to the both sides, respectively we do nothing to the block, which means we actually embed bit 0 to that block no matter the actual bit to be embedded is 1 or 0. The introduced error bit will be corrected by using ECC. 3. RELATED WORK An innovative approach for hiding data the data in digital images for providing robust copy right protection is presented by authors W. L. Lin and W. H. Tsai (1) in the year The introduction of the boundary stream related to the data embedded is an idea in this approach and this boundary stream related to data are later embedded into mosaic image by detecting the respective boundary regions of preselected mosaic image in the Steganography application. The copyright protection has been consistent concerned area in field of digital image processing and the respective method in this method helps to protect copyright protection by inserting the meaningful mosaic images into the paper copies. But this method fails when the attacks are done beyond limits. The generation of the mosaic images for protecting the copyright by using square shape tiles are introduced by S. C. Hung, T. Y. Liu and W. H. Tsai (2)in the year The main motto of this work is creating mosaic images to embed more data but the square shape data is not always done in all applications. The mosaic image generation in other shapes can increase embedded capacity in watermarking application. In this work the tiles used are not overlap with each others as occurs in conventional and the tiles orientation are always deterministic. The rotation and movement of tiles especially in the edges can pose problems of attacks. The work proposed in this method are tends for invisible watermarking which is used to providing robust copyright protection. This work leaves the future scope for creation of meaningful images in other shapes rather than square to increase the embedding capacity. An high equipped fast processed watermarking scheme based on Reversible contrast mapping has been implanted in the year 2007 by DinuColtuc and Jean-Marc Chassery (4)to resolve two important issues which commonly faced in the past works namely resolving the high complexity issue by resulting low complexity in the results and simultaneously a new to provide robustness against all unnecessary cropping by creating the look up table. 4. PROPOSED METHODOLOGY The image histogram modification is very important concern while adding some data to image but two images having equal histogram then these techniques can t work well. So to overcome this type of problems we are implemented a new algorithm

3 known as an efficient extension of histogram Modification in which we are going to consider the pixel value differences instead of going for one by one pixel values. As we know the gray images having high correlation as well as spatial redundancy, are having the distribution of pixel differences are very high. The value of these differences is expected to zero, so the number of candidates are very high. By using this algorithm we can say that the data is hided in difference between the pixels. So these all the simulation results also show that the proposed algorithm works well. Our proposed algorithm is outlined as below for the lossless data hiding algorithm. A. Embedding Procedure For embedding process the basic requirments Grayscale image,message,histogram modification, Inverse- s-order 2. From pixel difference we get a peak point P Inverse S-order for grayscale image can be shown in below figure Scan order: inverse s-order 3. Apply inverse S-order for total image using peak point P value d < P by shifting x pixel values with 3 units. x, ifi = 0 ord < P y = x + 3, ifd > Pandx x x 3, ifd > Pandx < x y is the marked image value of pixel i. Let us consider a gray scale host image with N- pixels havingx pixel values it denotes grayscale value of i pixel 0 i N 1x [0,255](range of pixel 0 (min) to 255 (max) value). And have to embed M message in gray scale host image by using inverse S-order method. 1. Inverse S-order method can be applied on grayscale host image to embed the M message. Here the pixel value of host image can be inter changed and d difference between pixels x andx can be calculated as given below x d =, ifi = 0, x x, otherwise 4. Whend = P, x can be changed according to the message y = x + M, ifd = Pandx x x M, ifd = Px < x From the above process data hiding processes is completed by using one peak point. For large data process we need to repeat the embedding process. B. Extraction procedure Requirements: Grayscale image, Inverse S-order,,Message,, To Extract the embedding message from the marked image. The reverse process of the embedding

4 processing is done. Inverse S-order is applied to calculate the pixel difference between the pixels x, i.e; varies from 0 to 255.To recover the marked message loselesssly from the host image, we need to consider the grayscale value of the ith pixel in the image as y, 0 i N 1, y [0, 255]. The following are the steps to recover the marked message from an image as shown: 1) Scan the marked image in the same order and position as done is the embedding process 2) Set x = y, it indicates that recovering the marked message (x ) from the embedded image (y ) 3) Extract message M by the following conditions 0, if y x = P, 1, if y M = x = P + 1, 2, if y x = P + 2, 3, if y x = P + 3, wherex denotes the restored value of y. 4) Restore the original value of host pixel x by 5) Go to step 3 continuously until the embedded message is completely extracted. As a result, the exact copy of the original host image will be obtained Host image Marked image x y + ( y x P), ifp < y x P + 3 andy < x, y ( y x P), ifp < y x P + 3 andy > x, = y + 3, if y x > P + 3 andy < x, y 3, if y x > P + 3 andy < x, y, otherwise. Fig.4.1: An example of the proposed lossless data hiding 5. APPLICATIONS Message to be embedded (a) Covert communication (b) Copyright protection of images (authentication) (c) Fingerprinting (traitor-tracing)

5 (d) Adding captions to images, additional information, such as subtitles, to videos (e) Image integrity protection (fraud detection) (f) Copy control in DVD (g) Intelligent browsers, automatic copyright information, viewing movies in given rated version 6. SIMULATION RESULTS amount of information in image is concerned area in digital image processing. In proposed methodology, Laplacian distributed is used in proposed method to hide more payload than traditional methods which help to increase the data hiding capacity at invertible distortion and this process adaptively modify according to compressed image formats in effective way. Two operations namely embedding and extraction are carried out to support different domains to achieve good authentication REFERENCES Fig 5.1: Original and marked images [1] J. Fridrich, M. Goljan, and R. Du, Invertible authentication, Proceedings of the SPIE, Security and Watermarking of Multimedia Contents III, vol. 3971, San Jose, California, Jan. 2001, pp [2] W. L. Lin and W. H. Tsai, Data Hiding in Image Mosaics by Visible Boundary Regions and Its Copyright Protection Application against Print- AndScan Attacks, Proceedings of International Computer Symposium 2004, Taipei, Taiwan, Dec , [3]DinuColtuc and Jean-Marc Chassery Very fast watermarking by reversible contrast mapping.ieee signal process.lett.14 (4): (2007). Fig 5.2: Histogram of the above images 6. CONCLUSION Pixel difference between adjacent pixels is implemented in this paper for histogram modification technique to attain simple pixel value. Hiding large [4] M. Van der Veen, F. Bruekers, A. Van Leest, and S. Cavin, High capacity reversible watermarking for audio, Proceedings of the SPIE, Security and Watermarking of Multimedia Contents V, vol. 5020, Santa Clara CA, Jan. 2003, pp [5] D. Rui and J. Fridrich, Lossless authentication of MPEG-2 video, Proceedings of IEEE International

6 Conference on Image Processing, vol. 2, Rochester, NY, 2002, pp [6] C. C. Chang, W. L. Tai, and C. C. Lin, A reversible data hiding scheme based on side match vector quantization, IEEE Transactions on Circuits and Systems for Video Technology, vol. 16, no. 10, pp , Oct [7] Z. Ni, Y. Q. Shi, N. Ansari, and W. Su, Reversible data hiding, IEEE Transactions on Circuits and Systems for Video Technology, vol. 16, no. 3, pp , Mar