IMPROVED CLUSTER BASED IMAGE PARTITIONING TO SUPPORT REVERSIBLE DATA HIDING

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1 IMPROVED CLUSTER BASED IMAGE PARTITIONING TO SUPPORT REVERSIBLE DATA HIDING M.Divya Sri 1, Dr.A.Jaya Lakshmi 2 1 PG Student, 2 Professor, Department of CSE,DVR&DR HS mic college of engineering and technology, Kanchikacherla ABSTRACT: Data hiding in images is a technique by which the original message can be losslessly recovered after the embedded message is extracted. The previous approaches embed data by reserving room before encryption [RRBE] framework. In this framework iterative partitioning approach is used in the image encryption process which results in more processing complexity. In this paper cluster based image partitioning approach is used in the same framework which reduces the processing complexity. Experiments show that the proposed approach can ensure faster rate at each partition for the same image quality. Key words:-iterative partitioning, Cluster based partitioning, Reversible data hiding. [1] INTRODUCTION Data hiding is a technique for embedding data into images. Reversible data hiding is a technique to embed message into an image with a reversible manner so that the original image can be perfectly recovered after extraction of hidden message. Data hiding in images can be done by using three stage framework or two stage framework. The following authors used three stage framework. Fridrich [1] proposed a methodology for reversible data hiding [RDH]. In this method first, it compresses the original image losslessly then the extra space achieved in the image that is used for embedding the data. Another author tian [2] methodology is based on difference expansion [DE]. In this method the pixels are grouped and data is embedded into the least significant bit [LSB] position of the pixel. In another strategy A data hider performs reversible data hiding [RDH] by histogram shift. By using histogram shift the data can be embedded into zero and peak points of the histogram [3]. Johnson [4] method is the compression of the image by finding the syndromes of low parity check codes. According to zhang [5] method a lossy compression is performed. Image can be perfectly compressed by discarding the rough and fine 65

2 IMPROVED CLUSTER BASED IMAGE PARTITIONING TO SUPPORT REVERSIBLE DATA HIDING parts of the image, in those parts the data can be embedded. Okamoto-uchimaya [6] where the composite signal representation is used which is best suitable for data hiding. Data hiding can also be done by using two stage framework. Few authors used two stage frame work. There are some existing joint data hiding and encryption schemes. It works as follows A part of the image should carry the data and the remaining image is encrypted. For example [7], the intra-prediction mode, motion vector difference and the signs of discrete cosine transform [DCT] coefficients are encrypted, while a watermark is embedded into the amplitude of discrete cosine transform [DCT] coefficients. In [8] the cover data in higher and lower bit-planes of transform domain are respectively encrypted and watermarked. In zhang [9] the encrypted image can be divided into several blocks. By transforming the least significant bit [LSB] of the pixels some space can be achieved. In that space the data is embedded. Hwang [10] used the spatial correlation method which uses the side match technique that leads to error rate. [2] PREVIOUS WORK Zhang [11] proposed reserving room before encryption [RRBE] framework. The total procedure can be divided into three stages as shown in figure 1 First stage:- The first stage can be handled by content owner. In this stage the content owner checks the appropriate positions in the image which is suitable for data embedding and then the image is encrypted by using the encryption key which gives encrypted image. Second stage:- The second stage can be handled by data hider. The content owner handovers the encrypted image to data hider. The data hider embed data into the positions of the image that was given by the content owner with the help of data hiding key 66

3 original image Encryption key Data hiding key Reserving room for embedding data content owner Image encryption Data embedding data hider Additional data Data extraction Data hiding key Encryption key Receiver Image recovery Fig 1. Frame work reserving room before encryption RRBE Third stage:- The retrieval of data from the image and the recovery of the original image is done in third stage. The three stages in this frame work are interlinked. The encryption key and the data hiding key are handedover to the receiver. The hidden data is extracted with the help of data hiding key. The original image will be recovered with the help of encryption key. In this framework while performing the image encryption it internally partitions the image by using iterative partitioning approach. The iterative partitioning approach can be performed as follows 67

4 IMPROVED CLUSTER BASED IMAGE PARTITIONING TO SUPPORT REVERSIBLE DATA HIDING i) First partition the image into two or more parts based on the image size ii) Read the pixel values at each partition from top to bottom iii) The locations in which the data have to be embedded can be visualized in the form of a table for identifying dull and vibrant colors iv) The data hider embedded data in dull area of the image by checking each partition. By doing this it results more processing complexity. For reducing this complexity cluster based partitioning approach is proposed in our present work. [3] PRESENT WORK In our present work we use cluster based image partitioning while performing image encryption in RRBE framework. Image partitioning is the process of partitioning the digital image into multiple segments (set of pixels also known as super pixels). By using cluster based image partitioning it simplifies the representation of image that is more meaningful. There are several clustering approaches. Here K-means clustering algorithm is used. The final output ensures a balanced distribution of pixels to each partition at faster rate K-means clustering algorithm:- Input:- An RGB color image Process:- 1) Convert the image into L*a*b space 2) Use K-means algorithm to initialize the features of the region. 3) Find the standard deviation of the image 4) Iterative optimization 4.1 Estimation Estimate the label configuration f 4.2 Relabeling Remove small regions having less than 100 pixels. Reorder the labels in proper sequence obtaining a new f. 5) Repeat step-4 until there is no change between two successive iterations or the maximum number of iterations are reached Output:-Multiple segmented regions of the image Description:- Step-1:- An RGB color image is taken as input Step-2:- Convert image into L*a*b space where L stands for the name of the pixel and a,b are the positions of the pixels. Step-3:- K-means algorithm is used for initializing the features of one region. It works as follows Calculate the mean value The pixel values that should be less than mean value can be grouped as one cluster The mean value and the pixel values that are greater than mean value can be grouped 68

5 into another cluster. This process is done for each partition in the image. Step-4:- Find the number of pixels in each cluster and label as f Step-5:- The pixels which are in vibrant color means the RGB value is high. Group the pixels in vibrant color into one region and group the pixels which are in dull color into another region. Step-6:- Each time the value of f is changed. Step-7:- If f do not change between two successive iterations stop the process Step-8:-The final output is the multiple segmented regions of the image. Now the data embedding process will be easy for the data hider. The region where the dull color pixels are grouped the data can be embedded into that region. The reason behind why we are embedding data into dull region is in the image recovery process the quality of the image is improved. The identification of locations where the data hider have to embed data can be represented in the form of a table in previous work. In our present work the locations can be viewed in the form of the image so the data hider easily embed the data. In this way the overall time complexity is reduced. [4] COMPARISIONS AND CONCLUSION We have compared the enhanced method with the previous work [11]. The approach used in the previous work results in more time complexity. The enhanced method reduces the time complexity. The time taken to extract the data is compared with the different embedding rates. Graph1 shows the time for retrieving the data under given different embedding rates. From graph 1 it can be observed that more time was taken for extracting the data from the image by using the previous work. Instead by using our enhanced method that time complexity is reduced. Another advantage of our approach is that we can easily identify the locations of the image where the data to be embedded. As the clustered image is the combination of dull and vibrant areas, we can easily embed data in dull areas. Where as in previous approach it uses more number of iterations to identify the locations to embed data that leads to more time consumption. 69

6 IMPROVED CLUSTER BASED IMAGE PARTITIONING TO SUPPORT REVERSIBLE DATA HIDING Graph 1:- comparisons between previous and enhanced methods REFERENCES [1] J. Fridrich and M. Goljan, Lossless data embedding for all image formats, in Proc. SPIE Proc. Photonics West, Electronic Imaging, Securityand Watermarking of Multimedia Contents, San Jose, CA, USA, Jan. 2002, vol. 4675, pp [2] J. Tian, Reversible data embedding using a difference expansion, IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 8, pp ,Aug [3] Z. Ni, Y. Shi, N. Ansari, and S. Wei, Reversible data hiding, IEEE Trans. Circuits Syst. Video Technol., vol. 16, no. 3, pp , Mar [4] M.johnson, P. Ishwar, V.M. Prabhakaran, D. Schonberg, and K. Ramchandran, on compressing encrypted data, IEEE Trans,Signal process, vol.52, no.10,.pp ,oct.2004 [5] X.Zhang, lossy compression and iterative reconstruction for encrypted image, IEEE Trans. Inform.Forenics security, Vol.6,no.1,pp.53-58,Feb.2011 [6] N.Menon and P.W.Wong, A buyer-seller watermarking protocal IEEE trans. Image process., Vol.10,no.4,pp ,apr.2001 [7] S. Lian, Z. Liu, Z. Ren, and H. Wang, Commutative encryption and watermarking in video compression, IEEE Trans. Circuits Syst. Video Technol., vol. 17, no. 6, pp , [8] M. Cancellaro, F. Battisti, M. Carli, G. Boato, F. G. B. Natale, and A. Neri, A commutative digital image watermarking and encryption method in the tree structured haar transform domain, Signal Process.:Image Commun., DOI /j.image , to be published. 70

7 [9] X. Zhang, Reversible data hiding in encrypted images, IEEE SignalProcess. Lett., vol. 18, no. 4, pp , Apr [10] W. Hong, T. Chen, and H.Wu, An improved reversible data hiding in encrypted images using side match, IEEE Signal Process. Lett., vol.19, no. 4, pp , Apr [11] Kede ma, Weiming Zhang, Xianfeng Zhao, Member, IEEE, Nenghai Yu,and Fenghua li Reversible data hiding in encrypted images by reserving room before encryption IEEE trans on information forenics and security, vol.8,no.3 pp ,march