IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 11, Issue 2, Ver. II (Mar-Apr .2016), PP 55-60 www.iosrjournals.org DOI: 10.9790/2834-1102025560 www.iosrjournals.org 55 | Page Robust Reversible Watermarking via clustering and Enhanced pixel Wise Masking P. Pratyusha 1 ,Ch. Hari Kishore 2 , P.Navatej 3 ,S.Miraj Kumar 4 ,Ch.Gayatri 5 (Electronics and communication Engineering ,Lendi Institute Of Engineering and Technology,JNTUK, India) Abstract: Digital watermarking technique have been indicated so far as a possible solution when, in a specific scenario like authentication, copy right protection, fingerprint etc. There is the need to embed an informative message in a digital document in an imperceptible way. Such a goal is achieved by performing Robust reversible watermarking (RRW) methods. However conventional RRW methods are not readily applicable in practice. This is mainly because:1)they fail to offer satisfactory reversibility on large scale image database;2)they have limited robustness in extracting watermarks from the watermarked images destroyed by unintentional attacks; and 3) some of them suffer from extremely poor invisibility for watermarked image. Therefore it is necessary to have a framework that overcome these three problems. To overcome these drawbacks, wavelet-domain statistical quantity histogram shifting and clustering (WSQH-SC). Compared to conventional methods, this method improves robustness and reducing run-time complexity by using histogram shifting and clustering. Additionally WSQH-SC includes the PIPA to effectively handle overflow and underflow of pixels. Furthermore, to increase its practical applicability, WSQH-SC methods designs an enhanced pixel- wise masking to balance robustness and invisibility. We perform more experiments over natural, medical, medical, and synthetic aperture radar images to show the effectiveness of WSQH-SC by comparing with the conventional methods Keywords: Integer wavelet transformation, K-means clustering, making ,robust reversible watermarking I. Introduction Image watermarking is finding more and more support as a possible solution for protecting intellectual property rights. To aim this, many techniques have been proposed in literature in last few years. Among those Robust reversible watermarking has found a huge surge of experimentation in its domain in past decade as a need of recovering the original image after extracting the watermark arises in various applications like the law enforcement, medical and military image system. Multimedia data embedding or digital watermarking refers to the process of inserting the information bits into the host multimedia signal without introducing perceptible artifacts[1]-[2]. A variety of embedding techniques, ranging from high capacity bit modification to low capacity bit modification to transform domain methods, are used in various applications such as authentication, meta-data tagging ,content-protection and secret communication. Most multimedia data embedding techniques modify and hence, distort the host signal in order to insert the additional information p[2]-[3]. Although researchers proposed some RW methods for various media, e.g., images , audios , videos , and 3-D meshes[4] , they assume the transmission channel as lossless. The robust RW (RRW) is thus a challenging task. For Robust RW, the essential objective is to accomplish watermark embedding and extraction in both lossless and lossy environment[5]. As a result, RRW is required not only recover host images and watermarks without distortion for the lossless channel, but also resist unintentional attacks and extract as many watermarks as possible for the noised channel . Recently, RRW methods for digital images have been proposed , which can be divided into two groups: Histogram rotation (HR)-based methods and histogram distribution constrained (HDC) methods[6]. The HR based methods, accomplish robust lossless embedding by slightly rotating the centroid vectors of two random zones in the non overlapping blocks[7]. Due to the close correlation of neighboring pixels, these methods were stated to be robust against JPEG compression. However they are sensitive to "salt-and-pepper" noise, it leads to poor visual quality of watermarked images, and impedes lossless recovery of original (host) images and watermarks. To overcome this problem, the HDC methods have been developed in spatial and wavelet-domains, which divide image blocks into different types and embed the modulated watermarks for each type based on histogram distribution. Unfortunately, these conventional methods suffer from unstable reversibility and robustness. In summary, the above analysis shows that both kinds of methods are not readily applicable in practice[8]. Therefore, a novel pragmatic RRW framework with the following three objectives: 1) Reversibility, 2) Robustness and 3) Invisibility.