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A Feature-Based Robust Digital Image Watermarking Scheme


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  • 1. A Feature-Based Robust Digital Image Watermarking Scheme M. Hemahlathaa C.Chellppan Department of Computer Science, Department of Computer Science, Anna University, Chennai-24 Anna University, Chennai-24Abstract-The effectiveness of a digital also attracted the attentions of hackers andwatermarking algorithm is indicated by the criminals alike who are interested in breakingrobustness of embedded watermarks the watermarks in order to crack the copyrightagainst various attacks. Improving the protection system. As a result, there is arobustness of a watermark so as to constant challenge on the researchers to keepwithstand attacks has been one of the main improving the robustness of the watermarkingresearch objectives in digital image technique.watermarking. The two issues of existing Attacks which attempt to destroy orfeature-based schemes that have to be invalidate watermarks can be classified intoaddressed are: one is avoiding repeated two types, noise-like signal processing andselection of robust regions for geometric distortions. Attacks of the first typewatermarking to resist similar attacks, and intend to remove embedded watermarks fromthe other is the difficulty of selecting the the cover image by a signal processingmost robust and smallest feature region set approach. The second type of attack, whichto be watermarked. In order to achieve results in synchronization errors by geometricrobustness, an overall architecture for a distortions, makes a detector fail to detect thefeature-based robust digital image existence of watermarks even if they are stillwatermarking scheme is designed. A on the image.simulated attacking procedure is performed A watermarked feature region mayusing predefined attacks to evaluate the have different degrees of robustness againstrobustness of every candidate feature different attacks. It would be helpful to findregion selected. Comparing with some well- out the most robust regions if there is priorknown feature-based methods, the proposed knowledge of each region’s attack resistancemethod exhibits better performance in capability. As a result, we propose a featurerobust digital watermarking. region selection method based on the idea of simulated attacking. This method can beKeywords- Generic algorithm, geometric distortions, integrated into the feature-based watermarkingrobust digital watermarking, simulated attacking. schemes to enhance their robustness against I. INTRODUCTION various types of attack. The remainder of this paper is organised as follows. In Section II, we discuss Digital watermarking technology is the basic information about digital imagemainly applied on the following: copyright watermarking system. In Section III, theprotection, operation tracking or piracy overall block diagram of the proposed systemtracking, image authentication and copy is discussed in brief. A critical analysis ofcontrol, in which copyright protection is the watermark attacks is given in Section IV.most important application. As one of the Section V explains the primary feature setmost popular and viable techniques in searching stage. A feature set extension stageprotecting copyrights in digital media, is presented in Section VI. The concludingwatermarking technology has received remarks are drawn in Section VII.enormous level of attention of researchers andpractitioners alike. Unfortunately, due to thesame reason, watermarking technology has
  • 2. Figure 1. General process of Digital Image Watermarking System extended by a genetic algorithm-based search II. DIGITAL IMAGE WATERMARKING procedure to enhance its robustness to SYSTEM undefined attacks. Figure 1 depicts the general process offeature-based digital image watermarking IV. SIMULATED ATTACKINGmethods. The watermark sequence is A few representative attacks areembedded into the feature regions in the applied to the watermarked feature regions inwatermark insertion phase. The embedding the simulated attacking phase. The simulated attacking procedure using pre-defined attacksprocedure can be defined as a superposition of is used to evaluate the robustness of everythe digital watermark signal onto the original candidate feature region. By performing theimage. Feature detectors perform specific pre-defined attacks on the watermarked image,transformations on digital images to extract the region being attacked by the attacks can betheir local features, ranging from a point to an found out easily, because the watermarkobject, and have been adopted in many attacks generally concentrate on specificapplications such as object recognition, regions only. The knowledge of watermark attacks would provide a valuable insight intodatabase retrieval, and motion tracking. the design of a more robust digital imageExtracting the watermark can be divided into watermarking technique.two phases: locating the watermark and The watermark attacks are mainlyrecovering the watermark information. Two classified into four different categories.kinds of extraction are available: using the Removal attacks aim at the complete removaloriginal document and in the absence of the of the watermark information from the watermarked data without cracking theoriginal document. security of the watermarking algorithm. III. SELECTION OF OPTIMAL FEATURE Geometric attacks do not actually remove the REGION SET embedded watermark itself, but intend to distort the watermark detector synchronization An overall architecture for the with the embedded information. Protocolproposed system is shown in Figure 2. The attacks aim at attacking the entire concept ofsimulated attacking procedure along with the the watermarking application. Cryptographicprimary feature set searching stage is attacks aim at cracking the security methods inresponsible to find out a minimal feature watermarking schemes and thus finding a wayregion set under the objective of resisting as to remove the embedded watermarkmany predefined attacks as possible. Here, a information.track-with-pruning algorithm is developed tosearch for the optimal solution. In the featureextension stage, the primary feature set is
  • 3. Figure 2. Block diagram of Proposed Feature region Selector V. CRITICAL ANALYSIS OF produces a conflicting outcome in spatial and WATERMARK ATTACKS frequency domain. In spatial domain, it Gaussian smoothing attack averages produces lower variation pixel values, but inthe value of pixels over an area using frequency domain it seems to have increasedweighting coefficients derived from a the frequency components of the image [4].Gaussian function. An observation on the The amount of blur is computed by averageimage histogram of the Gaussian smoothed edge-spread in the image, or more specifically,images shows that as the width of the it is measured by the average extent of thehistogram decreases and the peak of the slope’s spread of an edge in gradients directionhistogram increases as the level of the and also its opposing direction [5]. Thereforesmoothing increases. This phenomenon is an the edge-spread indicates the amount of blurindication that smoothing reduces the variation exhibited by each edge the image pixel values [1]. Image sharpening can be seen as the Histogram equalization attack works opposite of image smoothing. The process isby reducing the number of unique grey values achieved in the frequency domain by using aan image and reshape the histogram to high pass filter, which intensifies the highapproximate a uniform distribution. In effect, frequency components in the Fourier spectrumhistogram equalization is controlled by [6]. A high pass filter H(w) is obtained from itsadjusting the desired number of unique grey low pass filter L(w) counterpart and calculatedvalues [2]. Salt and pepper noise is another using H(w) = 1 – L(w) formula in theexample of statistical noise with a very frequency domain.different probability density function (PDF). Gaussian noise is statistical noise thatIts PDF takes the form of two impulse has a probability density function of the zerofunctions at two discrete locations [3]. mean normal distribution. The power of the Median Filtering aims at reducing the noise is controlled by varying the width of thepresence of noise in an image, hence normal distribution. The wider the width theenhancing the image quality. Median filter more variation the noise value takes [7]. A
  • 4. direct comparison between the original ‫ ׊‬rk , rj Rp, k  j ĺ rk ŀ rj = ĭwatermarked image and the most severelyattacked watermarked image show a where Rp is the set of selected feature regionssignificant change in the spectrum shape in which any two regions rk and rj are notespecially in the high frequency region [8]. The Set removal attack can remove the overlapped, and the value of for aembedded watermarks with few visual predefined attack is determined byimpacts. In order to increase the robustness,several copies of the same watermark areembedded in the embedding bit-plane. In the = 1, ‫׌‬r Rp, dr, ai  0extracting process, the highest obtained valueas the degree of the similarity between the 0, otherwiseoriginal watermark image (W) and the attackedwatermark image (W’) is considered [11]. The projected process aims at picking Self-Similarity attack erases the the fewest regions from those found by awatermark while granting the best Peak Signal feature detector to accomplish the greatestto Noise Ratio (PSNR) between the robustness. The most important feature regionwatermarked image and the attacked one. set and the pruned feature region set areHere, the correlation between the different initialized as null, and the number of examinedparts of the image is taken into account [12]. feature regions is set as one. An iterativeThe Blind signal separation attack regards the search is carried out for determining thewatermark system as a black box by assuming primary feature set. The candidate featurewatermark signal as different kinds of noise, region sets with the cardinality equal to currentinstead of using the prior knowledge of the values are selected from the power set of thewatermark signal and embedding methods. To set of detected feature regions. Besides, eachestimate the original image data, blind of them is satisfied with two conditions: 1) allestimation and a MIMO linear channel are feature regions are nonoverlapped and 2) allintroduced [13]. elements of its power set are not in the pruned set. A candidate set is allocated as the new VI. PRIMARY FEATURE SET primary feature set if it increases the number SEARCHING STAGE of resisted attacks. On the other hand, a The attack resistance analysis phase is candidate set is contained within the prunedapplied by a two-step procedure. As an initial feature region set while there is no furtherstep, the original feature regions are first tested attack resisted after adding new feature regionsif they can be re-detected in the attacked to the set. The early pruning mechanism andimage. The watermark Wr embedded in each the constraint of nonoverlapping betweenpositively redetected region is then extracted feature regions would reduce computationto inspect the consistency (bit error) amongst time significantly because of diminishingthe original watermark and itself. Using dr, a impossible sets quickly. Finally, the implementation ranges the desired number ofto indicate whether the region r can resist thepredefined attack a or not, it is defined as resisted attacks. VII. FEATURE SET EXTENSION STAGEdr, a = 1, BER(W, Wr) ” T As an extension to the earlier phase, 0, otherwise an optimal feature region set is selected to resist predefined attacks on watermarking. In the final step, the most robust and Moreover to resist some non-predefinedsmallest set of nonoverlapping feature regions attacks, auxiliary regions are carefully chosenis selected according to the result of attack from the residual feature regions. Thisresistance analysis. This work is formulated as augments the robustness of watermarkedfollows: image against indefinite attacks and preserves its visual quality. Since the appearances of = arg max Rp Ňmin |Rp | undefined attacks are of extensive variant and are problematic to model, we therefore implement a multi-criteria optimization
  • 5. approach for the selection of auxiliary feature [8] Xinmin Zhou,Weidong Zhoa and Li Pan, “Security Theory and Attack Analysis for Text Watermarking” IEEE regions. At first, an assumption that the feature International Conference on Signal Processing 2007. regions which last more types of predefined [9] Salahaldeen Altous, Muhammad Kashif Samee, and Jurgen attacks are more probable to resist undefined Gotze, “ Reduced Reference Image Quality Assessment for JPEG Distortion”, International Journal of Computer Science attacks is applied. The symbol is defined & Emerging Technologies,VOL.2, Issue.1, PP.178-187, to point out the complete resistance degree of February 2007. [10] Chun Hsiang Huang and Ja-Ling Wu, ”Attacking Invisible the region r against all predefined attacks, and Watermarking Schemes”, IEEE Transactions on Multimedia, it is determined by April 2006. [11] Mir Shahriar Emami and Ghazali Bin Sulong, “ Set Removal Attack: A New Geometric Watermarking Attack”, = ( d r, a1 + d r, a2 + . . . + d r,a Na ) = International Conference on Future Information Technology, March 2006. [12] Xinpeng Zhang and Shuozhong Wang, “Generalised Watermarking Attack based on Watermark Estimation and where dr, ai {0,1} and specifies if the region Perceptual Remodulation”, IOP Science Journal of Optics r can resist the ith predefined attack ai , and Na 2006 . [13] Jiang Du, Heung-Kyu Lee and Young Ho Su, ”BSS: A new is the overall total of predefined attacks. The Approach for Watermark Attack”, IEEE Eighth International resistance of a region against a predefined Symposium on Multimedia Software engineering, 2005. [14] Svlatolsav Voloshynovskly, Shelby Peretra and Thlerry Pun, attack is viewed as a possible characteristic of “Attacks on Digital Watermarks: Classification, Estimation- the region. The symbol is the summary Based Attacks and Benchmarks”, Journal of Signal depiction of Na characteristics of a region. Processing Elsevier April 2005. [15] Haiyan Zhao, “Algorithm of digital image watermarking technique combined with HVS”, IEEE International VIII. CONCLUSION Conference on Image processing 2005. [16] Fabien A.P.Petitcolas, Ross J. Anderso and Markus G.Kuhn, A novel technique centred on the ”Attacks on Copyright Marking Systems”, IEEE Transactions simulated attacking approach is developed to on Multimedia February 2004. select the most adequate feature regions for [17] K. Subramanian, “Constrained PDF based histogram equalization for image contrast enhancement”, International robust digital image watermarking under the Conference on Image Processing, January 2004. limitation of preserving image quality. In comparison with other feature-based watermarking methods, the robustness against various attacks is significantly improved by the proposed method, and the image quality after watermarking is well-preserved. REFERENCES[1] Wink.A.M and Roerdink.J.B.T.M, “Denoising functional MR images: a comparison of Wavelet denoising and Gaussian smoothing”, IEEE transactions on Medical Imaging Vol.23, No.3, PP.374-387, March 2011.[2] Hojat Yeganeh, Ali Ziaei and Amirhossein Rezaie, “A Novel Approach for Contrast Enhancement Based on Histogram Equalization”, International Conference on Computer and Communication Engineering, January 2011.[3] Yi Wan and Qiqiang Chen, “A Novel Quadratic type variation method for efficient salt-and-pepper noise removal”, ( IJACSA) International Journal of Advanced Computer Science and Applications, Special Issue on Image Processing and Analysis, PP.85-90, 2010.[4] Chin-Chen Chang, Ju-Yuan Hsiao and Chih-Ping Hsieh, “An Adaptive Median Filter for Image Denoising”, IEEE Conference on Image Processing, June 2010.[5] E. Tsomko, H.J. Kim, and E. Izquierdo, “ Linear Gaussian blur evolution for detection of blurry images”, ICGST-GVIP Journal, ISSN 1687-398X, Volume.9, Issue.2, PP.1-9, Feb. 2010.[6] Ning Xu, Yeong-Taeg, and Kim, “An image sharpening algorithm for high magnification image zooming”, IEEE Region 10 Conference TENCON 2004, VOL.1, PP. 459–462, November 2009.[7] Kozick.R.J and Sadler.B.M, “Maximum-likelihood array processing in non-Gaussian noise with Gaussian mixtures”, IEEE transactions on information forensics and security, VOL.6, NO.3, PP.1028-1039, March 2009.