Fast and High Capacity Digital Image Watermarking Technique Based on Phase of Zernike Moments

Zernike Moments (ZMs) are used in many image processing applications, due to their resistance against various signal processing and geometric attacks. Digital image watermarking is one of those application areas, where ZMs are widely used to insert and extract the watermark bits for digital media authentication. In all the existing ZM based watermarking techniques, magnitude of moments is used to insert and extract the watermark. In this paper, the authors’ have proposed a semi blind watermarking technique in which phase of ZMs is used for watermark insertion and extraction. Due to the use of phase of ZMs, 100% detection ratio is achieved against any geometric and other signal processing attacks. To make the proposed technique fast, q-recursive method is used to compute the Zernike polynomials. The use of q-recursive method has also increased the transparency of watermark due to its better reconstruction ability as compared to traditional moment computation method. Through detailed experimentation, it has been confirmed that the proposed watermarking technique is fast, has more imperceptibility, less Bit Error Rate (BER) and more capacity as compared to traditional ZMs magnitude based watermarking technique. DOI: 10.4018/ijcvip.2012010104 International Journal of Computer Vision and Image Processing, 2(1), 60-74, January-March 2012 61 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. The visibility of watermark in the watermarked image can be controlled. Yongqing et al. (2011) have analyzed the invariance property of ZMs and proposed magnitude based watermarking technique. In this watermarking technique, magnitude of selected moments is quantized to insert the watermark bits. The authors have computed ZMs up to high order to support high payload to be embedded as watermark. The watermarking technique proposed by them is robust against rotation, scaling, flipping, additive noise and lossy compression. They have concluded that as the number of embedded watermark bits increases, the Peak Signal to Noise Ratio (PSNR) decreases and hence quality of watermarked image degrades. In the proposed watermarking technique, large number of bits can be inserted as watermark without degrading the quality of watermarked image. Kim et al. (2003) have also used magnitude of ZMs as invariant feature vector of the host image. This feature vector is modified to insert the watermark bits and using the modified feature vector watermarked image is reconstructed. At the detector end, the feature vector of transformed watermarked image and original feature vector is compared to authenticate the watermarked image. The drawback of this technique is that the computation of ZMs (up to moment order 5) take five minutes for 256 ×256 image. To reduce the computation time, we propose the use of q-recursive method for computing Zernike polynomials. Palak et al. (2004) have proposed a watermarking technique based on ZMs and odd even quantization. In this technique, the watermark bits are inserted by quantizing the magnitude of the ZMs using odd-even quantization method. Through experiments, the authors have concluded that this technique has detection ratio of 97% for rotation and of 75% for additive noise. In all of the existing digital image watermarking techniques (Bas et al., 2002; Xin et al., 2004, 2007; Jin et al., 2009; Jie et al., 2008; Nitin et al., 2007; Viet et al., 2007), magnitude of ZMs is used for watermark insertion and retrieval. Shan et al. (2009) have analyzed the importance of phase information of ZMs and used the combination of both magnitude and phase feature to design an Invariant Zernike Moment Descriptor (IZMD) for image retrieval. Due to sensitivity of phase information against image rotation, authors have performed phase correction at the time of feature extraction from the rotated image. After correcting the phase, feature descriptor is found to be more robust against Rotation, Scaling and Translation (RST) attacks and other signal processing attacks. In the proposed digital image watermarking technique, before the extraction of watermark bits, phase of the transformed watermarked image is corrected using the method proposed by Shan et al. (2009) and Singh et al. (2011). To correct the phase of transformed watermarked image, moments of original watermarked image and transformed watermarked image are required. Further, in order to reduce the time required to compute ZMs, q-recursive method proposed by Chong et al. (2003) is used in our proposed watermarking technique. Also using the q-recursive method, moments remain stable up to high order as compared to the traditional ZMs computation method as proved by Singh et al. (2011). Due to the stability of moments high order, more moments can be computed and selected for watermark insertion by using q-recursive moment computation method. Thus, with the help of q-recursive method, capacity of the proposed watermarking technique is increased and computation time is decreased as compared to the existing watermarking techniques. Also due to the extraction of moments from corrected moments, BER is reduced to zero in our proposed watermarking technique. The benchmark for a high-quality watermarking technique is to have high capacity, low BER, more invisibility of watermark and robustness against all geometric attacks and signal processing attacks. All these parameters are achieved in our proposed watermarking technique using combination of q-recursive moment computation technique and use of phase component of the ZMs for watermark insertion and extraction. The q-recursive method to compute Zernike polynomials is specified 13 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/article/fast-high-capacity-digitalimage/68004?camid=4v1 This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Computer Science, Security, and Information Technology. Recommend this product to your librarian: www.igi-global.com/e-resources/libraryrecommendation/?id=2

[1]  Miroslaw Pawlak,et al.  Circularly orthogonal moments for geometrically robust image watermarking , 2007, Pattern Recognit..

[2]  Upendra Kumar,et al.  Segmentation of Ill-Defined Objects by Convoluting Context Window of Each Pixel with a Non-Parametric Function , 2013, Int. J. Comput. Vis. Image Process..

[3]  Samy S. A. Ghoniemy Performance Analysis of Mobile Ad-Hoc Network Protocols Against Black Hole Attacks , 2013, Int. J. Comput. Vis. Image Process..

[4]  K. P. Subbalakshmi,et al.  Rotation and cropping resilient data hiding with Zernike moments , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[5]  Kiyoharu Aizawa,et al.  Geometrically Invariant Object-Based Watermarking using SIFT Feature , 2006, 2007 IEEE International Conference on Image Processing.

[6]  Xiangjin Zeng,et al.  A Novel Subpixel Edge Detection Based on the Zernike Moment , 2010 .

[7]  Chandan Singh,et al.  Algorithms for fast computation of Zernike moments and their numerical stability , 2011, Image Vis. Comput..

[8]  Yvon Voisin,et al.  An Evaluation Framework and a Benchmark for Multi/Hyperspectral Image Compression , 2011, Int. J. Comput. Vis. Image Process..

[9]  Yongqing Xin,et al.  Geometrically robust image watermarking via pseudo-Zernike moments , 2004, Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No.04CH37513).

[10]  Rajeev Srivastava Research Developments in Computer Vision and Image Processing: Methodologies and Applications , 2013 .

[11]  Ying Weng,et al.  A New Robust Watermarking Scheme for Color Image in Spatial Domain , 2007, 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System.

[12]  Chee-Way Chong,et al.  A comparative analysis of algorithms for fast computation of Zernike moments , 2003, Pattern Recognit..

[13]  Junichi Suzuki,et al.  Developing and Applying Biologically-Inspired Vision Systems: Interdisciplinary Concepts , 2012 .

[14]  Heung-Kyu Lee,et al.  Invariant image watermark using Zernike moments , 2003, IEEE Trans. Circuits Syst. Video Technol..

[15]  Shervan Fekri Ershad Defect Detection Approach Based on Combination of Histogram Segmentation and Probabilistic Estimation Technique , 2011, Int. J. Comput. Vis. Image Process..

[16]  M. Pawlak,et al.  A multibit geometrically robust image watermark based on Zernike moments , 2004, ICPR 2004.

[17]  Phyo Thu Thu Khine,et al.  Character Segmentation Scheme for OCR System: For Myanmar Printed Documents , 2011, Int. J. Comput. Vis. Image Process..

[18]  Xiao Bai,et al.  Graph-Based Methods in Computer Vision: Developments and Applications , 2012 .

[19]  Benoit M. Macq,et al.  Geometrically invariant watermarking using feature points , 2002, IEEE Trans. Image Process..

[20]  Ismail A. Ismail,et al.  Invariant Image Watermarking Using Accurate Zernike Moments , 2010 .

[21]  Mahdi Yaghoobi,et al.  A Robust Digital Image Watermarking Approach against JPEG Compression Attack Based on Hybrid Fractal-Wavelet , 2011 .

[22]  Raveendran Paramesran,et al.  On the computational aspects of Zernike moments , 2007, Image Vis. Comput..

[23]  Serge Miguet,et al.  Probabilistic Modeling for Detection and Gender Classification , 2014, Int. J. Comput. Vis. Image Process..

[24]  Abd El Rahman Shabayek,et al.  Visual Behaviour Based Bio-Inspired Polarization Techniques in Computer Vision and Robotics , 2012 .

[25]  Shan Li,et al.  Complex Zernike Moments Features for Shape-Based Image Retrieval , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[26]  Wei He,et al.  RST Invarian Watermarking Scheme Based on SIFT Feature and Pseudo-Zernike Moment , 2009, 2009 Second International Symposium on Computational Intelligence and Design.

[27]  Jingyu Hua,et al.  Image Denoising via 2-D FIR Filtering Approach , 2013 .

[28]  Kuo-Ming Hung,et al.  A robust watermarking technique for copyright protection of digital images , 2007 .

[29]  Farhad Soleimanian Gharehchopogh,et al.  A Novel Approach for Edge Detection in Images Based on Cellular Learning Automata , 2012, Int. J. Comput. Vis. Image Process..

[30]  V. Madasu,et al.  A feature based face recognition technique using Zernike moments , 2007 .

[31]  Sang Uk Lee,et al.  Robust Image Watermarking Based on Local Zernike Moments , 2007, 2007 IEEE 9th Workshop on Multimedia Signal Processing.

[32]  Ekta Walia,et al.  Rotation invariant complex Zernike moments features and their applications to human face and character recognition , 2011 .