Robust Digital Image Watermarking Based on Gradient Vector Quantization and Denoising using Bilateral filter and its method noise Thresholding

Topics: Wavelet, Gaussian function, Digital photography Pages: 7 (3433 words) Published: October 24, 2014

Robust Digital Image Watermarking Based on Gradient Vector Quantization and Denoising using Bilateral filter and its method noise ThresholdingI. Kullayamma, P. Sathyanarayana, Assistant Professor, Department of ECE, Professor, Department of ECE, SV University, Tirupati, AITS, Tirupati, ikusuma96@gmail.com satyamp1@yahoo.com Abstract- In this modern world Digital watermarking is of prime importance. This has increased the demand for copyright protection. Digital watermarking is a solution to the problem of copyright protection and authentication of multimedia data while working in a networked environment. We propose a robust quantization-based image watermarking scheme, called the gradient direction watermarking (GDWM), and based on the uniform quantization of the direction of gradient vectors. In GDWM, the watermark bits are embedded by quantizing the angles of significant gradient vectors at multiple wavelet scales. The proposed scheme has the following advantages: 1) Increased invisibility of the embedded watermark, 2) Robustness to amplitude scaling attacks, and 3) Increased watermarking capacity. To quantize the gradient direction, the DWT coefficients are modified based on the derived relationship between the changes in coefficients and the change in the gradient direction. This watermarking technique is more robust to various sizes of watermark images. The Gaussian filter is a local and linear filter that smoothens the whole image irrespective of its edges or details, whereas the bilateral filter is also a local but non-linear, considers both gray level similarities and geometric closeness of the neighbouring pixels without smoothing edges. The extension of bilateral filter: multi-resolution bilateral filter, where bilateral filter is applied on approximation subbands of an image decomposed and after each level of wavelet reconstruction. The application of bilateral filter on the approximation subband results in loss of some image details, where as that after each level of wavelet reconstruction flattens the gray levels there by resulting in a cartoon-like appearance. To tackle these issues, it is proposed to use the blend of Bilateral and its method noise thresholding using wavelets. In various noise scenarios, the performance of proposed method is compared with bilateral denoising method and found that, proposed method has inferior performance. Keywords- Bilateral; Bilateral and Detailed Thresholding; Denoising; Digital Watermarking; Gradient Direction Quantization . I.INTRODUCTION

Watermarking approaches can generally be classified into two categories: Spread Spectrum (SS)-based watermarking and quantization-based watermarking. The SS type watermarking, adding a pseudorandom noise-like watermark into the host signal, has been shown to be robust to many types of attacks. Based on the distribution of the coefficients in the watermark domain, different types of optimum and locally optimum decoders have been proposed. Many SS based methods have been developed. In quantization watermarking, a set of features extracted from the host signal are quantised so that each watermark bit is represented by a quantized feature value. Kundur and Hatzinakos proposed a fragile watermarking approach for tamper proofing, where the watermark is embedded by quantizing the DWT coefficients [1].Chen and Wornell [2] introduced quantization index modulation (QIM) as a class of data- hiding codes, which yields larger watermarking capacity than SS based methods. Gonzalez and Balado proposed a quantized projection method that combines QIM and SS methods [3]. Chen and Lin [4] embedded the watermark by modulating the mean of a set of wavelet coefficients. Wan and Lin embedded the watermark by...


References: [1] D. Kundur and D. Hatzinakos, “Digital watermarking for telltaletamper proofing and authentication,” Proc. IEEE, vol. 87, no. 7, pp.1167–1180, Jul. 1999.
[2] B. Chen and G. W. Wornell, “Quantization index modulation: A classof provably good methods for digital watermarking and informationembedding,” IEEE Trans. Inf. Theory, vol. 47, no. 4, pp. 1423–1443,May 2001.
[3]. Perez-Gonzlez and F. Balado, “Quantized projection data hiding,”in Proc. Int. Conf. Image Process., 2002, vol. 2, pp. 889–892.
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[6] P. Bao and X. Ma, “Image adaptive watermarking using wavelet domainsingular value decomposition,” IEEE Trans. Circuits Syst. VideoTechnol., vol. 15, no. 1, pp. 96–102, Jan. 2005.
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