Abstract: 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 neighboring pixels without smoothing edges. The extension of bilateral filter: multi-resolution bilateral filter, where bilateral filter is applied on approximation sub bands of an image decomposed and after each level of wavelet reconstruction. The application of bilateral filter on the approximation sub band 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, Robust.