Digital Image Watermarking Based On Gradient Direction Quantization and Denoising Using Guided Image Filtering

ABTRACT Digital watermarking is the art of hiding of information or data in documents, where the embedded information or data can be extracted to resist copyright violation or to verify the uniqueness of a document which leads to security. Protecting the digital content has become a major issue for content owners and service providers. Watermarking using gradient direction quantization is based on the uniform quantization of the direction of gradient vectors, which is called gradient direction watermarking (GDWM). In GDWM, the watermark bits are embedded by quantizing the angles of significant gradient vectors at multiple wavelet scales. The proposed scheme has the advantages of increased invisibility and robustness to amplitude scaling effects. The DWT coefficients are modified to quantize the gradient direction based on the on the derived relationship between the changes in the coefficients and the change in the gradient direction. In this paper, we propose a novel explicit image filter called guided filter. It is derived from a local linear model that computes the filtering output using the content of guidance image, which can be the input image itself or any other different image. The guided filter naturally has a fast and non approximate linear time algorithm, regardless of the kernel size and the intensity range. Finally, we show simulation results of denoising method using guided image filtering over bilateral filtering.