A novel attention-guided JND Model for improving robust image watermarking

Abstract

Just noticeable distortion (JND) and visual attention (VA), which are two widely used mathematical models of human visual system (HVS) that aim to simulate the human brain mechanism, are sufficiently explored and applied to many researches including digital watermarking. The activity of human brain, however, is extremely complex and it can be more limited due to complicated fusion of spatial saliency for image domain. In this paper, we propose a novel VA guided JND model in which we fuse the final attention map from the low-level features by using two laws of Gestalt principle. Firstly, we demonstrate a classic JND model in DCT domain, which consists of spatial contrast sensitivity function (CSF), luminance adaptation (LA) and contrast masking (CM). The foveation effect and orientation feature are considered to obtain the CSF and CM factor. The foveation effect is affected by spatial attention, and the orientation features are modeled for CM effect together with traditional block texture strength through three direction-based AC coefficients in DCT domain. The attention features are integrated with a novel Gestalt principle-based weighting mechanism for the final block-based VA model, which is then used to modulate JND profiles with two non-linear functions. Finally, the proposed VA-guided JND model is incorporated into a logarithmic spread transform dither modulation (L-STDM) watermarking scheme. Experimental results show that the newly proposed algorithm can achieve good performance in term of robustness and get better visual quality.