Reversible watermarking using enhanced local prediction

Reversible watermarking has drawn extensive attentions in recent years due to its broad applications of digital forensics and data security. This paper proposes a novel reversible watermarking approach, which has the perfect reversibility of the embedded data and the original image. In order to increase the embedding capacity, the proposed approach utilizes the enhanced local prediction to reduce the prediction error of every pixel value. Different from traditional reversible watermarking algorithms considering all the image pixels equivalently before prediction, an enhanced image is first computed by multiplying the original image with its saliency map. By linearly formulating each pixel value in an original image as a weighted sum of the enhanced pixel values in its local neighborhood, the correlation coefficients as learned weights are then solved as a least squares solution. Based on two state-of-the-art datasets, the experimental results show that the proposed approach achieves large embedding capacity with relatively low visual distortion.