Contourlet-based Image Adaptive Watermarking

Abstract

In the contourlet transform (CT), the Laplacian pyramid (LP) decomposes an image into a low-frequency (LF) subband and a high-frequency (HF) subband. The LF subband is created by filtering the original image with 2-D low-pass filter. However, the HF subband is created by subtracting the synthesized LF subband from the original image but not by 2-D high-pass filtering the original image. In this paper, we propose a contourlet-based image adaptive watermarking (CIAW) scheme, in which the watermark is embedded into the contourlet coefficients of the largest detail subbands of the image. The transform structure of the LP makes the embedded watermark spread out into all subbands likely in which the LF subbands are included when we reconstruct the watermarked image based on the watermarked contourlet coefficients. Since both the LF subbands and the HF subbands contain watermarking components, our watermarking scheme is expected to be robust against both the LF image processing and the HF image processing attacks. The corresponding watermarking detection algorithm is proposed to decide whether the watermark is present or not by exploiting the unique transform structure of LP. With the new proposed concept of spread watermark, the watermark is detected by computing the correlation between the spread watermark and the watermarked image in all contourlet subbands fully. The proposed CIAW scheme is particularly superior to the conventional watermarking schemes when the watermarked image is attacked by some image processing methods, which destroy the HF subbands, thanks to the watermarking components preserved in the LF subbands. Experimental results show the validity of CIAW in terms of both the watermarking invisibility and the watermarking robustness. In addition, the comparison experiments prove the high-efficiency of CIAW again.