Abstract
In this paper, the backward-propagation neural network (BPNN) technique and just-noticeable difference (JND) model are incorporated into a block-wise discrete cosine transform (DCT)-based scheme to achieve effective blind image watermarking. To form a block structure in the DCT domain, we partition a host image into non-overlapped blocks of size 8×8 and then apply DCT to each block separately. By referring to certain DCT coefficients over a 3×3 grid of blocks, the BPNN can offer adequate predictions of designated coefficients inside the central block. The watermarking turns out to be a process of adjusting the relationship between the intended coefficients and their BPNN predictions subject to the JND. Experimental results show that the proposed scheme is able to withstand a variety of image processing attacks. Compared with two other schemes that also utilize inter-block correlations, the proposed one apparently exhibits superior robustness and imperceptibility under the same payload capacity.
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