Recently, some steganographic schemes, which take into account the interaction among adjacent modifications, have emerged to tackle the issue of nonadditive embedding and exhibited higher security when compared with the original additive schemes. However, the performances of these schemes are volatile and very sensitive to both the heuristic parameters and the choice of initial additive distortion functions. We proposed a model-based steganographic scheme, which incorporates the adjacent embedding information without relying on any heuristic parameters. Following the spirit of prior arts, the proposed scheme divides the cover image into several interleaved sublattices and embeds messages on each sublattice sequentially. Adjacent modifications are utilized as a priori knowledge to optimize the asymmetric change probabilities of modifying each pixel by +1 and −1. Experimental results show that the proposed scheme can rival or outperform the prior arts and, moreover, maintains a stable security performance, irrespective of any initial additive distortion or any steganalysis detector.
This paper presents a unified way to estimate the parameters of affine transformation in the absence of original image. With 2-D cyclostationary characterization, we analytically show that the covariance of affine transformed image is periodic with the affine transformation matrix. Based on the relationship between the affine transformation matrix and the position of resampling-caused striking peaks in the 2-D spectrum of the image’s edge map, we further study how to estimate the parameters of several typical affine transformations, e.g., the scaling factor, the rotation angle, the joint scaling and rotation transformation parameters. Examples of the output of our algorithm are shown and comparative results are presented to evaluate the performance of the proposed algorithm.
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