A well defined cost function is crucial to steganography under the scenario of minimizing embedding distortion. In this paper, we present a new cost function for spatial image steganography. The proposed cost function is designed by using a high-pass filter to locate the less predictable parts in an image, and then using two low-pass filters to make the low cost values more clustered. Experiments show that the steganographic method with the proposed cost function makes the embedding changes more concentrated in texture regions, and thus achieves a better performance on resisting the state-of-the-art steganalysis over prior works, including HUGO, WOW, and S-UNIWARD.
In prediction-error expansion (PEE) based reversible data hiding, better exploiting image redundancy usually leads to a superior performance. However, the correlations among prediction-errors are not considered and utilized in current PEE based methods. Specifically, in PEE, the prediction-errors are modified individually in data embedding. In this paper, to better exploit these correlations, instead of utilizing prediction-errors individually, we propose to consider every two adjacent prediction-errors jointly to generate a sequence consisting of prediction-error pairs. Then, based on the sequence and the resulting 2D prediction-error histogram, a more efficient embedding strategy, namely, pairwise PEE, can be designed to achieve an improved performance. The superiority of our method is verified through extensive experiments.
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