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• W2912367388 abstract "In recent years, binary image steganography has developed so rapidly that the research of image steganalysis becomes more important for information security. In most state-of-the-art binary image steganographic schemes, they always find out the flippable pixels to minimize the embedding distortions. For this reason, the stego images generated by the previous schemes maintain visual quality and it is hard for steganalyzer to capture the embedding trace in spacial domain. However, the distortion maps can be calculated for cover and stego images and the difference between them is significant. In this paper, a novel binary image steganalytic scheme is proposed, which is based on distortion level co-occurrence matrix. The proposed scheme first generates the corresponding distortion maps for cover and stego images. Then the co-occurrence matrix is constructed on the distortion level maps to represent the features of cover and stego images. Finally, support vector machine, based on the gaussian kernel, is used to classify the features. Compared with the prior steganalytic methods, experimental results demonstrate that the proposed scheme can effectively detect stego images." @default.
• W2912367388 created "2019-02-21" @default.
• W2912367388 creator A5079323553 @default.
• W2912367388 creator A5083054357 @default.
• W2912367388 date "2018-05-30" @default.
• W2912367388 modified "2023-09-25" @default.
• W2912367388 title "Binary Image Steganalysis Based on Distortion Level Co-occurrence Matrix" @default.
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• W2912367388 doi "https://doi.org/10.3970/cmc.2018.01781" @default.
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