Binary Image Steganalysis Based on Distortion Level Co-Occurrence Matrix

• 作者: Cong Lin Junjia Chen Wei Lu Xianjin Liu Yingjie Xue Yue Zhang Yuileong Yeung
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 工学
• 关键词: Binary image STEGANALYSIS informational security EMBEDDING DISTORTION DISTORTION level map CO-OCCURRENCE matrix support vector machine.
• 年，卷（期）: 2018,（5）
• 页码范围: 201-211
• 页数: 11页
• 分类号: TP3

摘要

In recent years,binary image steganography has developed so rapidly that the research of binary 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.