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Steganalysis Based on Reducing the Differences of Image Statistical Characteristics

机译：基于减少图像统计特性差异的隐写分析

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摘要

Compared with the process of embedding, the image contents make a more significant impact on the differences of image statistical characteristics. This makes the image steganalysis to be a classification problem with bigger within-class scatter distances and smaller between-class scatter distances. As a result, the steganalysis features will be inseparate caused by the differences of image statistical characteristics. In this paper, a new steganalysis framework which can reduce the differences of image statistical characteristics caused by various content and processing methods is proposed. The given images are segmented to several sub-images according to the texture complexity. Steganalysis features are separately extracted from each subset with the same or close texture complexity to build a classifier. The final steganalysis result is figured out through a weighted fusing process. The theoretical analysis and experimental results can demonstrate the validity of the framework.
机译：与嵌入过程相比，图像内容对图像统计特性的差异影响更大。这使得图像隐写分析成为具有较大的类内散布距离和较小的类间散布距离的分类问题。结果，隐写分析特征将由于图像统计特性的差异而分离。本文提出了一种新的隐写分析框架，可以减少各种内容和处理方法所引起的图像统计特性差异。根据纹理复杂度，将给定的图像分割为几个子图像。从具有相同或接近的纹理复杂度的每个子集中分别提取隐写特征，以构建分类器。通过加权融合过程可以得出最终的隐写分析结果。理论分析和实验结果可以证明该框架的有效性。

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