降低特征类内离散度的JPEG图像隐写分析

Steganalysis of JPEG Images Based on Reducing Between-Class Scatter

图像内容特征差异使得载体、载密图像的隐写检测特征混淆在一起而难以区分,这导致图像隐写分析成了一个"类内分散、类间聚合"的分类问题.针对此问题,从降低因图像内容、处理手段等造成的隐写检测特征类内离散度的角度出发,提出了一种更加可靠的隐写检测模型.依据内容复杂度将待检测图像分类,分别提取具有相同内容复杂度的每一类图像的隐写检测特征和训练分类器,得到最终检测结果.数据分析和实验结果表明:基于图像分类的隐写分析方法能够有效提高检测性能.

Compared with the process of embedding,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.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 classified according to the texture complexity.Steganalysis features are separately extracted from each subset with the same or close complexity evaluation function to build a classifier.The theoretical analysis and experimental results can demonstrate the validity of the proposed framework.