基于预分类和特征扩展的 JPEG 图像隐写分析

摘要

The performance of joint photographic experts group (JPEG)image steganalysis is related not only to the information-hiding methods but also to the complexity of image content.A novel approach based on pre-classification and feature extension is proposed to improve the accuracy of JPEG universal steganalysis. Firstly,an image content complexity evaluation criterion is exploited to divide the training samples into several overlapping categories.After that,the more effective extended feature set is extracted from each category re-spectively to build a classifier.Given a testing image,its category is determined according to the minimum dis-tance from its complexity to the average value of each category.Then,the feature set is extracted and sent to the corresponding classifier.Experimental results indicate that this new approach exhibits excellent performance for the detection of typical JPEG steganography algorithms.%联合图像专家组（joint photographic experts group，JPEG）图像的隐写分析性能不仅与信息嵌入方式有关，同时与其自身的内容复杂度也紧密相关。本文提出一种基于预分类和特征扩展的新方法以提高 JPEG图像通用隐写分析准确性。首先采用图像内容复杂度度量准则将训练样本划分为若干互相重叠的类别，并对每一类子库分别提取更加有效的扩展特征集以及构造各自的分类器模型；对于给定的待测图像，根据其内容复杂度与各类复杂度均值的最小距离进行预分类，提取特征并进行相应类别的分类器测试。实验结果表明，该方法对典型的 JPEG 图像隐写算法具有更好的检测性能。