针对自适应隐写的通用隐写分析研究

数字图像是隐写领域使用最多的载体之一。在实际应用中，待检测图像经何种隐写算法被嵌入秘密信息往往是未知的，因此可检测未知隐写算法的通用分析方法非常重要。为此，针对数字图像自适应隐写术提出一种新的通用隐写分析方法，在综合考虑不同自适应隐写术对载体图像统计特性影响的基础上提取特征，通过学习得到可对未知隐写术进行准确检测的隐写分析工具。实验表明，利用该方法对未知隐写算法进行检测可达到相当高的准确率。

Universal Steganalysis against Adaptive Steganographic Algorithms

Digital image is a popular carrier in steganography. In reality, the specific steganographic algorithm used to hide secrete data is generally unknown. Therefore, universal steganalytic techniques capable of detecting hidden information with unknown steganographic algorithms are important. This paper proposes a universal steganalysis technique against adaptive steganographic algorithms for images. With feature extraction and training, the influence of various adaptive steganographic algorithms on statistical characteristics of the carrier image are captured so that unknown steganographic algorithms can be accurately detected. Experimental results show that high detection accuracy can be obtained even for previously unknown steganographic algorithms.