结合通道共生和选择集成的彩色图像隐写分析

Steganalysis for color images based on channel co-occurrence and selective ensemble

目的为了有效对抗针对彩色图像的隐写方案,提出一种新的基于RGB格式的彩色图像隐写分析方法。方法该方法中的特征包括通道内特征和通道间特征,首先从通道内差分平面上提取共生矩阵特征构成通道内特征集合,通道内特征可以有效捕捉到每一个颜色通道内差分系数之间的相关性;然后对通道与通道相互之间的二次差分平面上提取共生矩阵特征构成通道间特征集合,通道间特征可以捕捉到两两通道之间的相关性。在分类阶段利用遗传算法对多个子分类器进行权值优化,选择权值最优的若干个子分类器,通过众数投票进行集成判决,最终获得最佳的检测性能。结果针对误检率,提出的通道共生特征比SPAM特征要降低4%5%,而选择性集成分类器要比完全集成分类器要降低1%2%。结论该方法具有较低的时间复杂度,适合小嵌入率的RGB格式彩色图像,在整体性能上优于已有的隐写分析方法。

Objective This paper presents a novel blind steganalytic scheme of color images on the basis of RGB space to effectively prevent steganography. Method The proposed scheme includes intra-channel and inter-channel features. Intra- channel features are formed by features of matrices from the difference planes; these features effectively cap- ture the dependency among coefficients in any color channel. Inter-channel features are extracted in second-order difference planes between channels; these features can effectively capture the dependency between channels. During classification, the costs of each sub-classifier are optimized by the genetic algorithm. Several sub-classifiers with optimal costs are select- ed, and the optimal decisions are synthesized through majority voting. Result Experimental results show that the prediction error rate of the proposed features is 4% - 5% lower than that of SPAM features, whereas the prediction error rate of the selective ensemble classifier is 1% - 2% lower than that of the ensemble classifier. Conclusion The proposed scheme has minimal time complexity and is applicable to low-embedding color RGB images. Furthermore, the performance of the pro- posed scheme outperforms state-of-the-art steganalytic schemes.