摘要:为了解决目前图像纹理复杂度建模的隐写载体选择指标难以有效适用于JPEG隐写的问题，提出一种基于Haar小波域指标自适应选择载体的JPEG隐写方法，以高阶Haar小波变换模型建立JPEG图像像素关系，计算各方向上的分解图像矩阵的范数均值，用于选择难以被检测的载体.该指标比已有方法的像素间建模能力更强，更能反映JPEG隐写所影响的像素间关系，且能增强JPEG隐写的隐蔽性.实验结果表明，在大多数情况下，与现有载体选择指标相比，该方法与隐写隐蔽性具有更高的相关性.使用该方法优选的载体进行隐写，比优选前的隐蔽性提高约7.7%，比用现有指标优选载体的JPEG隐写方法的隐蔽性平均提高约2.0%.因此，该方法隐写具有更高的隐蔽性.



Abstract:The existing steganographic cover selection indicators based on image texture complexity modeling are not compatible with JPEG steganography. To solve this problem, a JPEG steganography is proposed based on cover selection using Haar wavelet domain indicators. This method establishes the relationship of JPEG image pixels by taking high-ordered Haar wavelet translation as the model, and calculates the average norm of the decomposition image matrix in each direction to select highly undetectable covers. Moreover, the proposed indicator, which performs better than most of the existing models in the inter pixel modeling ability, can enhance the concealment of JPEG steganography in cover selection. Experimental results show that, in most cases, the proposed JPEG steganography using cover selection achieves higher concealment than that without selecting covers by an average value of about 7.7%. This figure has higher concealment than the existing cover selection indicators by an average value of 2.0%. Therefore, the proposed steganography attains better concealment.



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