收稿日期: 2016-08-04
修回日期: 2016-08-18
网络出版日期: 2016-09-30
基金资助
广东省应用型科技研发专项基金（No.2015B010130003）；广州市科技项目基金（No.201510010275）；广州市人体数据科学重点实验室项目基金（No.201605030011）资助
Texture-Based Image Steganography Using Adaptive Pixel Pair Matching
Received date: 2016-08-04
Revised date: 2016-08-18
Online published: 2016-09-30
图像纹理区结构随机性较强，因此在纹理丰富区域嵌入隐蔽信息比在平坦区域嵌入的安全性更高。提出一种基于自适应像素对匹配法（adaptive pixel pairmatching，APPM）的纹理区隐写算法，优先在复杂纹理区嵌入密信。定义了图像纹理区域判别准则，由此根据待嵌入秘密信息的长度调整阈值，实现自适应隐写。在嵌入过程中可能出现嵌密块纹理复杂度小于阈值的异常情况，为解决这一问题，算法包含了基于最小失真的像素值局部调整策略。实验结果表明，该算法的嵌入效率高于原始APPM算法，而且KL距离较小，抵抗几种常用隐写分析算法的能力也比其他几种代表性算法LSB和APPM更强。
刘烁炜, 胡永健, 刘琲贝, 化晓茜 . 利用自适应像素对匹配的图像纹理区隐写[J]. 应用科学学报, 2016 , 34(5) : 515 -526 . DOI: 10.3969/j.issn.0255-8297.2016.05.004
Compared with flat regions, texture-rich regions in an image are more randomlike, and therefore are more secure for hidden messages. Based on the adaptive pixel pair matching (APPM) scheme, we propose an improved steganographic algorithm that embeds secret data preferentially in texture regions. A criterion for image texture assessment is defined. According to the length of secret data, We use the the criterion to adaptively select appropriate regions for embedding. A pixel value adjustment strategy based on optimizing a minimum distortion function through local search is proposed to deal with possible abnormal situations in embedding. Experimental results show that the proposed algorithm outperforms the original APPM in terms of embedding efficiency and Kullback-Leibler divergence. It is also more robust against several representative steganalytic attacks than a number of least significant bits (LSB) algorithms and APPM.
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