Edge Adaptive Image Steganography Based on LSB Matching Revisited
Edge Adaptive Image Steganography Based on LSB Matching Revisited
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DOI：
10.1109/tifs.2010.2041812
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发表时间：
2010-06-01
影响因子：
6.8
通讯作者：
Huang, Jiwu
中科院分区：
文献类型：
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作者：
Luo, Weiqi;Huang, Fangjun;Huang, Jiwu
基于最低有效位(LSB)的隐写算法是空域中流行的一种隐写算法。然而，我们发现在现有的大多数方法中，在封面图像中嵌入位置的选择主要依赖于伪随机数生成器，而没有考虑图像内容本身与秘密消息大小之间的关系。因此，即使在低嵌入率的情况下，图像中的平滑/平坦区域在数据隐藏后也不可避免地会受到污染，这将导致视觉质量差，安全性低，特别是对于那些平滑区域较多的图像。本文对LSB匹配重访图像隐写算法进行了扩展，提出了一种边缘自适应的隐写算法，该算法可以根据隐藏信息的大小和图像中两个相邻像素的差值来选择嵌入区域。对于较低的嵌入率，仅使用更锐利的边缘区域，同时保持其他更平滑的区域不变。当嵌入率增加时，只需调整几个参数，就可以自适应地释放更多的边缘区域用于数据隐藏。对6000幅自然图像的3种特殊和4种通用隐写分析算法的实验结果表明，与典型的基于LSB的隐写方法及其边缘自适应方法(如像素值差分方法)相比，新方案在保持较高视觉质量的同时，显著提高了隐写图像的安全性。
The least-significant-bit (LSB)-based approach is a popular type of steganographic algorithms in the spatial domain. However, we find that in most existing approaches, the choice of embedding positions within a cover image mainly depends on a pseudorandom number generator without considering the relationship between the image content itself and the size of the secret message. Thus the smooth/flat regions in the cover images will inevitably be contaminated after data hiding even at a low embedding rate, and this will lead to poor visual quality and low security based on our analysis and extensive experiments, especially for those images with many smooth regions. In this paper, we expand the LSB matching revisited image steganography and propose an edge adaptive scheme which can select the embedding regions according to the size of secret message and the difference between two consecutive pixels in the cover image. For lower embedding rates, only sharper edge regions are used while keeping the other smoother regions as they are. When the embedding rate increases, more edge regions can be released adaptively for data hiding by adjusting just a few parameters. The experimental results evaluated on 6000 natural images with three specific and four universal steganalytic algorithms show that the new scheme can enhance the security significantly compared with typical LSB-based approaches as well as their edge adaptive ones, such as pixel-value-differencing-based approaches, while preserving higher visual quality of stego images at the same time.