一种基于模糊分类的信息隐藏方法

Steganographic method based on fuzzy classification

提出了一种在图像DCT域内的信息隐藏方法。为了在保证隐秘图像视觉质量可以接受的前提下,有效提高载体图像的信息隐藏量,首先根据HVS特征和模糊理论将载体图像的各个DCT子块分为三类,然后根据DCT子块的类型不同,自适应变化嵌入每个DCT系数的秘密信息的位数。实验结果表明:该文方法在图像质量和信息嵌入量方面,都优于DCT域内固定嵌入位数的信息隐藏方法。此外,在秘密信息提取时,该文算法不需要参考原始载体图像,实现了提取信息的盲检测。

This paper presents a novel steganographic method in Discrete Cosine Transform（DCT） domain.In order to improve the embedding capacity of a cover-image while maintaining the stego-image quality acceptable,the DCT blocks of a cover-image are classified into three classes based on Human Visual System （HVS） characteristics and fuzzy theory,and then the number of bits to be embedded in each quantized DCT coefficient of a DCT block is varied with the type of the block.The experimental results show that the proposed method performs better than the fixed-length Least-Significant-Bits （LSBs） substitution method in DCT domain in terms of message capacity and image quality.Besides,the embedding data can be extracted from the stego-image without referencing to the original cover-image.