作　　者： ; ; ;

机构地区： 深圳大学信息工程学院

出　　处： 《信号处理》 2012年第6期821-826,共6页

摘　　要： 根据超完备字典图像稀疏表示的稀疏性和特征保持性,提出了基于遗传优化图像稀疏分解的密写算法。该密写算法将信息隐藏与基于图像稀疏分解的压缩过程合二为一。首先在基于MP的图像稀疏分解每步迭代中,采用遗传算法快速实现最佳匹配原子的选取;对稀疏分解得到的结果用不同的量化位数进行量化;最后采用LSB嵌入方式将秘密信息隐藏于量化后参数的不同最低有效位中,得到载密图像。实验结果表明,本文提出的基于遗传优化图像稀疏分解的密写算法具有良好的视觉效果,与相同嵌入容量的经典空域和DCT域LSB算法相比,本文的密写算法获得了更高的抵抗隐写分析能力。抗隐写分析实验也表明新的密写算法对嵌入位数不敏感,可灵活地扩充嵌入容量。 Considering the sparsity and integrity of the sparse representation of images over-complete dictionaries,this paper presents a novel image steganographic method with genetic algorithm（GA） based on sparse decomposition.In this method,the data hiding process is integrated into the image sparse compression process.First,in each iteration of the matching pursuit of image sparse decomposition,the best matching atom is selected by GA.Then,the coefficients of sparse decomposition are quantified by different quantization bits.Finally,the stego image is obtained via embedding secret information in the different least significant bits（LSBs） of the quantized coefficients.Experimental results show that the proposed steganographic algorithm maintains good invisibility.Meanwhile,compared to the classical LSB methods of space domain and DCT domain,the new steganography has better ability in resisting steganalysis under the same embedding capacity.Experimental results also indicate that the new steganography is less sensitive to the number of the embedding bits,leading to good expandability in embedding capacity.

关 键 词： 稀疏分解 匹配追踪 遗传优化 隐写

领　　域： [自动化与计算机技术] [自动化与计算机技术]