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    基于区域随机性度量的LSB匹配隐写分析

    Steganalysis of LSB Matching Based on the Measurement of the Region Randomness

    • 摘要: 依据图像信源区域平稳性质,分析LSB匹配隐写对图像区域统计特性的影响,提出一种基于区域随机性特征的隐写分析方法.运用分块处理划分图像区域,对各区域像素进行Hilbert扫描并提取像素最低有效位比特序列,进而将比特序列作异或运算所得到的参量定义为区域随机性度量指标,最后统计并分析区域随机性指标直方图,提取直方图信息熵、特殊取值及原点矩3类特征,结合Fisher线性分类器对载体、载密图像进行判别.实验结果表明,该方法在不同图像库和不同嵌入率条件下对LSB匹配隐写均表现出良好的检测性能,与现有典型检测算法相比其检测性能具有明显提高.

       

      Abstract: Currently, image steganalysis has evolved into a significant subject of interest in the field of information security. In accordance with the region stationary characteristic of the image source, a new steganalytic method that can exploit the image region randomness features is proposed based on analyzing the changes of the regional randomness caused by the least significant bit (LSB) matching steganography. First of all, a given image is separated into a great many of block regions, and the regional LSB sequences are extracted from the pixel sequences of each block region which are obtained by the Hilbert scanning. Furthermore, the parameter which is derived from a LSB sequence with the bit XOR operation, is defined as the region randomness measurement index. Finally, the histogram of the index is calculated and analyzed; three sorts of histogram features including the information entropy, the special histogram values, and the moments are extracted; and the Fisher linear discriminator is applied for classifying the cover images and stego images. Extensive experimental results on three grayscale image databases with different embedding rates show that the proposed approach has good performance in detecting LSB matching steganographic scheme, and it obviously outperforms the previous typical steganalytic algorithms.

       

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