Steganalysis of least significant bit matching using multi-order differences
Steganalysis of least significant bit matching using multi-order differences
复制标题
使用多阶差异的最低有效位匹配的隐写分析
DOI：
10.1002/sec.864
复制
发表时间：
2014-08-01
影响因子：
--
通讯作者：
Wang, Baowei
中科院分区：
文献类型：
--
作者：
Xia, Zhihua;Wang, Xinhui;Wang, Baowei
本文介绍了一种基于学习的切解/检测方法，用于攻击灰度图像中最不重要的位（LSB）匹配的隐形摄影，这是许多复杂的隐志方法的动态型。我们将LSB匹配嵌入的消息建模为与图像的独立噪声，从理论上证明LSB匹配可以平滑多阶差异的直方图。由于相邻像素之间的依赖性，低阶差异的直方图可以通过拉普拉斯分布近似。在直方图的峰值上，LSB匹配引起的平滑度尤为明显。因此，计算图像像素的低阶差异。共发生矩阵用于模拟与较小的绝对值以提取特征的差异。最后，对矢量机分类器进行了培训，以识别原始图像或Stego图像的测试图像。提出的方法通过LSB匹配及其改进的版本Hugo评估。此外，将提出的方法与最先进的割草方法进行了比较。实验结果证明了新检测器的可靠性。版权（C）2013 John Wiley＆Sons，Ltd。
This paper presents a learning-based steganalysis/detection method to attack spatial domain least significant bit LSB matching steganography in grayscale images, which is the antetype of many sophisticated steganographic methods. We model the message embedded by LSB matching as the independent noise to the image, and theoretically prove that LSB matching smoothes the histogram of multi-order differences. Because of the dependency among neighboring pixels, histogram of low order differences can be approximated by Laplace distribution. The smoothness caused by LSB matching is especially apparent at the peak of the histogram. Consequently, the low order differences of image pixels are calculated. The co-occurrence matrix is utilized to model the differences with the small absolute value in order to extract features. Finally, support vector machine classifiers are trained with the features so as to identify a test image either an original or a stego image. The proposed method is evaluated by LSB matching and its improved version "Hugo". In addition, the proposed method is compared with state-of-the-art steganalytic methods. The experimental results demonstrate the reliability of the new detector. Copyright © 2013 John Wiley & Sons, Ltd.