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Steganographic System Based on Higher-Order Statistics

机译:基于高阶统计的隐写系统

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摘要

Universal blind steganalysis attempts to detect Steganographic data without knowledge about the applied steganographic system. Farid proposed such a detection algorithm based on higher-order statistics for separating original images from stego images. His method shows an astonishing performance on current steganographic schemes. Starting from the statistical approach in Farid's algorithm, we investigate the well known steganographic tool Jsteg as well as a newer approach proposed by Eggers et al., which relies on histogram-preserving data mapping. Both schemes show weaknesses leading to a certain detectability. Further analysis shows which statistic characteristics make both schemes vulnerable. Based on these results, the histogram preserving approach is enhanced such that it achieves perfect security with respect to Farid's algorithm.
机译:通用盲隐分析试图在不了解所应用隐写系统的情况下检测隐写数据。 Farid提出了一种基于高阶统计量的检测算法,用于将原始图像与隐秘图像分离。他的方法在当前的密写方案上显示了惊人的性能。从Farid算法的统计方法开始,我们研究了众所周知的隐写工具Jsteg以及Eggers等人提出的一种新方法,该方法依赖于直方图保留数据映射。两种方案均显示出导致一定可检测性的弱点。进一步的分析表明哪种统计特性使这两种方案都容易受到攻击。基于这些结果,增强了直方图保存方法,从而相对于Farid算法实现了完美的安全性。

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