Content-Adaptive Steganography by Minimizing Statistical Detectability
Content-Adaptive Steganography by Minimizing Statistical Detectability
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DOI：
10.1109/tifs.2015.2486744
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发表时间：
2016-02-01
影响因子：
6.8
通讯作者：
Fridrich, Jessica
中科院分区：
文献类型：
--
作者：
Sedighi, Vahid;Cogranne, Remi;Fridrich, Jessica
当前大多数隐写术方案通过最小化一种启发式定义的失真来嵌入秘密负载。类似地，它们的安全性是使用配备丰富图像模型的分类器凭经验评估的。在本文中，我们寻求一种基于局部估计多元高斯封面图像模型的替代方法，该模型足够简单，可以为内容自适应最低有效位匹配的最强大检测器的功效推导出一个封闭形式的表达式，但同时又足够复杂，能够捕捉自然图像的非平稳特性。我们表明，当封面模型估计器选择得当时，可以获得最先进的性能。在所选模型内可检测性的封闭形式表达式被用于获得关于构建为分类器的经验隐写分析检测器性能极限的新的基本见解。特别是，我们考虑一种新的可检测性受限的发送方，并估计单个图像的安全负载。
Most current steganographic schemes embed the secret payload by minimizing a heuristically defined distortion. Similarly, their security is evaluated empirically using classifiers equipped with rich image models. In this paper, we pursue an alternative approach based on a locally estimated multivariate Gaussian cover image model that is sufficiently simple to derive a closed-form expression for the power of the most powerful detector of content-adaptive least significant bit matching but, at the same time, complex enough to capture the non-stationary character of natural images. We show that when the cover model estimator is properly chosen, the state-of-the-art performance can be obtained. The closed-form expression for detectability within the chosen model is used to obtain new fundamental insight regarding the performance limits of empirical steganalysis detectors built as classifiers. In particular, we consider a novel detectability limited sender and estimate the secure payload of individual images.