Cost polarization by dequantizing for JPEG steganography
Cost polarization by dequantizing for JPEG steganography
复制标题
DOI：
10.2352/ei.2023.35.4.mwsf-374
复制
发表时间：
2023-01
期刊：
影响因子：
--
通讯作者：
Edgar Kaziakhmedov;Yassine Yousfi;Eli Dworetzky;J. Fridrich
中科院分区：
文献类型：
--
作者：
Edgar Kaziakhmedov;Yassine Yousfi;Eli Dworetzky;J. Fridrich
在这篇文章中，我们研究了一个最近提出的方法，以提高经验安全的JPEG图像中的隐写，发送者开始与一个添加剂嵌入方案的对称成本± 1变化，然后减少这些变化的成本的基础上，通过应用去块（JPEG dequantization）算法的覆盖JPEG获得的图像。这种方法在安全性方面提供了相当大的收益，对于各种质量因素和各种嵌入方案，嵌入复杂性开销可以忽略不计。保留这个想法的发明人的原始解释，其基于将去量化图像解释为预覆盖（未压缩）图像的估计，我们提供替代参数。关键的观察结果和这种方法有效的主要原因是各个DCT系数的极化如何一起工作。通过使用未压缩的封面图像的内容复杂性的MiPOD模型，我们表明，成本极化技术降低了嵌入变化的“坏”组合的机会，可能会引入由原始计划与对称成本。通过计算隐写图像w.r. t的似然性来量化该陈述艾德。DCT域的多元高斯预覆盖分布。此外，它示出的成本极化降低块之间的空间不连续性（块效应）在隐写图像和强制执行所需的相关性的嵌入变化跨块。为了进一步证明这一点，在遵循precover模型的源中，简单的维纳滤波器可以作为基于深度学习的去块器。
In this article, we study a recently proposed method for improving empirical security of steganography in JPEG images in which the sender starts with an additive embedding scheme with symmetrical costs of ± 1 changes and then decreases the cost of one of these changes based on an image obtained by applying a deblocking (JPEG dequantiza-tion) algorithm to the cover JPEG. This approach provides rather significant gains in security at negligible embedding complexity overhead for a wide range of quality factors and across various embedding schemes. Challenging the original explanation of the inventors of this idea, which is based on interpreting the dequantized image as an estimate of the precover (uncompressed) image, we provide alternative arguments. The key observation and the main reason why this approach works is how the polarizations of individual DCT coefficients work together. By using a MiPOD model of content complexity of the uncompressed cover image, we show that the cost polarization technique decreases the chances of “bad” combinations of embedding changes that would likely be introduced by the original scheme with symmetric costs. This statement is quantified by computing the likelihood of the stego image w.r.t. the multivariate Gaussian precover distribution in DCT domain. Furthermore, it is shown that the cost polarization decreases spatial discontinuities between blocks (blockiness) in the stego image and enforces desirable correlations of embedding changes across blocks. To further prove the point, it is shown that in a source that adheres to the precover model, a simple Wiener filter can serve equally well as a deep-learning based deblocker.