论文标题
通过对解压缩图像进行建模，用于JPEG图像的侧面隐身志
Side-Informed Steganography for JPEG Images by Modeling Decompressed Images
论文作者
论文摘要
侧面隐身一直是该领域最安全的方法之一。但是，大多数用于JPEG图像的现有方法都以启发式方式使用侧面信息，在这里舍入错误。我们第一次表明，舍入误差的有用性来自其与嵌入更改的协方差。不幸的是，连续变量和离散变量之间的这种协方差在分析上无法获得。提出了协方差的估计值，它允许对隐身志进行模拟DCT系数方差的变化。由于今天的坚定分析最好在空间结构域中执行，因此我们得出了一个似然比测试，以保留减压的JPEG图像模型。然后，提出的方法通过最大程度地限制盖子和Stego分布之间的kullback-leibler差异来界定该测试的功能。我们在两个流行的数据集中实验证明，它可以针对深度学习探测器实现最新的性能。此外，通过考虑使用质量因子100压缩的图像的不同像素方差估计器，可以获得更大的改进。
Side-informed steganography has always been among the most secure approaches in the field. However, a majority of existing methods for JPEG images use the side information, here the rounding error, in a heuristic way. For the first time, we show that the usefulness of the rounding error comes from its covariance with the embedding changes. Unfortunately, this covariance between continuous and discrete variables is not analytically available. An estimate of the covariance is proposed, which allows to model steganography as a change in the variance of DCT coefficients. Since steganalysis today is best performed in the spatial domain, we derive a likelihood ratio test to preserve a model of a decompressed JPEG image. The proposed method then bounds the power of this test by minimizing the Kullback-Leibler divergence between the cover and stego distributions. We experimentally demonstrate in two popular datasets that it achieves state-of-the-art performance against deep learning detectors. Moreover, by considering a different pixel variance estimator for images compressed with Quality Factor 100, even greater improvements are obtained.
