Recently, the field of steganography has experienced rapid developments based on deep learning (DL). DL based steganography distributes secret information over all the available bits of the cover image, thereby posing difficulties in using conventional steganalysis methods to detect, extract or remove hidden secret images. However, our proposed framework is the first to effectively disable covert communications and transactions that use DL based steganography. We propose a DL based steganalysis technique that effectively removes secret images by restoring the distribution of the original images. We formulate a problem and address it by exploiting sophisticated pixel distributions and an edge distribution of images by using a deep neural network. Based on the given information, we remove the hidden secret information at the pixel level. We evaluate our technique by comparing it with conventional steganalysis methods using three public benchmarks. As the decoding method of DL based steganography is approximate (lossy) and is different from the decoding method of conventional steganography, we also introduce a new quantitative metric called the destruction rate (DT). The experimental results demonstrate performance improvements of 10-20% in both the decoded rate and the DT.
翻译:最近,在深层学习(DL)的基础上,成色学领域经历了迅速的发展。基于DL的成像法在覆盖图像的所有部分散发秘密信息,从而在使用常规的隐蔽图像分析方法探测、提取或移除隐蔽的隐蔽图像方面造成困难。然而,我们提议的框架是第一个有效禁用隐蔽通信和使用基于DL的成像法的交易的领域。我们提出了一个基于DL的成像分析技术,通过恢复原始图像的分布,有效地清除秘密图像。我们形成了一个问题,并通过利用深层神经网络利用复杂的像素分布和图像的边缘分布来解决问题。根据提供的信息,我们删除了在像素一级隐藏的秘密信息。我们用三个公共基准将我们的技术与传统的隐蔽分析方法进行比较。由于基于DL的成像法解码法的解码方法非常接近(损失),而且不同于传统的成像法的解码方法,我们还采用了一个新的定量指标,称为销毁率(DT)。实验结果显示DB的10-20率和DDD码中的10-20率的改进。