Up to now, most existing steganalytic methods are designed for grayscale images, and they are not suitable for color images that are widely used in current social networks. In this paper, we design a universal color image steganalysis network (called UCNet) in spatial and JPEG domains. The proposed method includes preprocessing, convolutional, and classification modules. To preserve the steganographic artifacts in each color channel, in preprocessing module, we firstly separate the input image into three channels according to the corresponding embedding spaces (i.e. RGB for spatial steganography and YCbCr for JPEG steganography), and then extract the image residuals with 62 fixed high-pass filters, finally concatenate all truncated residuals for subsequent analysis rather than adding them together with normal convolution like existing CNN-based steganalyzers. To accelerate the network convergence and effectively reduce the number of parameters, in convolutional module, we carefully design three types of layers with different shortcut connections and group convolution structures to further learn high-level steganalytic features. In classification module, we employ a global average pooling and fully connected layer for classification. We conduct extensive experiments on ALASKA II to demonstrate that the proposed method can achieve state-of-the-art results compared with the modern CNN-based steganalyzers (e.g., SRNet and J-YeNet) in both spatial and JPEG domains, while keeping relatively few memory requirements and training time. Furthermore, we also provide necessary descriptions and many ablation experiments to verify the rationality of the network design.
翻译:到现在为止,大多数现有分析方法都是为灰色图像设计的,它们不适合于当前社交网络中广泛使用的彩色图像。 在本文中,我们设计了一个空间和JPEG域通用的彩色图像分析网络(称为UCNet),在空间和JPEG域中,我们设计了一个通用的彩色图像分析网络(称为UCNet),建议的方法包括预处理、变动和分类模块。为了在预处理模块中保存每个彩色频道中的彩色文物,我们首先根据相应的嵌入空间空间(即空间血清RGB和JPEG系统系统图案的YCbCr),将输入图像图像图像图像图像图像图像图像的残存与62个固定的高通度过滤过滤器(称为UCNEG),最后将所有变异性图像的残存都用于随后的分析,而不是将其与正常的变异性(如现有的CNNEMS)分析器。为了加速网络趋同和有效减少基于参数的数量,我们仔细设计了三种类型的层结构,我们用不同的捷径连接和组合结构结构来进一步学习高层次的JBAIS标准的图像数据,同时,我们还可以化了一个模块,我们也可以化的网络和系统设计方法可以完全地进行。