In this paper, we introduce a graph representation learning architecture for spatial image steganalysis, which is motivated by the assumption that steganographic modifications unavoidably distort the statistical characteristics of the hidden graph features derived from cover images. In the detailed architecture, we translate each image to a graph, where nodes represent the patches of the image and edges indicate the local relationships between the patches. Each node is associated with a feature vector determined from the corresponding patch by a shallow convolutional neural network (CNN) structure. By feeding the graph to an attention network, the discriminative features can be learned for efficient steganalysis. Experiments indicate that the reported architecture achieves a competitive performance compared to the benchmark CNN model, which has shown the potential of graph learning for steganalysis.

翻译：在本文中,我们引入了一个用于空间图像分层分析的图形代表学习结构,其动机是假设分层修改不可避免地扭曲封面图像产生的隐藏图形特征的统计特征。在详细结构中,我们将每个图像转换为图表,其中每个图像的节点代表图像的补丁和边缘代表了相片之间的局部关系。每个节点都与由浅层神经网络结构(CNN)从相应的补丁中确定的一个特征矢量相关联。通过将图输入一个关注网络,可以了解有区别的特征,以便进行高效的分层分析。实验表明,所报告的结构与基准CNN模型相比,具有竞争性的性能,该基准CNN模型展示了图学为进行分层分析而学习的潜力。