  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.
