Graph Representation Learning for Spatial Image Steganalysis
Qiyun Liu, Limengnan Zhou, Hanzhou Wu · 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP) · 2022
In this article, we introduce a novel graph representation learning architecture for spatial image steganalysis, which was motivated by the reasonable assumption that steganographic modifications will inevitably distort the statistical characteristics of the latent graph features determined from the cover images. In the architecture, we translate each image to a graph, in which the nodes represent the patches of the image and the edges between nodes indicate the local relationships between the corresponding patches. Each graph node is then associated with a feature vector determined from the corresponding patch by a shallow convolutional neural network (CNN). By feeding the graph containing node features to an attention network, the discriminative features can be learned for efficient spatial steganalysis. The experiments indicate that the reported architecture in this paper achieves a competitive detection performance compared to the benchmark CNN, which demonstrates the potential of graph neural network for steganalysis and may inspire us to develop advanced works.