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Graph Representation Learning for Spatial Image Steganalysis
[article]
2022
arXiv
pre-print
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
arXiv:2110.00957v3
fatcat:7tec4xvcrfapvnqcumlja55ryi