  The objective of image manipulation detection is to identify and locate the
manipulated regions in the images. Recent approaches mostly adopt the
sophisticated Convolutional Neural Networks (CNNs) to capture the tampering
artifacts left in the images to locate the manipulated regions. However, these
approaches ignore the feature correlations, i.e., feature inconsistencies,
between manipulated regions and non-manipulated regions, leading to inferior
detection performance. To address this issue, we propose a hierarchical Graph
Convolutional Network (HGCN-Net), which consists of two parallel branches: the
backbone network branch and the hierarchical graph representation learning
(HGRL) branch for image manipulation detection. Specifically, the feature maps
of a given image are extracted by the backbone network branch, and then the
feature correlations within the feature maps are modeled as a set of
fully-connected graphs for learning the hierarchical graph representation by
the HGRL branch. The learned hierarchical graph representation can sufficiently
capture the feature correlations across different scales, and thus it provides
high discriminability for distinguishing manipulated and non-manipulated
regions. Extensive experiments on four public datasets demonstrate that the
proposed HGCN-Net not only provides promising detection accuracy, but also
achieves strong robustness under a variety of common image attacks in the task
of image manipulation detection, compared to the state-of-the-arts.
