Image Manipulation Detection With Cascade Hierarchical Graph Representation

Wenyan Pan, Wentao Ma, Shan Zhao, Lichuan Gu, Guolong Shi, Zhihua Xia, Meng Wang

Abstract: Recent image manipulation detection approaches primarily rely on sophisticated Convolutional Neural Network (CNN)-based models for region localization, while they tend to ignore: 1) the feature correlations that exist between manipulated and non-manipulated regions; 2) significance of multi-scale representations in detecting manipulated regions of varying sizes, consequently hampering the overall performance of image manipulation detection. To address these limitations, we propose a novel approach, called Cascade Hierarchical Graph Convolutional Network (Cas-HGCN), which comprehensively learns the feature correlations between manipulated and non-manipulated regions at different scales using the Feature Correlations Modeling (FCM) module. Specifically, the FCM module treats the grids in the hierarchical image/feature maps as nodes, constructs a fully-connected graph by connecting each node, and leverages it to learn and refine feature correlations across different scales in a cascading manner. This process results in high discriminability for distinguishing manipulated and non-manipulated regions. Extensive experiments conducted on three public datasets, namely CASIA, NIST, and Coverage, demonstrate the promising detection accuracy achieved by Cas-HGCN without the need for pre-training on large datasets, surpassing the performance of existing state-of-the-art competitors.