HRGR: Enhancing Image Manipulation Detection via Hierarchical Region-aware Graph Reasoning

TL;DR

HRGR introduces a hierarchical, region-aware graph reasoning approach that models image correlations based on content-coherent regions with irregular shapes, significantly improving image manipulation detection accuracy.

Contribution

The paper proposes a novel Differentiable Feature Partition strategy and hierarchical graph reasoning to better capture content coherence, advancing manipulation detection methods.

Findings

Outperforms existing methods in manipulation detection accuracy

Effective integration as a plug-and-play component

Fully differentiable and end-to-end trainable

Abstract

Image manipulation detection is to identify the authenticity of each pixel in images. One typical approach to uncover manipulation traces is to model image correlations. The previous methods commonly adopt the grids, which are fixed-size squares, as graph nodes to model correlations. However, these grids, being independent of image content, struggle to retain local content coherence, resulting in imprecise detection.To address this issue, we describe a new method named Hierarchical Region-aware Graph Reasoning (HRGR) to enhance image manipulation detection. Unlike existing grid-based methods, we model image correlations based on content-coherence feature regions with irregular shapes, generated by a novel Differentiable Feature Partition strategy. Then we construct a Hierarchical Region-aware Graph based on these regions within and across different feature layers. Subsequently, we describe a structural-agnostic graph reasoning strategy tailored for our graph to enhance the representation of nodes. Our method is fully differentiable and can seamlessly integrate into mainstream networks in an end-to-end manner, without requiring additional supervision. Extensive experiments demonstrate the effectiveness of our method in image manipulation detection, exhibiting its great potential as a plug-and-play component for existing architectures. Codes and models are available at https://github.com/OUC-VAS/HRGR-IMD.