Aggregating global-scale pixel-wise forgery cues within a graph.

Abstract

Deep image inpainting techniques produce visually seamless forgeries that pose unique challenges to conventional forgery detectors due to their local coherence and semantic consistency. To address this, we propose a Fine-Grained Graph Convolution Network (IFL-GCN) for Inpainting Forgery Localization. Departing from prior local-global forgery detectors, IFL-GCN introduces a pixel-wise graph construction that achieves direct integration of local forgery traces across the entire image. By modeling each pixel as a graph node, our approach captures long-range inconsistencies while preserving fine-grained detail perception, enabling comprehensive analysis of irregular artifact distributions at the global scale. Additionally, we develop a Fidelity-aware Weighted Loss (FW loss) to dynamically calibrate learning objectives based on the estimated fidelity of the forged content, enhancing the detector's sensitivity to subtle, high-fidelity forgeries. Furthermore, to bridge the generalization gap across diverse inpainting artifacts and improve robustness, we propose Forgery Intensity Mixup augmentation (FIM), which expands the distribution of forgery features by modulating the intensity of forgeries while maintaining spatial-semantic integrity. Extensive experiments across 6 mainstream forgery detection benchmarks demonstrate that IFL-GCN achieves state-of-the-art performance, outperforming the closest competing method by 6.7% in average F1 score over all inpainting forgery test sets.