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

Hengrun Zhao¹, Yifan Wang¹, Yunzhi Zhuge¹

¹Dalian University of Technology, No.2 Linggong Road, Dalian, 116024, Liaoning, China.

Neural Networks : the Official Journal of the International Neural Network Society

|June 29, 2026

Summary

This summary is machine-generated.

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This study introduces a novel Fine-Grained Graph Convolution Network (IFL-GCN) to detect sophisticated image forgeries created by deep inpainting. The new method significantly improves the accuracy of identifying these challenging digital manipulations.

Area of Science:

Computer Vision
Digital Forensics
Machine Learning

Background:

Deep image inpainting creates realistic forgeries, challenging traditional detection methods due to local coherence and semantic consistency.
Existing detectors struggle with seamless forgeries, necessitating advanced techniques for accurate localization.

Purpose of the Study:

To propose a novel Fine-Grained Graph Convolution Network (IFL-GCN) for effective inpainting forgery localization.
To enhance the sensitivity and robustness of forgery detection against high-fidelity and diverse inpainting artifacts.

Main Methods:

Introduced a pixel-wise graph construction for direct integration of local forgery traces across the entire image.
Developed a Fidelity-aware Weighted Loss (FW loss) to calibrate learning objectives based on forged content fidelity.

Keywords:

Forgery localization Graph convolution Image forgery detection Inpainting detection

Related Experiment Videos

Proposed Forgery Intensity Mixup (FIM) augmentation to improve generalization across diverse inpainting artifacts.

Main Results:

IFL-GCN achieves state-of-the-art performance on 6 mainstream forgery detection benchmarks.
Outperformed the closest competing method by 6.7% in average F1 score across all inpainting forgery test sets.
Demonstrated enhanced sensitivity to subtle, high-fidelity forgeries and improved robustness.

Conclusions:

The proposed IFL-GCN effectively addresses the challenges posed by deep inpainting forgeries.
The pixel-wise graph, FW loss, and FIM augmentation contribute to superior inpainting forgery localization.
IFL-GCN represents a significant advancement in digital forensics for detecting manipulated images.