Image Forgery Localization via Guided Noise and Multi-Scale Feature Aggregation

TL;DR

This paper introduces a novel guided multi-scale feature aggregation network for image forgery localization, effectively improving detection accuracy, especially for small forged regions, and robustness against post-processing.

Contribution

It proposes a guided noise extraction module, a dynamic convolution-based feature aggregation module, and an atrous residual pyramid module to enhance forgery detection performance.

Findings

Outperforms state-of-the-art methods on five public datasets.

Achieves superior detection of small forged regions.

Demonstrates robustness against post-processing effects.

Abstract

Image Forgery Localization (IFL) technology aims to detect and locate the forged areas in an image, which is very important in the field of digital forensics. However, existing IFL methods suffer from feature degradation during training using multi-layer convolutions or the self-attention mechanism, and perform poorly in detecting small forged regions and in robustness against post-processing. To tackle these, we propose a guided and multi-scale feature aggregated network for IFL. Spectifically, in order to comprehensively learn the noise feature under different types of forgery, we develop an effective noise extraction module in a guided way. Then, we design a Feature Aggregation Module (FAM) that uses dynamic convolution to adaptively aggregate RGB and noise features over multiple scales. Moreover, we propose an Atrous Residual Pyramid Module (ARPM) to enhance features representation and capture both global and local features using different receptive fields to improve the accuracy and robustness of forgery localization. Expensive experiments on 5 public datasets have shown that our proposed model outperforms several the state-of-the-art methods, specially on small region forged image.