Multi-Scale Cross-Fusion and Edge-Supervision Network for Image Splicing Localization

TL;DR

This paper introduces a novel multi-scale cross-fusion and edge-supervision network that significantly improves image splicing localization by effectively exploiting edge information and boundary artifacts, outperforming existing methods.

Contribution

The paper proposes a new multi-scale cross-fusion framework with edge supervision for more accurate image splicing localization, addressing the insufficient edge information in prior approaches.

Findings

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

Effectively mines boundary artifacts for better localization

Enhances feature representation through multi-scale fusion

Abstract

Image Splicing Localization (ISL) is a fundamental yet challenging task in digital forensics. Although current approaches have achieved promising performance, the edge information is insufficiently exploited, resulting in poor integrality and high false alarms. To tackle this problem, we propose a multi-scale cross-fusion and edge-supervision network for ISL. Specifically, our framework consists of three key steps: multi-scale features cross-fusion, edge mask prediction and edge-supervision localization. Firstly, we input the RGB image and its noise image into a segmentation network to learn multi-scale features, which are then aggregated via a cross-scale fusion followed by a cross-domain fusion to enhance feature representation. Secondly, we design an edge mask prediction module to effectively mine the reliable boundary artifacts. Finally, the cross-fused features and the reliable edge mask information are seamlessly integrated via an attention mechanism to incrementally supervise and facilitate model training. Extensive experiments on publicly available datasets demonstrate that our proposed method is superior to state-of-the-art schemes.