Multi-scale Target-Aware Framework for Constrained Image Splicing Detection and Localization

TL;DR

This paper introduces a unified multi-scale target-aware framework for detecting and localizing spliced regions in images, improving feature matching and robustness against scale changes in multimedia forensics.

Contribution

It proposes a novel target-aware attention mechanism coupled with multi-scale projection to enhance feature learning and correlation matching in a single pipeline.

Findings

Outperforms state-of-the-art methods on benchmark datasets.

Demonstrates robustness against scale transformations.

Effectively promotes collaborative learning of related patches.

Abstract

Constrained image splicing detection and localization (CISDL) is a fundamental task of multimedia forensics, which detects splicing operation between two suspected images and localizes the spliced region on both images. Recent works regard it as a deep matching problem and have made significant progress. However, existing frameworks typically perform feature extraction and correlation matching as separate processes, which may hinder the model's ability to learn discriminative features for matching and can be susceptible to interference from ambiguous background pixels. In this work, we propose a multi-scale target-aware framework to couple feature extraction and correlation matching in a unified pipeline. In contrast to previous methods, we design a target-aware attention mechanism that jointly learns features and performs correlation matching between the probe and donor images. Our approach can effectively promote the collaborative learning of related patches, and perform mutual promotion of feature learning and correlation matching. Additionally, in order to handle scale transformations, we introduce a multi-scale projection method, which can be readily integrated into our target-aware framework that enables the attention process to be conducted between tokens containing information of varying scales. Our experiments demonstrate that our model, which uses a unified pipeline, outperforms state-of-the-art methods on several benchmark datasets and is robust against scale transformations.