TBFormer: Two-Branch Transformer for Image Forgery Localization
This work addresses the problem of detecting forged regions in images for applications like digital forensics, but it is incremental as it builds on existing Transformer methods with domain-specific adaptations.
The paper tackles image forgery localization by proposing TBFormer, a two-branch Transformer network that extracts features from RGB and noise domains, achieving effective results as demonstrated on public datasets.
Image forgery localization aims to identify forged regions by capturing subtle traces from high-quality discriminative features. In this paper, we propose a Transformer-style network with two feature extraction branches for image forgery localization, and it is named as Two-Branch Transformer (TBFormer). Firstly, two feature extraction branches are elaborately designed, taking advantage of the discriminative stacked Transformer layers, for both RGB and noise domain features. Secondly, an Attention-aware Hierarchical-feature Fusion Module (AHFM) is proposed to effectively fuse hierarchical features from two different domains. Although the two feature extraction branches have the same architecture, their features have significant differences since they are extracted from different domains. We adopt position attention to embed them into a unified feature domain for hierarchical feature investigation. Finally, a Transformer decoder is constructed for feature reconstruction to generate the predicted mask. Extensive experiments on publicly available datasets demonstrate the effectiveness of the proposed model.