Enhancing Copy-Move Forgery Detection via Attention-Based Similarity Modeling

In copy-move forgery detection (CMFD), distinguishing between source (copied) and target (pasted) regions is essential for understanding the nature and intent of the manipulation. A key component in existing CMFD methods is the similarity modeling branch, which typically relies on self-correlation to identify duplicated regions. However, self-correlation is often limited in detecting small, subtle manipulations due to its rigid spatial comparison. To address this, we introduce a self-attention mechanism in place of self-correlation, enabling the model to capture asymmetric and context-aware dependencies across feature maps. In this paper, we integrate this modification into the BusterNet architecture, which is the first framework to explicitly model source-target disambiguation through its dual-branch design. Experimental results demonstrate that our proposed attention-enhanced model achieves higher recall and F1-scores in pixel-level evaluations, particularly for fine-grained forgeries, while maintaining comparable computational cost. Importantly, this modification is general and can be applied to other CMFD architectures as well.