Edge-aware Affinity Enhancement for Image Manipulation Localization

Image manipulation localization (IML) refers to the task of identifying regions in images that have been altered by specific tampering techniques, such as copy-move, splicing, or inpainting. Transformers have been applied to IML tasks due to their ability to model long-range correlations between pixels through the self-attention mechanism. However, the inherent self-attention mechanism may not accurately model or sufficiently enhance these correlations, particularly in detailed edge traces, due to its limitations. In this paper, we propose an Edge-Aware Affinity Enhancement approach for the IML task. Specifically, we introduce an Affinity Regularization Module to establish inter-patch correlations for feature regularization via random walk propagation. Based on the extracted correlation representation, we propose an Edge-Affinity Guidance strategy to further refine the correlation accuracy, particularly in ambiguous edge regions. Extensive experimental results demonstrate that our method outperforms state-of-the-art image manipulation localization techniques in terms of localization accuracy.