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.