Prototype Memory-Based Neighboring Feature Fusion Network for Image Manipulation Localization

Image manipulation localization (IML) aims to segment manipulated regions in suspicious images. However, most existing methods rely solely on intrinsic features extracted from the input image and passively model local or global inconsistencies, making it difficult to accurately delineate manipulated regions with ambiguous boundaries. To address these challenges, we propose a prototype memory-based neighboring feature fusion network (PNF-Net), which is inspired by a biological memory mechanism. PNF-Net simulates selective preference by learning manipulation-trace prototypes as memory priors, thereby guiding representation learning toward consistent and discriminative manipulation cues. Specifically, we propose a memory-guided localization module (MLM) that models the consistencies and anomalies between manipulated regions and the background as memory priors, enabling precise localization. We then propose a neighboring feature interaction module (NFIM) that preserves fine-grained details from neighboring shallow features, enhances global semantics from neighboring deep features, and effectively fuses them. Finally, a verification fusion module (VFM) is designed to enrich contextual semantics and improve the completeness and accuracy of localization results. Extensive experiments on multiple benchmark datasets show that our PNF-Net outperforms most state-of-the-art IML models. Our code is available on https://github.com/vpsg-research/PNF-Net.