DCL-Net: Decoupled Contrastive Learning Network for Image Manipulation Localization

Extracting discriminative forensic artifacts from high-dimensional latent spaces is pivotal for Image Manipulation Localization (IML). Nevertheless, the intrinsic feature entanglement arising from subtle structural discrepancies continues to impede precise localization. Specifically, manipulation artifacts are frequently overwhelmed by coherent background textures or ’soft boundaries’, rendering the trace-rich features inextricably mixed with intrinsic image content. To disentangle these intertwined representations, we propose the Decoupled Contrastive Learning Network (DCL-Net) for robust image tampering localization. Leveraging a Vision Transformer (ViT) backbone integrated with a Simple Feature Pyramid Network (SFPN), DCL-Net incorporates a novel Feature Decoupling Module (FDM). The FDM explicitly disentangles the feature space into foreground, background, and uncertainty regions-thereby effectively isolating manipulation cues from coherent background textures and capturing the transitional nature of ambiguous boundaries. Furthermore, to align the optimization objective with the intrinsic structure of manipulation traces, we introduce a prior-guided contrastive learning strategy that explicitly ’pushes away’ manipulated features from authentic and uncertain components. Finally, the Contrast Feature Aggregation Module (CFAM) employs a two-stage attention mechanism to refine these disentangled features, further suppressing redundant background details. Extensive experiments across five public benchmarks demonstrate that DCL-Net delivers state-of-the-art localization performance and exhibits strong robustness against common distortions.