Amplifying Discrepancies: Exploiting Macro and Micro Inconsistencies for Image Manipulation Localization

The rapid development of image manipulation technologies poses significant challenges to multimedia forensics, especially in accurate localization of manipulated regions. Existing methods often fail to fully explore the intrinsic discrepancies between manipulated and authentic regions, resulting in sub-optimal performance. To address this limitation, we propose the Focus Region Discrepancy Network (FRD-Net), a novel and efficient framework that significantly enhances manipulation localization by amplifying discrepancies at both macro- and micro-levels. Specifically, our proposed Iterative Clustering Module (ICM) groups features into two discriminative clusters and refines representations via backward propagation from cluster centers, improving the distinction between tampered and authentic regions at the macro level. Thereafter, our Differential Progressive Module (DPM) is constructed to capture fine-grained structural inconsistencies within local neighborhoods and integrate them into a Central Difference Convolution, increasing sensitivity to subtle manipulation details at the micro level. Finally, these complementary modules are seamlessly integrated into a compact architecture that achieves a favorable balance between accuracy and efficiency. Extensive experiments on multiple benchmarks demonstrate that FRD-Net consistently surpasses state-of-the-art methods in terms of manipulation localization performance while maintaining a lower computational cost.