PIM-Net: Progressive Inconsistency Mining Network for image manipulation localization

Abstract

The content authenticity and reliability of digital images have promoted the research on image manipulation localization (IML). Most current deep learning-based methods focus on extracting global or local tampering features for identifying forged regions. These features usually contain semantic information and lead to inaccurate detection results for non-object or incomplete semantic tampered regions. In this study, we propose a novel Progressive Inconsistency Mining Network (PIM-Net) for effective IML. Specifically, PIM-Net consists of two core modules, the Inconsistency Mining Module (ICMM) and the Progressive Fusion Refinement module (PFR). ICMM models the inconsistency between authentic and forged regions at two levels, i.e., pixel correlation inconsistency and region attribute incongruity, while avoiding the interference of semantic information. Then PFR progressively aggregates and refines extracted inconsistent features, which in turn yields finer and pure localization responses. Extensive qualitative and quantitative experiments on five benchmarks demonstrate PIM-Net’s superiority to current state-of-the-art IML methods. Code: https://github.com/ningnbai/PIM-Net.