F2Mamba: Fine-grained forgery-aware Mamba for image manipulation localization.
TL;DR
A Fine-grained Forgery-aware Mamba is proposed that learns multi-scale global features at low computational cost with the linear-complexity global modeling capability of VMamba and achieves strong generalization capability compared with state-of-the-art IML models.
Abstract
Image Manipulation Localization (IML) aims to precisely identify forged regions within an image at the pixel level, which is crucial for preventing the spread of misinformation that may pose potential threats to public safety. Recent Transformer-based methods can model long-range dependencies to capture global forgery features, however, they usually require heavy computation and overlook local fine-grained details that are essential for forensic analysis. To address these limitations, we propose a Fine-grained Forgery-aware Mamba (F2Mamba) that can efficiently and accurately localize potential manipulations. Specifically, F2Mamba learns multi-scale global features at low computational cost with the linear-complexity global modeling capability of VMamba. The Fine-grained Forgery-aware Adapter (FFA) is further introduced to adaptively fuse local critical details with global representations during decoding, which facilitates the recovery of fine-grained information lost in the earlier feature learning stage. Additionally, a Forgery-Guided Refinement Decoder (FRD) is designed at the final decoding stage to calibrate boundary errors by performing iterative Conditional Random Field (CRF) refinement, effectively suppressing checkerboard artifacts and improving localization precision. Extensive experiments on diverse datasets show that F2Mamba achieves an average F1 score of 69.92% and an average IoU score of 61.26%, demonstrating strong generalization capability compared with state-of-the-art IML models. Code is available at: https://github.com/ii-zy/F2Mamba.