ForgerySpotter: Pinpointing Tampered Regions with Multi-scale Evidence and Confidence-Guided Refinement
TL;DR
This paper proposes a unified framework that incorporates multi-dimensional feature extraction, multi-scale feature fusion, and a confidence-guided refine mechanism that achieves state-of-the-art performance and shows strong generalization, validating its effectiveness and practicality.
Abstract
Image manipulation detection (IMD) is crucial for maintaining the integrity of digital media, as forged images can be used to spread false information and erode public trust. IMD faces two persistent challenges: i) limited generalization to diverse real-world post-processing operations, and ii) imprecise localization accuracy yielding coarse or incomplete tampering regions. Moreover, existing methods often lack interpretability due to the absence of reliable confidence estimation. The primary research often prioritizes feature or architectural improvements while neglecting the integration of detection reliability with localization refinement. In this paper, we propose a unified framework that incorporates multi-dimensional feature extraction, multi-scale feature fusion, and a confidence-guided refine mechanism. Our method captures tampering traces across types and scales adaptively, while the confidence-guided mechanism refines localization maps and estimates pixel-wise reliability. Extensive experiments on multiple datasets demonstrate that the proposed approach achieves state-of-the-art performance and shows strong generalization, validating its effectiveness and practicality.