Image Inpainting Detection via Dual Guidance of Uncertainty and Precise Boundary Information

Deep-learning-based image inpainting technology has achieved remarkable visual consistency but is vulnerable to malicious use. Existing detection methods overlook semantic inconsistencies between targets and backgrounds, leading to ambiguous results due to low discriminability. To tackle these challenges, we draw inspiration from human strategies in visual tasks, which involve initially assigning uncertainty across the entire input and subsequently concentrating on highly uncertain regions using prior knowledge like boundary information. Building on this, we propose a Dual Information Guided Network (DIGNet). It combines object-background semantic modulation with uncertainty to precisely locate inpainting regions. This is the first work to address inpainting prediction inaccuracies by considering both edge uncertainty and semantic inconsistency. DIGNet consists of three key parts: the Edge Uncertainty Awareness Module (EUAM), the Edge Correction Module (ECM) based on semantic differences, and the Dual Information Guided Interaction Module (DIGIM). We use semantic inconsistency to get edge constraints and quantify uncertainty as feature variance to guide mainstream feature maps. The DIGIM effectively fuses guide information for accurate predictions. Comprehensive experiments show that our method outperforms existing CNN-based approaches. Specifically, it improves the F1 Score by at least 0.31% and the IOU by at least 0.19% on multiple datasets.