EMF-Net: An edge-guided multi-feature fusion network for text manipulation detection

Abstract

Text images have a more uniform background than natural images, making it difficult to capture tampering traces. Thus, it poses significant challenges to the detection and localization of tampered text images. To effectively detect tampered text images, we propose the edge-guided multi-feature fusion network (EMF-Net) including four different modules, the difference semantic discriminator (DSD), the edge-guidance feature aggregation module (EFAM), the edge supervised module (ESM), and the multi-branch attention-induced fusion module (MAFM). The proposed network combines RGB images and learned noise-sensitive features to deeply excavate the hidden tampered region features to improve the detection accuracy due to the insufficient feature information. Meanwhile, the multi-branch attention-induced fusion and semantic discriminator methods are integrated to reduce false alarms by shielding the feature interference in non-tampered regions and diminish the external interference. Furthermore, we craft a dataset of 12,000 text images and their tempered versions with three tempering operations including copy-move, splicing and inpainting. Extensive experiments have shown that the proposed network can improve the generalization performance and achieve the higher detection accuracy compared to current other state-of-the-art methods.